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Chen Z, Zhu Y, Wang L, Cong R, Feng B, Cai W, Liang M, Li D, Wang S, Hu M, Mi Y, Wang S, Ma X, Zhao X. Virtual MR Elastography and Multi-b-value DWI Models for Predicting Microvascular Invasion in Solitary BCLC Stage A Hepatocellular Carcinoma. Acad Radiol 2025; 32:2569-2584. [PMID: 39643466 DOI: 10.1016/j.acra.2024.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Revised: 11/07/2024] [Accepted: 11/11/2024] [Indexed: 12/09/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the performance of virtual MR elastography (vMRE) for predicting microvascular invasion (MVI) in Barcelona Clinic Liver Cancer (BCLC) stage A (≤ 5.0 cm) hepatocellular carcinoma (HCC) and to construct a combined nomogram based on vMRE, multi-b-value DWI models, and clinical-radiological (CR) features. METHODS Consecutive patients with suspected HCC who underwent multi-b-value DWI examinations were prospectively collected. Quantitative parameters from vMRE, mono-exponential, intravoxel incoherent motion, and diffusion kurtosis imaging models were obtained. Multivariate logistic regression was used to identify independent MVI predictors and build prediction models. A combined MRI_Score was constructed using independent quantitative parameters. A visualized nomogram was built based on significant CR features and MRI_Score. The predictive performance of quantitative parameters and models was evaluated. RESULTS The study included 103 patients (median age: 56 years; range: 35-70 years; 87 males and 16 females). Diffusion-based shear modulus (μDiff) exhibited a predictive performance for MVI with area under the curve (AUC) of 0.735. The MRI_Score was developed employing true diffusion coefficient (D), mean kurtosis (MK), and μDiff. CR model and MRI_Score achieved AUCs of 0.787 and 0.840, respectively. The combined nomogram based on AFP, corona enhancement, tumor capsule, TTPVI, and MRI_Score significantly improved the predictive performance to an AUC of 0.931 (Delong test p < 0.05). CONCLUSION vMRE exhibited great potential for predicting MVI in BCLC stage A HCC. The combined nomogram integrating CR features, vMRE, and quantitative diffusion parameters significantly improved the predictive accuracy and could potentially assist clinicians in identifying appropriate treatment options.
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Affiliation(s)
- Zhaowei Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Yongjian Zhu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Leyao Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Rong Cong
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Bing Feng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Wei Cai
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Meng Liang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Dengfeng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Shuang Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Mancang Hu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Yongtao Mi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing 100176, China (S.W.).
| | - Xiaohong Ma
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
| | - Xinming Zhao
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China (Z.C., Y.Z., L.W., R.C., B.F., W.C., M.L., D.L., S.W., M.H., Y.M., X.M., X.Z.).
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Lu JP, Feng JK, Zhao Y, Chen B, Li PP, He C, Gong L, Bao LL. Grading risk of microvascular invasion impacts survival in hepatocellular carcinoma patients undergoing adjuvant transarterial chemoembolization: A multicenter study. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2025; 51:110102. [PMID: 40300381 DOI: 10.1016/j.ejso.2025.110102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2025] [Revised: 04/10/2025] [Accepted: 04/24/2025] [Indexed: 05/01/2025]
Abstract
PURPOSE To investigate the influence of postoperative adjuvant transarterial chemoembolization (PA-TACE) on the prognosis of hepatocellular carcinoma (HCC) patients with microvascular invasion (MVI) following liver resection (LR), and explore whether grading risk of MVI can impact the survival of HCC patients undergoing PA-TACE. METHODS Patients who had HCC with MVI were consecutively enrolled. Overall survival (OS) and recurrence-free survival (RFS) were compared between the PA-TACE and LR groups. Univariate and multivariate analyses were performed to identify independent prognostic factors for these patients. Subgroup survival analysis was conducted using the grading risk of MVI. RESULTS The median OS and RFS of the PA-TACE group were significantly longer than the LR group. PA-TACE was associated with significantly better OS (P = 0.032) and RFS (P = 0.023) compared with LR alone. In subgroup analysis, there were no significant differences in prognosis between the PA-TACE and LR groups for HCC patients with low-risk MVI. For HCC patients with high-risk MVI, the PA-TACE group had significantly better prognosis than the LR group (for OS, P = 0.017; for RFS, P = 0.018). CONCLUSION PA-TACE should be performed selectively in HCC patients with high-risk MVI after curative liver resection. Nonetheless, for HCC patients with low-risk MVI, PA-TACE is not recommended.
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Affiliation(s)
- Jin-Pian Lu
- Department of General Surgery, Dongyang Hospital of Traditional Chinese Medicine, Jinhua, 322100, Zhejiang Province, China
| | - Jin-Kai Feng
- Department of Hepatobiliary Surgery, No.971 Hospital of the Chinese People's Liberation Army (PLA) Navy, Qingdao, 266071, Shandong Province, China
| | - Yang Zhao
- Medical Service Training Center, No.971 Hospital of the Chinese People's Liberation Army (PLA) Navy, Qingdao, 266071, Shandong Province, China
| | - Bin Chen
- Department of General Surgery, Dongyang Hospital of Traditional Chinese Medicine, Jinhua, 322100, Zhejiang Province, China
| | - Peng-Ping Li
- Department of General Surgery, The First People's Hospital of Xiaoshan District, Hangzhou, 311200, Zhejiang Province, China
| | - Chao He
- Department of General Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310006, Zhejiang Province, China
| | - Lin Gong
- Department of Hepatobiliary Surgery, No.971 Hospital of the Chinese People's Liberation Army (PLA) Navy, Qingdao, 266071, Shandong Province, China.
| | - Ling-Ling Bao
- Department of General Surgery, Dongyang Hospital of Traditional Chinese Medicine, Jinhua, 322100, Zhejiang Province, China.
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3
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You GR, Park SY, Cho SH, Cho SB, Koh YS, Lee CH, Jo HG, Choi SK, Yoon JH. Extrahepatic Recurrence After Surgical Resection of Hepatocellular Carcinoma Without Intrahepatic Recurrence: A Multi-Institutional Observational Study. Cancers (Basel) 2025; 17:1417. [PMID: 40361344 PMCID: PMC12070905 DOI: 10.3390/cancers17091417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2025] [Revised: 04/22/2025] [Accepted: 04/23/2025] [Indexed: 05/15/2025] Open
Abstract
BACKGROUND/OBJECTIVES Extrahepatic recurrence (EHR) is a significant negative prognostic factor in hepatocellular carcinoma (HCC). Although EHR is commonly observed in high-risk patients following HCC hepatectomy, its occurrence without concurrent intrahepatic HCC remains poorly understood. Therefore, this study aims to examine the clinical characteristics and risk factors associated with EHR in patients without intrahepatic HCC at diagnosis. METHODS This study included 1066 treatment-naïve patients who underwent curative hepatectomy for HCC at four tertiary academic centers between January 2004 and December 2019. After excluding those with intrahepatic recurrence (IHR), concurrent EHR, or incomplete clinical records, 569 patients were included in the final analysis. Risk factors for EHR were assessed using multivariate Cox regression over a median follow-up period of 3.91 years. RESULTS Among the cohort, 38 patients developed EHR post-surgery without residual intrahepatic HCC, with a median follow-up of 1.04 years. These patients experienced earlier initial HCC recurrence than those without EHR (1.73 vs. 4.43 years). Multivariate analysis revealed significant associations between EHR and microvascular invasion (hazard ratio [HR]: 2.418, p = 0.020), tumor necrosis (HR: 2.592, p = 0.009), and initial tumor staging beyond the Milan criteria (HR: 3.008, p = 0.001). Moreover, Cox regression analysis revealed that EHR strongly correlated with decreased post-hepatectomy survival (HR: 14.044, p < 0.001). Cumulative EHR and survival rates were closely linked to the number of risk factors present. CONCLUSIONS EHR without detectable IHR is significant and warrants close monitoring in high-risk patients.
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Affiliation(s)
- Ga Ram You
- Department of Gastroenterology and Hepatology, Chonnam National University Hwasun Hospital and Medical School, Hwasun 58128, Republic of Korea; (G.R.Y.); (S.B.C.)
| | - Shin Young Park
- Department of Gastroenterology and Hepatology, Chonnam National University Hospital and Medical School, Gwangju 61469, Republic of Korea; (S.Y.P.); (S.H.C.); (S.K.C.)
| | - Su Hyeon Cho
- Department of Gastroenterology and Hepatology, Chonnam National University Hospital and Medical School, Gwangju 61469, Republic of Korea; (S.Y.P.); (S.H.C.); (S.K.C.)
| | - Sung Bum Cho
- Department of Gastroenterology and Hepatology, Chonnam National University Hwasun Hospital and Medical School, Hwasun 58128, Republic of Korea; (G.R.Y.); (S.B.C.)
| | - Yang Seok Koh
- Department of Surgery, Chonnam National University Hwasun Hospital and Medical School, Hwasun 58128, Republic of Korea;
| | - Chang Hun Lee
- Department of Gastroenterology and Hepatology, Jeonbuk National University Hospital and Medical School, Jeonju 54907, Republic of Korea;
| | - Hoon Gil Jo
- Department of Gastroenterology and Hepatology, Wonkwang University Hospital and Medical School, Iksan 54538, Republic of Korea;
| | - Sung Kyu Choi
- Department of Gastroenterology and Hepatology, Chonnam National University Hospital and Medical School, Gwangju 61469, Republic of Korea; (S.Y.P.); (S.H.C.); (S.K.C.)
| | - Jae Hyun Yoon
- Department of Gastroenterology and Hepatology, Chonnam National University Hospital and Medical School, Gwangju 61469, Republic of Korea; (S.Y.P.); (S.H.C.); (S.K.C.)
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4
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Yang Z, Zhang Y, Zheng J, Tao L, Song C, Gong L, Jin R, Liang X. Minimally invasive versus open liver resection for hepatocellular carcinoma with microvascular invasion: a propensity score-matching study. Surg Endosc 2025:10.1007/s00464-025-11717-1. [PMID: 40251314 DOI: 10.1007/s00464-025-11717-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2024] [Accepted: 04/06/2025] [Indexed: 04/20/2025]
Abstract
BACKGROUND Microvascular invasion (MVI) is one of the major risk factors for postoperative recurrence of HCC. For HCC patients with MVI, few studies have examined the differences in prognosis between minimally invasive and open liver resection. MATERIALS AND METHODS A total of 171 HCC patients with MVI who underwent curative-intent hepatectomy from September 2017 to October 2022 at Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, were enrolled in this study. Patients were categorized into minimally invasive liver resection (MILR) group (Robotic or laparoscopic) and open liver resection (OLR) group. In order to balance the baseline characteristics between the two groups, 1:4 propensity score matching (PSM) was performed on the two groups. The survival parameters and perioperative parameters were compared between the two groups before and after PSM, respectively. RESULTS There was no significant difference in Recurrence Free Survival (RFS) and Overall Survival (OS) between the two groups before and after PSM. Subgroup analysis showed that there were no significant differences in OS and RFS between the two groups regarding anatomical resection, IWATE difficulty score, surgical margins, and postoperative adjuvant therapy. Perioperative parameters and the rate of major postoperative complications were comparable between the two groups. CONCLUSION Minimally invasive approach can provide a comparable long-term survival result compared with conventional open approach for patients with HCC associated with MVI.
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Affiliation(s)
- Zaibo Yang
- Zhejiang Key Laboratory of Multi-Omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China
- Department of Radiology, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
- Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease, Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, 310016, China
- Zhejiang University Cancer Center, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, 311121, China
| | - Yewei Zhang
- Zhejiang Key Laboratory of Multi-Omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China
- Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease, Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, 310016, China
- Zhejiang University Cancer Center, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, 311121, China
- School of Medicine, Shaoxing University, Shaoxing, 312000, Zhejiang, China
| | - Junhao Zheng
- Zhejiang Key Laboratory of Multi-Omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China
- Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease, Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, 310016, China
- Zhejiang University Cancer Center, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, 311121, China
| | - Liye Tao
- Zhejiang Key Laboratory of Multi-Omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China
- Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease, Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, 310016, China
- Zhejiang University Cancer Center, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, 311121, China
| | - Chao Song
- Zhejiang Key Laboratory of Multi-Omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China
- Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease, Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, 310016, China
- Zhejiang University Cancer Center, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, 311121, China
| | - Linghan Gong
- Zhejiang Key Laboratory of Multi-Omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China
- Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease, Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, 310016, China
- Zhejiang University Cancer Center, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, 311121, China
| | - Renan Jin
- Zhejiang Key Laboratory of Multi-Omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China.
- Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease, Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, 310016, China.
- Zhejiang University Cancer Center, Hangzhou, 310058, China.
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, 311121, China.
| | - Xiao Liang
- Zhejiang Key Laboratory of Multi-Omics Precision Diagnosis and Treatment of Liver Diseases, Department of General Surgery, Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, No. 3 East Qingchun Road, Hangzhou, 310016, Zhejiang Province, China.
- Zhejiang Minimal Invasive Diagnosis and Treatment Technology Research Center of Severe Hepatobiliary Disease, Zhejiang Research and Development Engineering Laboratory of Minimally Invasive Technology and Equipment, Hangzhou, 310016, China.
- Zhejiang University Cancer Center, Hangzhou, 310058, China.
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, 311121, China.
- School of Medicine, Shaoxing University, Shaoxing, 312000, Zhejiang, China.
- School of Basic Medical Sciences and Forensic Medicine, Hangzhou Medical College, Hangzhou, 310000, Zhejiang, China.
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5
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Jia G, He P, Dai T, Goh D, Wang J, Sun M, Wee F, Li F, Lim JCT, Hao S, Liu Y, Lim TKH, Ngo NT, Tao Q, Wang W, Umar A, Nashan B, Zhang Y, Ding C, Yeong J, Liu L, Sun C. Spatial immune scoring system predicts hepatocellular carcinoma recurrence. Nature 2025; 640:1031-1041. [PMID: 40074893 DOI: 10.1038/s41586-025-08668-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 01/17/2025] [Indexed: 03/14/2025]
Abstract
Given the high recurrence rates of hepatocellular carcinoma (HCC) post-resection1-3, improved early identification of patients at high risk for post-resection recurrence would help to improve patient outcomes and prioritize healthcare resources4-6. Here we observed a spatial and HCC recurrence-associated distribution of natural killer (NK) cells in the invasive front and tumour centre from 61 patients. Using extreme gradient boosting and inverse-variance weighting, we developed the tumour immune microenvironment spatial (TIMES) score based on the spatial expression patterns of five biomarkers (SPON2, ZFP36L2, ZFP36, VIM and HLA-DRB1) to predict HCC recurrence risk. The TIMES score (hazard ratio = 88.2, P < 0.001) outperformed current standard tools for patient risk stratification including the TNM and BCLC systems. We validated the model in 231 patients from five multicentred cohorts, achieving a real-world accuracy of 82.2% and specificity of 85.7%. The predictive power of these biomarkers emerged through the integration of their spatial distributions, rather than individual marker expression levels alone. In vivo models, including NK cell-specific Spon2-knockout mice, revealed that SPON2 enhances IFNγ secretion and NK cell infiltration at the invasive front. Our study introduces TIMES, a publicly accessible tool for predicting HCC recurrence risk, offering insights into its potential to inform treatment decisions for early-stage HCC.
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MESH Headings
- Animals
- Female
- Humans
- Male
- Mice
- Middle Aged
- Biomarkers, Tumor/analysis
- Biomarkers, Tumor/genetics
- Carcinoma, Hepatocellular/diagnosis
- Carcinoma, Hepatocellular/immunology
- Carcinoma, Hepatocellular/pathology
- Carcinoma, Hepatocellular/surgery
- Cohort Studies
- Extracellular Matrix Proteins/genetics
- Extracellular Matrix Proteins/deficiency
- Extracellular Matrix Proteins/metabolism
- Interferon-gamma/metabolism
- Killer Cells, Natural/immunology
- Killer Cells, Natural/cytology
- Liver Neoplasms/diagnosis
- Liver Neoplasms/immunology
- Liver Neoplasms/pathology
- Liver Neoplasms/surgery
- Mice, Knockout
- Neoplasm Recurrence, Local/immunology
- Neoplasm Recurrence, Local/diagnosis
- Neoplasm Recurrence, Local/pathology
- Reproducibility of Results
- Tumor Microenvironment
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Affiliation(s)
- Gengjie Jia
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Disease, Key Laboratory of Immune Response and Immunotherapy, Institute of Immunology, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Hospital of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Peiqi He
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Disease, Key Laboratory of Immune Response and Immunotherapy, Institute of Immunology, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Hospital of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China
| | - Tianli Dai
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Disease, Key Laboratory of Immune Response and Immunotherapy, Institute of Immunology, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Hospital of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China
| | - Denise Goh
- Institute of Molecular and Cell Biology (IMCB), Agency for Science Technology and Research (A*STAR), Singapore, Singapore
| | - Jiabei Wang
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Clinical Research Hospital of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China
| | - Mengyuan Sun
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Disease, Key Laboratory of Immune Response and Immunotherapy, Institute of Immunology, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Hospital of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China
| | - Felicia Wee
- Institute of Molecular and Cell Biology (IMCB), Agency for Science Technology and Research (A*STAR), Singapore, Singapore
| | - Fuling Li
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Disease, Key Laboratory of Immune Response and Immunotherapy, Institute of Immunology, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Hospital of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China
| | - Jeffrey Chun Tatt Lim
- Institute of Molecular and Cell Biology (IMCB), Agency for Science Technology and Research (A*STAR), Singapore, Singapore
| | - Shuxia Hao
- Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yao Liu
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Clinical Research Hospital of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China
| | - Tony Kiat Hon Lim
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Duke-NUS Medical School, Singapore, Singapore
| | | | - Qingping Tao
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Disease, Key Laboratory of Immune Response and Immunotherapy, Institute of Immunology, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Hospital of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China
| | - Wei Wang
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Disease, Key Laboratory of Immune Response and Immunotherapy, Institute of Immunology, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Clinical Research Hospital of Chinese Academy of Sciences, University of Science and Technology of China, Hefei, China
| | - Ahitsham Umar
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
- Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Disease, Key Laboratory of Immune Response and Immunotherapy, Institute of Immunology, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Björn Nashan
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China
| | - Yongchang Zhang
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Central South University, Changsha, China
| | - Chen Ding
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Institutes of Biomedical Sciences, Human Phenome Institute, Fudan University, Shanghai, China
| | - Joe Yeong
- Institute of Molecular and Cell Biology (IMCB), Agency for Science Technology and Research (A*STAR), Singapore, Singapore.
- Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore.
- Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Singapore, Singapore.
- Cancer Science Institute, National University of Singapore, Singapore, Singapore.
| | - Lianxin Liu
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China.
| | - Cheng Sun
- Department of Hepatobiliary Surgery, Anhui Provincial Clinical Research Center for Hepatobiliary Diseases, Anhui Province Key Laboratory of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), University of Science and Technology of China, Hefei, China.
- Chinese Academy of Sciences Key Laboratory of Innate Immunity and Chronic Disease, Key Laboratory of Immune Response and Immunotherapy, Institute of Immunology, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
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Dong M, Chen F, Huang W, Liao Y, Li W, Wang X, Luo S. Multiregional Radiomics to Predict Microvascular Invasion in Hepatocellular Carcinoma Using Multisequence MRI. J Comput Assist Tomogr 2025:00004728-990000000-00442. [PMID: 40165029 DOI: 10.1097/rct.0000000000001752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
OBJECTIVES This study aimed to develop a multiregional radiomics-based model using multisequence MRI to predict microvascular invasion in hepatocellular carcinoma. METHODS We enrolled 141 patients with hepatocellular carcinoma, including 61 with microvascular invasion, who were diagnosed between March 2017 and July 2022. Clinical data were compared using the Wilcoxon rank-sum test or χ2 test. Patients were randomly divided into training (n=112, 80%) and test (n=29, 20%) data sets. Four MRI sequences-including T2-weighted imaging, T2-weighted imaging with fat suppression, arterial phase-contrast enhancement, and portal venous phase contrast enhancement-were used to build the radiomics model. The tumor volumes of interest were manually delineated, and the expand-5 mm and expand-10 mm volumes of interest were automatically generated. A total of 1409 radiomic features were extracted from each volume of interest. Feature selection was performed using the least absolute shrinkage and selection operator and Spearman correlation analysis. Three logistic regression models (Tumor, Tumor-Expand5, and Tumor-Expand10) were established based on the radiomic features. Model performance was assessed using receiver operating characteristic analysis and Delong's test. RESULTS Maximum tumor diameter, hepatitis B virus DNA, and aspartate aminotransferase levels were significantly different between the groups. The Tumor-Expand5mm model exhibited the best performance among the 3 models, with areas under the curve of 0.90 and 0.84 in the training and test data sets. CONCLUSIONS The Tumor-Expand5 model based on multisequence MRI shows great potential for predicting microvascular invasion in patients with hepatocellular carcinoma, and may further contribute to personal clinical decision-making.
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Affiliation(s)
- Mengying Dong
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
| | - Yuting Liao
- Department of Clinical and Technical Support, Philips (China) Investment Co, Ltd, Haizhu District, Guangzhou, P.R. China
| | - Wenzhu Li
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
| | - Xiaoyi Wang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
| | - Shishi Luo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
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Hwang YJ, Lee H, Hong SK, Yu SJ, Kim H. Membranous Overexpression of Fibronectin Predicts Microvascular Invasion and Poor Survival Outcomes in Patients with Hepatocellular Carcinoma. Gut Liver 2025; 19:275-285. [PMID: 39778882 PMCID: PMC11907257 DOI: 10.5009/gnl240254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 09/05/2024] [Accepted: 09/06/2024] [Indexed: 01/11/2025] Open
Abstract
Background/Aims Fibronectin (FN) has recently been identified as being overexpressed in patients with hepatocellular carcinoma (HCC) and deemed a promising biomarker of vascular invasion. The aim of this study was to examine the patterns of FN expression in HCC cells and their clinicopathological significance, such as their association with vascular invasion and angiogenesis patterns. Methods Immunohistochemical analysis of FN was conducted using tissue microarrays from 258 surgically resected HCCs and matched nontumorous liver tissues. Three distinct FN expression patterns were observed: cytoplasmic, membranous, and sinusoidal. Moderate or strong expression was considered FN-positive. Results Cytoplasmic or sinusoidal FN expression was significantly more common in HCC cells than in the adjacent liver tissue (p<0.001). FN expression was detected in the membranes of HCC cells and absent in nonneoplastic hepatocytes (p<0.001). Overall survival and disease-free survival in patients with HCC cells with membranous FN expression were significantly shorter than those in patients without membranous FN expression. Membranous FN expression in HCC was significantly associated with high serum alpha-fetoprotein (AFP) and protein induced by vitamin K absence-II (PIVKA-II) levels, infiltrative gross type, poor Edmondson-Steiner grade, major vessel invasion, microvascular invasion, macrotrabecular massive subtype, advanced T stage, and vessel-encapsulating tumor cluster pattern. Sinusoidal pattern of FN expression in HCC was significantly associated with high serum AFP and PIVKA-II levels, infiltrative gross type, large tumor size, microvascular invasion, macrotrabecular massive subtype, and vessel-encapsulating tumor cluster patterns. Conclusions Evaluating FN expression in HCC cells may be useful for identifying aggressive cases of HCC with vascular invasion via biopsy.
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Affiliation(s)
- Yoon Jung Hwang
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
- Department of Pathology, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Hyejung Lee
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
| | - Suk Kyun Hong
- Department of Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Su Jong Yu
- Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine and Biomedical Research Institute, Center for Medical Innovation, Seoul National University Hospital, Seoul, Korea
| | - Haeryoung Kim
- Department of Pathology, Seoul National University College of Medicine, Seoul, Korea
- Department of Pathology, Seoul National University Hospital, Seoul, Korea
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Liang Y, Zhong D, Shang J, Yan H, Su Y, Chen Y, Yang Q, Huang X. Efficacy and safety of postoperative adjuvant HAIC with FOLFOX combining PD-1 inhibitors in HCC patients with microvascular invasion: a propensity score matching analysis. BMC Cancer 2025; 25:418. [PMID: 40055613 PMCID: PMC11887270 DOI: 10.1186/s12885-025-13793-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2024] [Accepted: 02/21/2025] [Indexed: 05/13/2025] Open
Abstract
PURPOSE To evaluate the efficacy and safety of postoperative adjuvant hepatic arterial infusion chemotherapy (PA-HAIC) plus programmed death-1 (PD-1) inhibitors versus PA-HAIC alone for hepatocellular carcinoma (HCC) patients with microvascular invasion (MVI). METHODS This retrospective study included HCC patients with MVI who were treated with either PA-HAIC or PA-HAIC plus PD-1 inhibitors between February 2021 and February 2024. The differences in baseline characteristics, disease-free survival (DFS), and overall survival (OS) were compared between the two groups before and after propensity score-matching (PSM). The treatment-related adverse events (TRAEs) were compared among the two groups after PSM. Cox regression analysis was utilized to determine factors affecting DFS and OS. RESULTS A total of 102 patients were included in the study: 65 in the PA-HAIC group and 37 in the PA-HAIC plus PD-1 group. PSM analysis generated 32 matched pairs of patients in the two groups. The HCC patients in the PA-HAIC plus PD-1 group experienced significantly better DFS compared to those in the PA-HAIC group alone (HR: 0.412; P = 0.031). However, there was no significant difference in OS between the two groups (P = 0.124). Multivariate analysis identified the treatment option (PA-HAIC vs. PA-HAIC + PD-1) as an independent predictive factor for DFS of the patients. Furthermore, the results indicated no statistically significant difference in the incidence of TRAEs between the two groups (P < 0.05). CONCLUSION In comparison with PA-HAIC alone, PA-HAIC combined with PD-1 inhibitors could improve the DFS benefits with acceptable safety profiles in HCC patients with MVI.
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Affiliation(s)
- Yuxin Liang
- Department of Liver Transplantation Center and HBP Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Deyuan Zhong
- Department of Liver Transplantation Center and HBP Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China
- Department of Hepatobiliary-Pancreatic Surgery, Cell Transplantation Center, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Jin Shang
- Department of Liver Transplantation Center and HBP Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongtao Yan
- Department of Liver Transplantation Center and HBP Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuhao Su
- Department of Liver Transplantation Center and HBP Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Yahui Chen
- Department of Liver Transplantation Center and HBP Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Qinyan Yang
- Department of Liver Transplantation Center and HBP Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Liver Transplantation Center and HBP Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
| | - Xiaolun Huang
- Department of Liver Transplantation Center and HBP Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Center, School of Medicine, Sichuan Cancer Hospital & Institute, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Liver Transplantation Center and HBP Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
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Zhang J, Zhang Z, Yang C, Liu Q, Song T. Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms. Sci Rep 2025; 15:7977. [PMID: 40055377 PMCID: PMC11889200 DOI: 10.1038/s41598-025-91475-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2024] [Accepted: 02/20/2025] [Indexed: 03/12/2025] Open
Abstract
Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor prognosis, especially when it is complicated by the presence of microvascular invasion (MVI). Identifying patients at high risk of MVI is crucial for personalized treatment strategies. Utilizing the single-cell RNA-sequencing dataset (GSE242889) of HCC, we identified malignant cell subtypes associated with microvascular invasion (MVI), in conjunction with the TCGA dataset, selected a set of MVI-related genes (MRGs). We developed an optimal prognostic model comprising 11 genes (NOP16, YIPF1, HMMR, NDC80, DYNLL1, CDC34, NLN, KHDRBS3, MED8, SLC35G2, RAB3B) based on MVI-related signature genes by integrating single-cell transcriptomic analysis with 101 machine learning algorithms. This model is meticulously crafted to forecast the prognosis of individuals afflicted with hepatocellular carcinoma (HCC). Additionally, we affirmed the predictive precision and superiority of our model through a meta-analysis against existing HCC models. Furthermore, we explored the differences between high- and low-risk groups through mutation and immune infiltration analyses. Lastly, we investigated immunotherapy responses and drug sensitivities between risk groups, providing novel therapeutic insights for liver cancer.
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Affiliation(s)
- Jiayi Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China
| | - Zheng Zhang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China
| | - Chenqing Yang
- Department of Gynaecology and Obstetrics Department, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China
| | - Qingguang Liu
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China.
| | - Tao Song
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China.
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10
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Xie XY, Chen R. Research progress of MRI-based radiomics in hepatocellular carcinoma. Front Oncol 2025; 15:1420599. [PMID: 39980543 PMCID: PMC11839447 DOI: 10.3389/fonc.2025.1420599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 01/20/2025] [Indexed: 02/22/2025] Open
Abstract
Background Primary liver cancer (PLC), notably hepatocellular carcinoma (HCC), stands as a formidable global health challenge, ranking as the sixth most prevalent malignant tumor and the third leading cause of cancer-related deaths. HCC presents a daunting clinical landscape characterized by nonspecific early symptoms and late-stage detection, contributing to its poor prognosis. Moreover, the limited efficacy of existing treatments and high recurrence rates post-surgery compound the challenges in managing this disease. While histopathologic examination remains the cornerstone for HCC diagnosis, its utility in guiding preoperative decisions is constrained. Radiomics, an emerging field, harnesses high-throughput imaging data, encompassing shape, texture, and intensity features, alongside clinical parameters, to elucidate disease characteristics through advanced computational techniques such as machine learning and statistical modeling. MRI radiomics specifically holds significant importance in the diagnosis and treatment of hepatocellular carcinoma (HCC). Objective This study aims to evaluate the methodology of radiomics and delineate the clinical advancements facilitated by MRI-based radiomics in the realm of hepatocellular carcinoma diagnosis and treatment. Methods A systematic review of the literature was conducted, encompassing peer-reviewed articles published between July 2018 and Jan 2025, sourced from PubMed and Google Scholar. Key search terms included Hepatocellular carcinoma, HCC, Liver cancer, Magnetic resonance imaging, MRI, radiomics, deep learning, machine learning, and artificial intelligence. Results A comprehensive analysis of 93 articles underscores the efficacy of MRI radiomics, a noninvasive imaging analysis modality, across various facets of HCC management. These encompass tumor differentiation, subtype classification, histopathological grading, prediction of microvascular invasion (MVI), assessment of treatment response, early recurrence prognostication, and metastasis prediction. Conclusion MRI radiomics emerges as a promising adjunctive tool for early HCC detection and personalized preoperative decision-making, with the overarching goal of optimizing patient outcomes. Nevertheless, the current lack of interpretability within the field underscores the imperative for continued research and validation efforts.
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Affiliation(s)
- Xiao-Yun Xie
- Department of Radiation Oncology, Medical School of Southeast University, Nanjing, China
| | - Rong Chen
- Department of Radiation Oncology, Zhongda Hospital, Nanjing, China
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11
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Yang G, Chen Y, Wang M, Wang H, Chen Y. Impact of microvascular invasion risk on tumor progression of hepatocellular carcinoma after conventional transarterial chemoembolization. Oncologist 2025; 30:oyae286. [PMID: 39475355 PMCID: PMC11884753 DOI: 10.1093/oncolo/oyae286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 09/11/2024] [Indexed: 03/08/2025] Open
Abstract
OBJECTIVE To assess tumor progression in patients with hepatocellular carcinoma (HCC) without macrovascular invasion who underwent treatment with conventional transarterial chemoembolization (cTACE) based on microvascular invasion (MVI) risk within 2 years. METHODS This retrospective investigation comprised adult patients with HCC who had either liver resection or cTACE as their first treatment from January 2016 to December 2021. A predictive model for MVI was developed and validated using preoperative clinical and MRI data from patients with HCC treated with liver resection. The MVI predictive model was applied to patients with HCC receiving cTACE, and differences in tumor progression between the MVI high- and low-risk groups were examined throughout 2 years. RESULTS The MVI prediction model incorporated nonsmooth margin, intratumoral artery, incomplete or absent tumor capsule, and tumor DWI/T2WI mismatch. The area under the receiver operating characteristic curve (AUC) for the prediction model, in the training cohort, was determined to be 0.904 (95% CI, 0.862-0.946), while in the validation cohort, it was 0.888 (0.782-0.994). Among patients with HCC undergoing cTACE, those classified as high risk for MVI possessed a lower rate of achieving a complete response after the first tumor therapy and a higher risk of tumor progression within 2 years. CONCLUSIONS The MVI prediction model developed in this study demonstrates a considerable degree of accuracy. Patients at high risk for MVI who underwent cTACE treatment exhibited a higher risk of tumor progression within 2 years.
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Affiliation(s)
- Guanhua Yang
- The First School of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, People’s Republic of China
| | - Yuxin Chen
- Department of Paediatrics, Division of Respiratory Medicine and Allergology, Sophia Children’s Hospital, Erasmus MC, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
| | - Minglei Wang
- The First School of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, People’s Republic of China
| | - Hongfang Wang
- The First School of Clinical Medicine, General Hospital of Ningxia Medical University, Yinchuan, People’s Republic of China
| | - Yong Chen
- Department of Interventional Radiology, General Hospital of Ningxia Medical University, Yinchuan, People’s Republic of China
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Yang C, Liang Z, Zhao L, Li R, Ma P. Prediction of microvascular invasion in hepatocellular carcinoma using a preoperative serum C-reactive protein-based nomogram. Sci Rep 2025; 15:522. [PMID: 39748118 PMCID: PMC11696813 DOI: 10.1038/s41598-024-84835-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Accepted: 12/27/2024] [Indexed: 01/04/2025] Open
Abstract
Microvascular invasion (MVI) diagnosis relies on postoperative pathological examinations, underscoring the urgent need for a novel diagnostic method. C-Reactive Protein (CRP), has shown significant relevance to hepatocellular carcinoma (HCC) prognosis. This study aims to explore the relationship between preoperative serum CRP levels and microvascular invasion in hepatocellular carcinoma and develop a nomogram model for predicting MVI. Patients were categorized into MVI-positive and MVI-negative groups for analysis. Serum CRP levels were compared between the two groups. And then use LASSO regression to screen variables and build a nomogram. CRP levels showed significant differences between the MVI-positive and MVI-negative groups. Multivariable logistic regression analysis identified CRP (OR = 4.85, P < 0.001), lnAFP (OR = 3.11, P < 0.001), WBC count (OR = 2.73, P = 0.003), and tumor diameter (OR = 2.38, P = 0.01) as independent predictors of MVI. A nomogram based on these variables showed good predictive performance in both the training and validation cohorts with dual validation. The clinical prediction nomogram model, which includes serum CRP levels, WBC count, tumor diameter, and serum AFP levels, showed good performance in predicting MVI in both the training and validation cohorts.
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Affiliation(s)
- Chaohao Yang
- Hepatopancreatobiliary Surgery Department, The first affiliated hospital of Zhengzhou university, Zhengzhou, 450001, China
| | - Zhiwei Liang
- Hepatopancreatobiliary Surgery Department, The first affiliated hospital of Zhengzhou university, Zhengzhou, 450001, China
| | - Longshuan Zhao
- Hepatopancreatobiliary Surgery Department, The first affiliated hospital of Zhengzhou university, Zhengzhou, 450001, China
| | - Renfeng Li
- Hepatopancreatobiliary Surgery Department, The first affiliated hospital of Zhengzhou university, Zhengzhou, 450001, China.
| | - Pengfei Ma
- Hepatopancreatobiliary Surgery Department, The first affiliated hospital of Zhengzhou university, Zhengzhou, 450001, China.
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Ma L, Zhang C, Wen Y, Xing K, Li S, Geng Z, Liao S, Yuan S, Li X, Zhong C, Hou J, Zhang J, Gao M, Xu B, Guo R, Wei W, Xie C, Lu L. Imaging-based surrogate classification for risk stratification of hepatocellular carcinoma with microvascular invasion to adjuvant hepatic arterial infusion chemotherapy: a multicenter retrospective study. Int J Surg 2025; 111:872-883. [PMID: 39051653 PMCID: PMC11745592 DOI: 10.1097/js9.0000000000001903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 06/24/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND Patients with microvascular invasion (MVI)-positive hepatocellular carcinoma (HCC) have shown promising results with adjuvant hepatic arterial infusion chemotherapy (HAIC) with FOLFOX after curative resection. The authors aim to develop an imaging-derived biomarker to depict MVI-positive HCC patients more precisely and promote individualized treatment strategies of adjuvant HAIC. MATERIALS AND METHODS Patients with MVI-positive HCC were identified from five academic centers and utilized for model development ( n =470). Validation cohorts were pooled from a previously reported prospective clinical study conducted [control cohort ( n =145), adjuvant HAIC cohort ( n =143)] (NCT03192618). The primary endpoint was recurrence-free survival (RFS). Imaging features were thoroughly reviewed, and multivariable logistic regression analysis was employed for model development. Transcriptomic sequencing was conducted to identify the associated biological processes. RESULTS Arterial phase peritumoral enhancement, boundary of the tumor enhancement, tumor necrosis stratification, and boundary of the necrotic area were selected and incorporated into the nomogram for RFS. The imaging-based model successfully stratified patients into two distinct prognostic subgroups in both the training, control, and adjuvant HAIC cohorts (median RFS, 6.00 vs. 66.00 months, 4.86 vs. 24.30 months, 11.46 vs. 39.40 months, all P <0.01). Furthermore, no significant statistical difference was observed between patients at high risk of adjuvant HAIC and those in the control group ( P =0.61). The area under the receiver operating characteristic curve at 2 years was found to be 0.83, 0.84, and 0.73 for the training, control, and adjuvant HAIC cohorts, respectively. Transcriptomic sequencing analyses revealed associations between the radiological features and immune-regulating signal transduction pathways. CONCLUSION The utilization of this imaging-based model could help to better characterize MVI-positive HCC patients and facilitate the precise subtyping of patients who genuinely benefit from adjuvant HAIC treatment.
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Affiliation(s)
- Lidi Ma
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
| | - Cheng Zhang
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
| | - Yuhua Wen
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Liver Surgery, Sun Yat-sen University Cancer Center
| | - Kaili Xing
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Anesthesiology, Sun Yat-Sen University Cancer Center
| | - Shaohua Li
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Liver Surgery, Sun Yat-sen University Cancer Center
| | - Zhijun Geng
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
| | - Shuting Liao
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
| | - Shasha Yuan
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University
| | - Xinming Li
- Department of Radiology, Zhujiang Hospital, Southern Medical University
| | - Chong Zhong
- Department of Hepatobilliary Surgery, The First Affiliated Hospital of Guangzhou University of Chinese Medicine
| | - Jing Hou
- Department of Radiology, Hunan Cancer Hospital; Changsha
| | - Jie Zhang
- Department of Radiology, Zhuhai People ‘s Hospital (Zhuhai hospital affiliated with Jinan University), Zhuhai, P.R China
| | - Mingyong Gao
- Department of Radiology, The First People’s Hospital of Foshan, Foshan, Guangdong
| | - Baojun Xu
- Department of Radiology, The First Affiliated Hospital of Jinan University, Guangzhou
| | - Rongping Guo
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Liver Surgery, Sun Yat-sen University Cancer Center
| | - Wei Wei
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Liver Surgery, Sun Yat-sen University Cancer Center
| | - Chuanmiao Xie
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China
| | - Lianghe Lu
- Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center
- Department of Liver Surgery, Sun Yat-sen University Cancer Center
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Famularo S, Penzo C, Maino C, Milana F, Oliva R, Marescaux J, Diana M, Romano F, Giuliante F, Ardito F, Grazi GL, Donadon M, Torzilli G. Preoperative detection of hepatocellular carcinoma's microvascular invasion on CT-scan by machine learning and radiomics: A preliminary analysis. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2025; 51:108274. [PMID: 38538504 DOI: 10.1016/j.ejso.2024.108274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/20/2024] [Accepted: 03/16/2024] [Indexed: 08/22/2024]
Abstract
INTRODUCTION Microvascular invasion (MVI) is the main risk factor for overall mortality and recurrence after surgery for hepatocellular carcinoma (HCC).The aim was to train machine-learning models to predict MVI on preoperative CT scan. METHODS 3-phases CT scans were retrospectively collected among 4 Italian centers. DICOM files were manually segmented to detect the liver and the tumor(s). Radiomics features were extracted from the tumoral, peritumoral and healthy liver areas in each phase. Principal component analysis (PCA) was performed to reduce the dimensions of the dataset. Data were divided between training (70%) and test (30%) sets. Random-Forest (RF), fully connected MLP Artificial neural network (neuralnet) and extreme gradient boosting (XGB) models were fitted to predict MVI. Prediction accuracy was estimated in the test set. RESULTS Between 2008 and 2022, 218 preoperative CT scans were collected. At the histological specimen, 72(33.02%) patients had MVI. First and second order radiomics features were extracted, obtaining 672 variables. PCA selected 58 dimensions explaining >95% of the variance.In the test set, the XGB model obtained Accuracy = 68.7% (Sens: 38.1%, Spec: 83.7%, PPV: 53.3% and NPV: 73.4%). The neuralnet showed an Accuracy = 50% (Sens: 52.3%, Spec: 48.8%, PPV: 33.3%, NPV: 67.7%). RF was the best performer (Acc = 96.8%, 95%CI: 0.91-0.99, Sens: 95.2%, Spec: 97.6%, PPV: 95.2% and NPV: 97.6%). CONCLUSION Our model allowed a high prediction accuracy of the presence of MVI at the time of HCC diagnosis. This could lead to change the treatment allocation, the surgical extension and the follow-up strategy for those patients.
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Affiliation(s)
- Simone Famularo
- Hepatobiliary Surgery Unit, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Catholic University of the Sacred Heart, Rome, Italy; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; IRCAD, Research Institute Against Cancer of the Digestive System, 1 Place de l'Hôpital, Strasbourg, 67091, France.
| | - Camilla Penzo
- Pole d'Expertise de la Regulation Numérique (PEReN), Paris, France
| | - Cesare Maino
- Department of Radiology, San Gerardo Hospital, Monza, Italy
| | - Flavio Milana
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Riccardo Oliva
- IRCAD, Research Institute Against Cancer of the Digestive System, 1 Place de l'Hôpital, Strasbourg, 67091, France
| | - Jacques Marescaux
- IRCAD, Research Institute Against Cancer of the Digestive System, 1 Place de l'Hôpital, Strasbourg, 67091, France
| | - Michele Diana
- IRCAD, Research Institute Against Cancer of the Digestive System, 1 Place de l'Hôpital, Strasbourg, 67091, France; Department of General, Digestive and Endocrine Surgery, University Hospital of Strasbourg, France; ICube Lab, Photonics for Health, Strasbourg, France
| | - Fabrizio Romano
- School of Medicine and Surgery, University of Milan-Bicocca, Department of Surgery, San Gerardo Hospital, Monza, Italy
| | - Felice Giuliante
- Hepatobiliary Surgery Unit, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Catholic University of the Sacred Heart, Rome, Italy
| | - Francesco Ardito
- Hepatobiliary Surgery Unit, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Catholic University of the Sacred Heart, Rome, Italy
| | - Gian Luca Grazi
- Division of Hepatobiliarypancreatic Unit, IRCCS - Regina Elena National Cancer Institute, Rome, Italy
| | - Matteo Donadon
- Department of Health Sciences, Università del Piemonte Orientale, Novara, Italy; Department of General Surgery, University Maggiore Hospital Della Carità, Novara, Italy
| | - Guido Torzilli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy; Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
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Yang S, Ni H, Zhang A, Zhang J, Zang H, Ming Z. Significance of anatomical resection and wide surgical margin for HCC patients with MVI undergoing laparoscopic hepatectomy: A multicenter study. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2025; 51:109353. [PMID: 39489041 DOI: 10.1016/j.ejso.2024.109353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2024] [Revised: 10/24/2024] [Accepted: 10/29/2024] [Indexed: 11/05/2024]
Abstract
OBJECTIVE To investigate the impact of surgical resection margin and hepatic resection type on prognosis and compare their prognostic significance on patients with hepatocellular carcinoma (HCC) with or without microvascular invasion (MVI) who underwent laparoscopic liver resection (LLR). METHODS A retrospective analysis was conducted on 320 patients with HCC who underwent LLR. According to the grading of MVI, patients were classified as M0, M1 and M2. Patients were divided into the anatomical resection (AR) and nonanatomical resection (NAR) groups according to the hepatic resection type. Survival and Cox regression analyses were performed to explore the effects of AR and NAR, wide and narrow resection margin on overall survival (OS) and time to recurrence (TTR). RESULTS In the whole cohort, narrow resection margin was an independent risk factor for OS and TTR, whereas NAR was not. Subgroup analysis showed that narrow resection margin and NAR were both independent risk factors for OS and TTR in HCC patients with MVI. The 5-year OS and TTR rates of the two groups (NAR-wide resection margin and AR-narrow resection margin) with M1 were 85.3 % versus 62 % and 34.4 % versus 60.2 %. Similarly, the 5-year OS and TTR rates of the two groups (NAR-wide resection margin and AR-narrow resection margin) with M2 were 80.2 % versus 47.9 % and 30.8 % versus 64.8 %. CONCLUSIONS Anatomical hepatectomy and wide resection margin were independent protective factors for HCC patients with MVI receiving LLR. Nonetheless, wide resection margin had a greater impact on prognosis than anatomical hepatectomy.
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Affiliation(s)
- Shiye Yang
- Department of Comprehensive Surgery, Vascular Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, 666 Shengli Road, Chongchuan District, Nantong City, Jiangsu Province, 226014, China
| | - Haishun Ni
- Department of General Surgery, Nantong Second People's Hospital, 298 Xinhua Road, Gangzha District, Nantong City, Jiangsu Province, 226002, China
| | - Aixian Zhang
- Department of Hepato-Biliary-Pancreatic Surgery, Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100080, China
| | - Jixiang Zhang
- Department of Hepatobiliary Surgery, Zhongshan People's Hospital, 2 Sun Wen East Road, Zhongshan City, Guangdong Province, 528403, China
| | - Hong Zang
- Department of Comprehensive Surgery, Hepato-Biliary-Pancreatic Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, 666 Shengli Road, Chongchuan District, Nantong City, Jiangsu Province, 226014, China.
| | - Zhibing Ming
- Department of Comprehensive Surgery, Vascular Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, 666 Shengli Road, Chongchuan District, Nantong City, Jiangsu Province, 226014, China.
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Deng M, Zhong C, Li D, Guan R, Lee C, Chen H, Qin W, Cai H, Guo R, Chen Z. Hepatic arterial infusion chemotherapy-based conversion hepatectomy in responders versus nonresponders with hepatocellular carcinoma: a multicenter cohort study. Int J Surg 2025; 111:135-145. [PMID: 39166963 PMCID: PMC11745704 DOI: 10.1097/js9.0000000000002043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 08/01/2024] [Indexed: 08/23/2024]
Abstract
BACKGROUND Hepatic arterial infusion chemotherapy (HAIC) has shown satisfactory therapeutic efficacy in unresectable hepatocellular carcinoma (HCC) and is regarded as an important conversion treatment. However, limited information is available regarding the optimal timing of HAIC-based conversion hepatectomy. This study aims to determine the optimal timing for HAIC-based conversion surgery in patients with HCC. METHODS Data from a retrospective cohort of patients who underwent HAIC-based conversion hepatectomy were reviewed. Oncological outcomes, surgical information, and risk factors were comparatively analyzed. RESULTS In total, 424 patients with HCC who underwent HAIC-based conversion hepatectomy were included and were divided into responder ( n =312) and nonresponder ( n =112) groups. The overall survival (OS) and recurrence-free survival (RFS) rates of both the whole responder cohort and patients who achieved a response after 4-6 cycles of HAIC were significantly better than those of the nonresponder cohort. Higher OS and RFS were observed in responders than in nonresponders with advanced-stage HCC. Patients in the responder group had a shorter occlusion duration and less intraoperative blood loss than those in the nonresponder group. There were no significant differences in other surgical information or postoperative complications between the two groups. Tumor response, differentiation, postoperative alpha-fetoprotein level, postoperative protein induced by vitamin K absence or antagonist-II level, age, microvascular invasion, pre-HAIC neutrophil-to-lymphocyte ratio, and preoperative systemic inflammatory response index were independent risk factors for poor long-term survival. CONCLUSIONS Conversion surgery should be considered when tumor response is achieved. Our findings may be useful in guiding surgeons and patients in decision-making regarding HAIC-based conversion hepatectomy.
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Affiliation(s)
- Min Deng
- Department of General Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen
- Department of Liver Surgery, Sun Yat-sen University Cancer Center
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine
| | - Chong Zhong
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangzhou University of Chinese Medicine
| | - Dong Li
- Department of General Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen
| | - Renguo Guan
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou
| | - Carol Lee
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong Special Administrative Region
| | - Huanwei Chen
- Department of Hepatic Surgery, The Affiliated Foshan Hospital of Sun Yat-Sen University, Foshan
| | - Wei Qin
- Department of General Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen
| | - Hao Cai
- Department of General Surgery, Department of Transplantation, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Rongping Guo
- Department of Liver Surgery, Sun Yat-sen University Cancer Center
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine
| | - Zubing Chen
- Department of General Surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen
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Meng A, Zhuang Y, Huang Q, Tang L, Yang J, Gong P. Development and validation of a cross-modality tensor fusion model using multi-modality MRI radiomics features and clinical radiological characteristics for the prediction of microvascular invasion in hepatocellular carcinoma. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2025; 51:109364. [PMID: 39536525 DOI: 10.1016/j.ejso.2024.109364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 10/29/2024] [Accepted: 11/03/2024] [Indexed: 11/16/2024]
Abstract
OBJECTIVES To develop and validate a cross-modality tensor fusion (CMTF) model using multi-modality MRI radiomics features and clinical radiological characteristics for the prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). MATERIALS AND METHODS This study included 174 HCC patients (47 MVI-positive and 127 MVI-negative) confirmed by postoperative pathology. The synthetic minority over-sampling technique was used to augment MVI-positive samples. The amplified dataset of 254 samples (127 MVI-positive and 127 MVI-negative) was randomly divided into training and test cohorts in a 7:3 ratio. Radiomics features were respectively extracted from arterial phase, delayed phase, diffusion-weighted imaging, and fat-suppressed T2-weighted imaging. The least absolute shrinkage and selection operator was used for feature selection. Univariate and multivariate logistic regression analyses were employed to identify clinical and radiological independent predictors. The selected multi-modality MRI radiomics features, clinical and radiological characteristics were used to construct the CMTF model, single modality (SM) model, early fusion (EF) model. RESULTS The CMTF model demonstrated superior performance in predicting MVI compared to the SM and EF models. When integrating four MRI modalities, the CMTF model achieved a high area under the curve (AUC) with 95 % confidence interval (95 % CI) of 0.894 (0.820-0.968). Additionally, incorporating clinical and radiological characteristics further enhanced the predictive performance of CMTF model, the AUC (95 % CI) value increased to 0.945 (0.892-0.998). CONCLUSION The CMTF model showed promising performance in preoperative MVI prediction, providing a more effective non-invasive detection tool for HCC patients.
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Affiliation(s)
- Ao Meng
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Yinping Zhuang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Qian Huang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Li Tang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China
| | - Jing Yang
- Department of Interventional Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China
| | - Ping Gong
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
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Wu F, Cao G, Lu J, Ye S, Tang X. Correlation between 18 F-FDG PET/CT metabolic parameters and microvascular invasion before liver transplantation in patients with hepatocellular carcinoma. Nucl Med Commun 2024; 45:1033-1038. [PMID: 39267532 PMCID: PMC11537472 DOI: 10.1097/mnm.0000000000001897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Accepted: 08/30/2024] [Indexed: 09/17/2024]
Abstract
BACKGROUND Microvascular infiltration (MVI) before liver transplantation (LT) in patients with hepatocellular carcinoma (HCC) is associated with postoperative tumor recurrence and survival. MVI is mainly assessed by pathological analysis of tissue samples, which is invasive and heterogeneous. PET/computed tomography (PET/CT) with 18 F-labeled fluorodeoxyglucose ( 18 F-FDG) as a tracer has been widely used in the examination of malignant tumors. This study investigated the association between 18 F-FDG PET/CT metabolic parameters and MVI before LT in HCC patients. METHODS About 124 HCC patients who had 18 F-FDG PET/CT examination before LT were included. The patients' clinicopathological features and 18 F-FDG PET/CT metabolic parameters were recorded. Correlations between clinicopathological features, 18 F-FDG PET/CT metabolic parameters, and MVI were analyzed. ROC curve was used to determine the optimal diagnostic cutoff value, area under the curve (AUC), sensitivity, and specificity for predictors of MVI. RESULT In total 72 (58.06%) patients were detected with MVI among the 124 HCC patients. Univariate analysis showed that tumor size ( P = 0.001), T stage ( P < 0.001), maximum standardized uptake value (SUV max ) ( P < 0.001), minimum standardized uptake value (SUV min ) ( P = 0.031), mean standardized uptake value (SUV mean ) ( P = 0.001), peak standardized uptake value (SUV peak ) ( P = 0.001), tumor-to-liver ratio (SUV ratio ) ( P = 0.010), total lesion glycolysis (TLG) ( P = 0.006), metabolic tumor volume (MTV) ( P = 0.011) and MVI were significantly different. Multivariate logistic regression showed that tumor size ( P = 0.018), T stage ( P = 0.017), TLG ( P = 0.023), and MTV ( P = 0.015) were independent predictors of MVI. In the receiver operating characteristic curve, TLG predicted MVI with an AUC value of 0.645. MTV predicted MVI with an AUC value of 0.635. Patients with tumor size ≥5 cm, T3-4, TLG > 400.67, and MTV > 80.58 had a higher incidence of MVI. CONCLUSION 18 F-FDG PET/CT metabolic parameters correlate with MVI and may be used as a noninvasive technique to predict MVI before LT in HCC patients.
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Affiliation(s)
- Fan Wu
- Department of Nuclear Medicine and Radiology, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University
| | - Guohong Cao
- Department of Nuclear Medicine and Radiology, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University
| | - Jinlan Lu
- Department of Radiology, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital
| | - Shengli Ye
- Department of Nuclear Medicine and Radiology, Shulan (Hangzhou) Hospital, Shulan International Medical College, Zhejiang Shuren University
| | - Xin Tang
- Department of Radiology, Hangzhou Wuyunshan Hospital, Hangzhou Health Promotion Research Institute, Hangzhou, China
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Aujla UI, Syed IA, Rafi K, Naveed A, Malik AK, Khan MY, Haq IU, Rashid S, Butt OT, Dar F. Predicting Microvascular Invasion in Liver Transplant Recipients for Hepatocellular Carcinoma. Cureus 2024; 16:e75007. [PMID: 39749089 PMCID: PMC11694041 DOI: 10.7759/cureus.75007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/02/2024] [Indexed: 01/04/2025] Open
Abstract
BACKGROUND Among primary liver tumors, hepatocellular carcinoma (HCC) is considered the most common hepatic tumor. Liver transplantation is one of the curative treatment options for HCC. However, the risk of HCC recurrence after liver transplantation varies and is influenced by various factors. Microvascular invasion (MVI) is a major factor associated with HCC recurrence after a liver transplant (LT). The study assessed the pre-transplant factors to predict MVI on explant liver specimens. METHODS The retrospective study included adult LT recipients with HCC on explant specimens to identify pre-transplant predictors of MVI. Univariate analyses, including Mann-Whitney U tests and chi-square tests, were conducted to assess associations between variables and MVI. Logistic regression was employed for multivariate analysis, including variables significant in univariate analysis. Pearson or Spearman correlation coefficients were calculated to examine correlations between continuous variables. Cohen's kappa coefficient was used to measure inter-rater reliability. RESULTS Out of 523 LT recipients, 136 (26%) were diagnosed with HCC based on pre-transplant imaging and histopathological analysis of the explanted liver. Descriptive data showed an average age of 54.06 ± 8.16 years (range: 15-70), with a majority being male (76.47%). Hepatitis C (HCV) was the leading etiology (72.8%). Most patients had moderately differentiated grade-II tumors (75.7%) and met the Milan criteria (74.3%). Mean pre-operative alpha-fetoprotein (pre-op AFP) levels were 104.42 ± 308.38 ng/ml. 74.3% were within the Milan criteria. MVI was present in 28.7%. The frequency of MVI among HCCs within vs. outside Milan criteria was not statistically significant (26.73% vs. 34.28% (p = 0.395)). Univariate analysis revealed that pre-op AFP levels (p = 0.001), Child-Turcotte Pugh class (p=0.05), and body mass index (p=0.02) were significantly associated with MVI. Multivariate logistic regression analysis showed that pre-op AFP was the only independent predictor of MVI (OR: 1.006, 95% CI: 1.003-1.008, p < 0.001). CONCLUSION This study not only reinforces the clinical significance of pre-op AFP levels as a simple pre-transplant predictor of MVI in patients with HCC but also advocates for the safety of liver transplantation beyond conventional Milan criteria, promoting extended LT protocols.
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Affiliation(s)
- Usman I Aujla
- Gastroenterology and Hepatology, Pakistan Kidney and Liver Institute and Research Center, Lahore, PAK
| | - Imran Ali Syed
- Gastroenterology and Hepatology, Pakistan Kidney and Liver Institute and Research Center, Lahore, PAK
| | - Kashif Rafi
- Gastroenterology, Pakistan Kidney and Liver Institute and Research Center, Lahore, PAK
| | - Ammara Naveed
- Hepatology, Pakistan Kidney and Liver Institute and Research Center, Lahore, PAK
| | - Ahmad K Malik
- Adult Gastroenterology, Pakistan Kidney and Liver Institute and Research Center, Lahore, PAK
| | - Muhammad Yasir Khan
- Hepatopancreatobiliary and Liver Transplant Surgery, Pakistan Kidney and Liver Institute and Research Center, Lahore, PAK
| | - Ihsan Ul Haq
- Hepatopancreatobiliary and Liver Transplant Surgery, Pakistan Kidney and Liver Institute and Research Center, Lahore, PAK
| | - Sohail Rashid
- Hepatopancreatobiliary and Liver Transplant Surgery, Pakistan Kidney and Liver Institute and Research Center, Lahore, PAK
| | - Osama T Butt
- Gastroenterology and Hepatology, Pakistan Kidney and Liver Institute and Research Center, Lahore, PAK
| | - Faisal Dar
- Hepatopancreatobiliary and Liver Transplant Surgery, Pakistan Kidney and Liver Institute and Research Center, Lahore, PAK
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Yang S, Ni H, Zhang A, Zhang J, Liang H, Li X, Qian J, Zang H, Ming Z. Grading severity of MVI impacts long-term outcomes after laparoscopic liver resection for early-stage hepatocellular carcinoma: A multicenter study. Am J Surg 2024; 238:115988. [PMID: 39342882 DOI: 10.1016/j.amjsurg.2024.115988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 08/26/2024] [Accepted: 09/23/2024] [Indexed: 10/01/2024]
Abstract
PURPOSE To examine the relationship between microvascular invasion (MVI) grading severity and long-term outcomes in early-stage hepatocellular carcinoma (HCC) patients undergoing laparoscopic liver resection (LLR). METHODS Patients who had LLR for early-stage HCC were enrolled. According to the grading severity of MVI, patients were classified into M0, M1 and M2. Recurrence-free survival (RFS) and overall survival (OS) among the groups were compared. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors of OS and RFS. RESULTS Among 233 patients, MVI grading as M0, M1, and M2 accounts for 122 (52.4 %), 84 (36 %), and 27 (11.6 %) patients, respectively. The median OS and RFS in patients with M0, M1, and M2 were 84.9, 40.1, and 25.2 months; and 76.9, 27.0, and 18.8 months, respectively. Multivariable analyses identified both M1 and M2 to be independent risk factors for OS and RFS. CONCLUSION Grading severity of MVI was independently associated with RFS and OS after LLR for early-stage HCC. Patients with MVI, especially those with M2, should receive stringent recurrence surveillance and active adjuvant therapy.
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Affiliation(s)
- Shiye Yang
- Department of Comprehensive Surgery, Vascular Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, 666 Shengli Road, Chongchuan District, Nantong City, Jiangsu Province, 226014, China
| | - Haishun Ni
- Department of General Surgery, Nantong Second People's Hospital, 298 Xinhua Road, Gangzha District, Nantong City, Jiangsu Province, 226002, China
| | - Aixian Zhang
- Department of Hepato-Biliary-Pancreatic Surgery, Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Haidian District, Beijing, 100080, China
| | - Jixiang Zhang
- Department of Hepatobiliary Surgery, Zhongshan People's Hospital, 2 Sun Wen East Road, Zhongshan City, Guangdong Province, 528403, China
| | - Huoqi Liang
- Department of Comprehensive Surgery, Vascular Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, 666 Shengli Road, Chongchuan District, Nantong City, Jiangsu Province, 226014, China
| | - Xing Li
- Department of Comprehensive Surgery, Vascular Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, 666 Shengli Road, Chongchuan District, Nantong City, Jiangsu Province, 226014, China
| | - Jiayi Qian
- Department of Comprehensive Surgery, Vascular Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, 666 Shengli Road, Chongchuan District, Nantong City, Jiangsu Province, 226014, China
| | - Hong Zang
- Department of Comprehensive Surgery, Hepato-Biliary-Pancreatic Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, 666 Shengli Road, Chongchuan District, Nantong City, Jiangsu Province, 226014, China.
| | - Zhibing Ming
- Department of Comprehensive Surgery, Vascular Surgery, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, 666 Shengli Road, Chongchuan District, Nantong City, Jiangsu Province, 226014, China.
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Zhao Y, Wang S, Wang Y, Li J, Liu J, Liu Y, Ji H, Su W, Zhang Q, Song Q, Yao Y, Liu A. Deep learning radiomics based on contrast enhanced MRI for preoperatively predicting early recurrence in hepatocellular carcinoma after curative resection. Front Oncol 2024; 14:1446386. [PMID: 39582540 PMCID: PMC11581961 DOI: 10.3389/fonc.2024.1446386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2024] [Accepted: 10/21/2024] [Indexed: 11/26/2024] Open
Abstract
Purpose To explore the role of deep learning (DL) and radiomics-based integrated approach based on contrast enhanced magnetic resonance imaging (CEMRI) for predicting early recurrence (ER) in hepatocellular carcinoma (HCC) patients after curative resection. Methods Total 165 HCC patients (ER, n = 96 vs. non-early recurrence (NER), n = 69) were retrospectively collected and divided into a training cohort (n = 132) and a validation cohort (n = 33). From pretreatment CEMR images, a total of 3111 radiomics features were extracted, and radiomics models were constructed using five machine learning classifiers (logistic regression, support vector machine, k-nearest neighbor, extreme gradient Boosting, and multilayer perceptron). DL models were established via three variations of ResNet architecture. The clinical-radiological (CR), radiomics combined with clinical-radiological (RCR), and deep learning combined with RCR (DLRCR) models were constructed. Model discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. The best-performing model was compared with the widely used staging systems and preoperative prognostic indexes. Results The RCR model (area under the curve (AUC): 0.841 and 0.811) and the optimal radiomics model (AUC: 0.839 and 0.804) achieved better performance than the CR model (AUC: 0.662 and 0.752) in the training and validation cohorts, respectively. The optimal DL model (AUC: 0.870 and 0.826) outperformed the radiomics model in the both cohorts. The DL, radiomics, and CR predictors (aspartate aminotransferase (AST) and tumor diameter) were combined to construct the DLRCR model. The DLRCR model presented the best performance over any model, yielding an AUC, an accuracy, a sensitivity, a specificity of 0.917, 0.886, 0.889, and 0.882 in the training cohort and of 0.844, 0.818, 0.800, and 0.846 in the validation cohort, respectively. The DLRCR model achieved better clinical utility compared to the clinical staging systems and prognostic indexes. Conclusion Both radiomics and DL models derived from CEMRI can predict HCC recurrence, and DL and radiomics-based integrated approach can provide a more effective tool for the precise prediction of ER for HCC patients undergoing resection.
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Affiliation(s)
- Ying Zhao
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Sen Wang
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Yue Wang
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Jun Li
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Jinghong Liu
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Yuhui Liu
- College of Medical Imaging, Dalian Medical University, Dalian, China
| | - Haitong Ji
- College of Medical Imaging, Dalian Medical University, Dalian, China
| | - Wenhan Su
- College of Medical Imaging, Dalian Medical University, Dalian, China
| | - Qinhe Zhang
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Qingwei Song
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Yu Yao
- Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, China
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Ailian Liu
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
- Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging, Dalian, China
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22
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Lu MY, Chuang WL, Yu ML. The role of artificial intelligence in the management of liver diseases. Kaohsiung J Med Sci 2024; 40:962-971. [PMID: 39440678 DOI: 10.1002/kjm2.12901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2024] [Revised: 09/24/2024] [Accepted: 09/24/2024] [Indexed: 10/25/2024] Open
Abstract
Universal neonatal hepatitis B virus (HBV) vaccination and the advent of direct-acting antivirals (DAA) against hepatitis C virus (HCV) have reshaped the epidemiology of chronic liver diseases. However, some aspects of the management of chronic liver diseases remain unresolved. Nucleotide analogs can achieve sustained HBV DNA suppression but rarely lead to a functional cure. Despite the high efficacy of DAAs, successful antiviral therapy does not eliminate the risk of hepatocellular carcinoma (HCC), highlighted the need for cost-effective identification of high-risk populations for HCC surveillance and tailored HCC treatment strategies for these populations. The accessibility of high-throughput genomic data has accelerated the development of precision medicine, and the emergence of artificial intelligence (AI) has led to a new era of precision medicine. AI can learn from complex, non-linear data and identify hidden patterns within real-world datasets. The combination of AI and multi-omics approaches can facilitate disease diagnosis, biomarker discovery, and the prediction of treatment efficacy and prognosis. AI algorithms have been implemented in various aspects, including non-invasive tests, predictive models, image diagnosis, and the interpretation of histopathology findings. AI can support clinicians in decision-making, alleviate clinical burdens, and curtail healthcare expenses. In this review, we introduce the fundamental concepts of machine learning and review the role of AI in the management of chronic liver diseases.
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Affiliation(s)
- Ming-Ying Lu
- Division of Hepatobiliary, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine and Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Wan-Long Chuang
- Division of Hepatobiliary, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine and Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ming-Lung Yu
- Division of Hepatobiliary, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine and Hepatitis Research Center, College of Medicine and Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, Taiwan
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung, Taiwan
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23
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Zhang X, Chen C, Wang Y, Xu J. Recurrence risk prediction models for hepatocellular carcinoma after liver transplantation. J Gastroenterol Hepatol 2024; 39:2272-2280. [PMID: 39113259 DOI: 10.1111/jgh.16693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 07/05/2024] [Accepted: 07/17/2024] [Indexed: 12/06/2024]
Abstract
Liver transplantation (LT) is an effective method for curing hepatocellular carcinoma (HCC). However postoperative tumor recurrence can lead to higher mortality rates. To select suitable candidates for LT, the Milan Criteria (MC) were first proposed based on tumor morphological characteristics. For those patients who meet the MC, the MC can effectively reduce the postoperative tumor recurrence rate and improve the prognosis of patients undergoing LT. It has always been internationally recognized as the gold standard for selecting candidates for LT, marking a milestone in the history of LT for HCC. However, its strict conditions exclude some HCC patients who could benefit from LT. Therefore, comprehension consideration criteria, including serum biomarkers, tumor histology, and other factor, have been continuously proposed in addition to tumor morphology. This article summaries the prediction model for HCC recurrence after LT from five aspects: tumor morphology, serum markers, histopathology, cellular inflammatory factors and downstaging treatment before transplantation. The aim is to assist clinicians in accurately assessing HCC status, selecting appropriate liver transplant candidates, maximize graft and patients' survival, and optimizing the utilization of social health resources.
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Affiliation(s)
- Xu Zhang
- Academy of Medical Sciences, Shanxi Medical University, Taiyuan, China
| | - Chi Chen
- Department of Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yan Wang
- Hepatobiliary and Pancreatic Surgery and Liver Transplantation Center, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jun Xu
- Hepatobiliary and Pancreatic Surgery and Liver Transplantation Center, First Hospital of Shanxi Medical University, Taiyuan, China
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24
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Zhu Y, Wang AD, Gu LL, Dai QQ, Zheng GQ, Chen T, Wu CL, Jia WD, Zhang FB. A nomogram model for early recurrence of HBV-related hepatocellular carcinomas after radical hepatectomy. Front Endocrinol (Lausanne) 2024; 15:1374245. [PMID: 39286273 PMCID: PMC11402705 DOI: 10.3389/fendo.2024.1374245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 08/16/2024] [Indexed: 09/19/2024] Open
Abstract
Background To identify the risk factors and construct a predictive model for early recurrence of hepatitis B virus(HBV-)- related hepatocellular carcinomas(HCCs) after radical resection. Data and methods A total of 465 HBV-related HCC patients underwent radical resections between January 1, 2012 and August 31, 2018.Their data were collected through the inpatient information management system of the First Affiliated Hospital of University of Science and Technology of China. Survival and subgroup analyses of early recurrence among male and female patients were performed using Kaplan-Meier curves. The independent risk factors associated with early postoperative tumor recurrence were analyzed using multivariate Cox proportional hazards regression model. Based on these independent risk factors, a risk function model for early recurrence was fitted, and a column chart for the prediction model was drawn for internal and external validation. Results A total of 181 patients developed early recurrences, including 156 males and 25 females. There was no difference in the early recurrence rates between males and females. Tumor diameters>5cm, microvascular invasion and albumin level<35 g/L were independent risk factors for early recurrence. A nomogram for the early recurrence prediction model was drawn; the areas under the curve for the model and for external verification were 0.638 and 0.655, respectively. Conclusion Tumor diameter>5 cm, microvascular invasion, and albumin level<35 g/L were independent risk factors for early recurrence. The prediction model based on three clinical indicators could predict early recurrence, with good discrimination, calibration, and extrapolation.
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Affiliation(s)
- Yu Zhu
- Department of Hepatopancreatobiliary Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
- Division of Liver Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Department of Hepatopancreatobiliary Surgery, Enze Hospital of Taizhou, Taizhou, China
| | - Ai-Dong Wang
- Department of Hepatopancreatobiliary Surgery, Enze Hospital of Taizhou, Taizhou, China
| | - Ling-Ling Gu
- Department of Hepatopancreatobiliary Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Qi-Qiang Dai
- Department of Hepatopancreatobiliary Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Guo-Qun Zheng
- Department of Hepatopancreatobiliary Surgery, Enze Hospital of Taizhou, Taizhou, China
| | - Ting Chen
- Department of Hepatopancreatobiliary Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Chun-Long Wu
- Department of Hepatopancreatobiliary Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Wei-Dong Jia
- Division of Liver Surgery, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Fa-Biao Zhang
- Department of Hepatopancreatobiliary Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
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Du J, Huang Z. NLR stability predicts response to immune checkpoint inhibitors in advanced hepatocellular carcinoma. Sci Rep 2024; 14:19583. [PMID: 39179639 PMCID: PMC11344071 DOI: 10.1038/s41598-024-68048-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 07/18/2024] [Indexed: 08/26/2024] Open
Abstract
A high baseline NLR is associated with a poor prognosis of immunotherapy in patients with advanced HCC. As anti-tumour immune activation takes time, early dynamic changes in NLR may serve as a biomarker for predicting immunotherapy response. We conducted a retrospective study in which we enrolled 209 patients with aHCC who received ICIs (training cohort: N = 121, validation cohort: N = 88). In the training cohort, we categorized the patients based on the early changes in their NLR. Specifically, we defined patients as NLR Stable-Responder, NLR Responder and NLR Non-Responder. We compared the outcomes of these three patient groups using survival analysis. Additionally, we shortened the observation period to 6 weeks and validated the findings in the validation cohort. In the training cohort, early dynamic changes in NLR (HR 0.14, 95%CI 0.03-0.65, p = 0.012, HR 0.19, 95%CI 0.07-0.54, p = 0.002; HR 0.21, 95%CI 0.10-0.42, p < 0.001, HR 0.40, 95%CI 0.23-0.69, p = 0.001), PD-L1 < 1% (HR 5.36, 95%CI 1.12-25.66, p = 0.036; HR 2.98, 95%CI 1.51-5.91, p = 0.002) and MVI (HR 3.52, 95%CI 1.28-9.69, p = 0.015; HR 1.99, 95%CI 1.14-3.47, p = 0.015) were identified as independent predictors of OS and PFS. In the validation cohort, when the observation period was reduced to 6 weeks, early NLR changes still have predictive value. Early dynamic changes in NLR may be an easily defined, cost-effective, non-invasive biomarker to predict aHCC response to ICIs.
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Affiliation(s)
- Jiajia Du
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan, 430030, Hubei, China
| | - Zhiyong Huang
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan, 430030, Hubei, China.
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Huang H, Wu F, Yu Y, Xu B, Chen D, Huo Y, Li S. Multi-transcriptomics analysis of microvascular invasion-related malignant cells and development of a machine learning-based prognostic model in hepatocellular carcinoma. Front Immunol 2024; 15:1436131. [PMID: 39176099 PMCID: PMC11338809 DOI: 10.3389/fimmu.2024.1436131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 07/26/2024] [Indexed: 08/24/2024] Open
Abstract
Background Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable prognosis, early recurrence, and metastatic progression. However, the precise mechanistic underpinnings governing its onset and advancement remain elusive. Methods In this research, we downloaded bulk RNA-seq data from the TCGA and HCCDB repositories, single-cell RNA-seq data from the GEO database, and spatial transcriptomics data from the CNCB database. Leveraging the Scissor algorithm, we delineated prognosis-related cell subpopulations and discerned a distinct MVI-related malignant cell subtype. A comprehensive exploration of these malignant cell subpopulations was undertaken through pseudotime analysis and cell-cell communication scrutiny. Furthermore, we engineered a prognostic model grounded in MVI-related genes, employing 101 algorithm combinations integrated by 10 machine-learning algorithms on the TCGA training set. Rigorous evaluation ensued on internal testing sets and external validation sets, employing C-index, calibration curves, and decision curve analysis (DCA). Results Pseudotime analysis indicated that malignant cells, showing a positive correlation with MVI, were primarily concentrated in the early to middle stages of differentiation, correlating with an unfavorable prognosis. Importantly, these cells showed significant enrichment in the MYC pathway and were involved in extensive interactions with diverse cell types via the MIF signaling pathway. The association of malignant cells with the MVI phenotype was corroborated through validation in spatial transcriptomics data. The prognostic model we devised demonstrated exceptional sensitivity and specificity, surpassing the performance of most previously published models. Calibration curves and DCA underscored the clinical utility of this model. Conclusions Through integrated multi-transcriptomics analysis, we delineated MVI-related malignant cells and elucidated their biological functions. This study provided novel insights for managing HCC, with the constructed prognostic model offering valuable support for clinical decision-making.
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Affiliation(s)
| | | | | | | | | | | | - Shaoqiang Li
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
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27
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Fan W, Zhu B, Chen S, Wu Y, Zhao X, Qiao L, Huang Z, Tang R, Chen J, Lau WY, Chen M, Li J, Kuang M, Peng Z. Survival in Patients With Recurrent Intermediate-Stage Hepatocellular Carcinoma: Sorafenib Plus TACE vs TACE Alone Randomized Clinical Trial. JAMA Oncol 2024; 10:1047-1054. [PMID: 38900435 PMCID: PMC11190833 DOI: 10.1001/jamaoncol.2024.1831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/29/2023] [Indexed: 06/21/2024]
Abstract
Importance Transarterial chemoembolization (TACE) is commonly used to treat patients with recurrent intermediate-stage hepatocellular carcinoma (HCC) and positive microvascular invasion (MVI); however, TACE alone has demonstrated unsatisfactory survival benefits. A previous retrospective study suggested that TACE plus sorafenib (SOR-TACE) may be a better therapeutic option compared with TACE alone. Objective To investigate the clinical outcomes of SOR-TACE vs TACE alone for patients with recurrent intermediate-stage HCC after R0 hepatectomy with positive MVI. Design, Setting, and Participants In this phase 3, open-label, multicenter randomized clinical trial, patients with recurrent intermediate-stage HCC and positive MVI were randomly assigned in a 1:1 ratio via a computerized minimization technique to either SOR-TACE treatment or TACE alone. This trial was conducted at 5 hospitals in China, and enrolled patients from October 2019 to December 2021, with a follow-up period of 24 months. Data were analyzed from June 2023 to September 2023. Interventions Randomization to on-demand TACE (conventional TACE: doxorubicin, 50 mg, mixed with lipiodol and gelatin sponge particles [diameter: 150-350 μm]; drug-eluting bead TACE: doxorubicin, 75 mg, mixed with drug-eluting particles [diameter: 100-300 μm or 300-500 μm]) (TACE group) or sorafenib, 400 mg, twice daily plus on-demand TACE (SOR-TACE group) (conventional TACE: doxorubicin, 50 mg, mixed with lipiodol and gelatin sponge particles [diameter, 150-350 μm]; drug-eluting bead TACE: doxorubicin, 75 mg, mixed with drug-eluting particles [diameter: 100-300 μm or 300-500 μm]). Main Outcomes and Measures The primary end point was overall survival by intention-to-treat analysis. Safety was assessed in patients who received at least 1 dose of study treatment. Results A total of 162 patients (median [range] age, 55 [28-75] years; 151 males [93.2%]), were randomly assigned to be treated with either SOR-TACE (n = 81) or TACE alone (n = 81). The median overall survival was significantly longer in the SOR-TACE group than in the TACE group (22.2 months vs 15.1 months; hazard ratio [HR], 0.55; P < .001). SOR-TACE also prolonged progression-free survival (16.2 months vs 11.8 months; HR, 0.54; P < .001), and improved the objective response rate when compared with TACE alone based on the modified Response Evaluation Criteria in Solid Tumors criteria (80.2% vs 58.0%; P = .002). Any grade adverse events were more common in the SOR-TACE group, but all adverse events responded well to treatment. No unexpected adverse events or treatment-related deaths occurred in this study. Conclusions and Relevance The results of this randomized clinical trial demonstrated that SOR-TACE achieved better clinical outcomes than TACE alone. These findings suggest that combined treatment should be used for patients with recurrent intermediate-stage HCC after R0 hepatectomy with positive MVI. Trial Registration ClinicalTrials.gov Identifier: NCT04103398.
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Affiliation(s)
- Wenzhe Fan
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Bowen Zhu
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shuling Chen
- Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yanqin Wu
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiao Zhao
- Cancer Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Liangliang Qiao
- Department of Interventional Oncology, Jinshazhou Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhen Huang
- Department of Interventional Angiology, Huizhou First People’s Hospital, Huizhou, China
| | - Rong Tang
- Department of Hepatopancreatobiliary Surgery, Hainan General Hospital, Haikou, China
| | - Jinghua Chen
- Cancer Center, Guangzhou Twelfth People’s Hospital, Guangzhou, China
| | - Wan Yee Lau
- Faculty of Medicine, the Chinese University of Hong Kong, Prince of Wale Hospital, Shatin, New Territories, Hongkong, SAR, China
| | - Minshan Chen
- Department of Liver Surgery, Cancer Center of Sun Yat-sen University, Guangzhou, China
| | - Jiaping Li
- Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ming Kuang
- Center of Hepato-PancreatoBiliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhenwei Peng
- Cancer Center, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Wei G, Fang G, Guo P, Fang P, Wang T, Lin K, Liu J. Preoperative prediction of microvascular invasion risk in hepatocellular carcinoma with MRI: peritumoral versus tumor region. Insights Imaging 2024; 15:188. [PMID: 39090456 PMCID: PMC11294513 DOI: 10.1186/s13244-024-01760-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 06/23/2024] [Indexed: 08/04/2024] Open
Abstract
OBJECTIVES To explore the predictive performance of tumor and multiple peritumoral regions on dynamic contrast-enhanced magnetic resonance imaging (MRI), to identify optimal regions of interest for developing a preoperative predictive model for the grade of microvascular invasion (MVI). METHODS A total of 147 patients who were surgically diagnosed with hepatocellular carcinoma, and had a maximum tumor diameter ≤ 5 cm were recruited and subsequently divided into a training set (n = 117) and a testing set (n = 30) based on the date of surgery. We utilized a pre-trained AlexNet to extract deep learning features from seven different regions of the maximum transverse cross-section of tumors in various MRI sequence images. Subsequently, an extreme gradient boosting (XGBoost) classifier was employed to construct the MVI grade prediction model, with evaluation based on the area under the curve (AUC). RESULTS The XGBoost classifier trained with data from the 20-mm peritumoral region showed superior AUC compared to the tumor region alone. AUC values consistently increased when utilizing data from 5-mm, 10-mm, and 20-mm peritumoral regions. Combining arterial and delayed-phase data yielded the highest predictive performance, with micro- and macro-average AUCs of 0.78 and 0.74, respectively. Integration of clinical data further improved AUCs values to 0.83 and 0.80. CONCLUSION Compared with those of the tumor region, the deep learning features of the peritumoral region provide more important information for predicting the grade of MVI. Combining the tumor region and the 20-mm peritumoral region resulted in a relatively ideal and accurate region within which the grade of MVI can be predicted. CLINICAL RELEVANCE STATEMENT The 20-mm peritumoral region holds more significance than the tumor region in predicting MVI grade. Deep learning features can indirectly predict MVI by extracting information from the tumor region and directly capturing MVI information from the peritumoral region. KEY POINTS We investigated tumor and different peritumoral regions, as well as their fusion. MVI predominantly occurs in the peritumoral region, a superior predictor compared to the tumor region. The peritumoral 20 mm region is reasonable for accurately predicting the three-grade MVI.
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Affiliation(s)
- Guangya Wei
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Guoxu Fang
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Pengfei Guo
- Southeast Big Data Institute of Hepatobiliary Health, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Peng Fang
- Department of Radiology, Henan Province Hospital of TCM, Zhengzhou, China
| | - Tongming Wang
- Department of Radiology, Henan Province Hospital of TCM, Zhengzhou, China
| | - Kecan Lin
- Department of Hepatopancreatobiliary Surgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Jingfeng Liu
- Department of Hepatopancreatobiliary Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China.
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Schmidt R, Hamm CA, Rueger C, Xu H, He Y, Gottwald LA, Gebauer B, Savic LJ. Decision-Tree Models Indicative of Microvascular Invasion on MRI Predict Survival in Patients with Hepatocellular Carcinoma Following Tumor Ablation. J Hepatocell Carcinoma 2024; 11:1279-1293. [PMID: 38974016 PMCID: PMC11227855 DOI: 10.2147/jhc.s454487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 04/18/2024] [Indexed: 07/09/2024] Open
Abstract
Purpose Histological microvascular invasion (MVI) is a risk factor for poor survival and early recurrence in hepatocellular carcinoma (HCC) after surgery. Its prognostic value in the setting of locoregional therapies (LRT), where no tissue samples are obtained, remains unknown. This study aims to establish CT-derived indices indicative of MVI on liver MRI with superior soft tissue contrast and evaluate their association with patient survival after ablation via interstitial brachytherapy (iBT) versus iBT combined with prior conventional transarterial chemoembolization (cTACE). Patients and Methods Ninety-five consecutive patients, who underwent ablation via iBT alone (n = 47) or combined with cTACE (n = 48), were retrospectively included between 01/2016 and 12/2017. All patients received contrast-enhanced MRI prior to LRT. Overall (OS), progression-free survival (PFS), and time-to-progression (TTP) were assessed. Decision-tree models to determine Radiogenomic Venous Invasion (RVI) and Two-Trait Predictor of Venous Invasion (TTPVI) on baseline MRI were established, validated on an external test set (TCGA-LIHC), and applied in the study cohorts to investigate their prognostic value for patient survival. Statistics included Fisher's exact and t-test, Kaplan-Meier and cox-regression analysis, area under the receiver operating characteristic curve (AUC-ROC) and Pearson's correlation. Results OS, PFS, and TTP were similar in both treatment groups. In the external dataset, RVI showed low sensitivity but relatively high specificity (AUC-ROC = 0.53), and TTPVI high sensitivity but only low specificity (AUC-ROC = 0.61) for histological MVI. In patients following iBT alone, positive RVI and TTPVI traits were associated with poorer OS (RVI: p < 0.01; TTPVI: p = 0.08), PFS (p = 0.04; p = 0.04), and TTP (p = 0.14; p = 0.03), respectively. However, when patients with combined cTACE and iBT were stratified by RVI or TTPVI, no differences in OS (p = 0.75; p = 0.55), PFS (p = 0.70; p = 0.43), or TTP (p = 0.33; p = 0.27) were observed. Conclusion The study underscores the role of non-invasive imaging biomarkers indicative of MVI to identify patients, who would potentially benefit from embolotherapy via cTACE prior to ablation rather than ablation alone.
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Affiliation(s)
- Robin Schmidt
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, 13353, Germany
- Experimental Clinical Research Center (ECRC) at Charité - Universitätsmedizin Berlin and Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin, 13125, Germany
| | - Charlie Alexander Hamm
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, 13353, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, 10117, Germany
| | - Christopher Rueger
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, 13353, Germany
| | - Han Xu
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, 13353, Germany
| | - Yubei He
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, 13353, Germany
- Experimental Clinical Research Center (ECRC) at Charité - Universitätsmedizin Berlin and Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin, 13125, Germany
| | | | - Bernhard Gebauer
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, 13353, Germany
| | - Lynn Jeanette Savic
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiology, Berlin, 13353, Germany
- Experimental Clinical Research Center (ECRC) at Charité - Universitätsmedizin Berlin and Max-Delbrück-Centrum für Molekulare Medizin (MDC), Berlin, 13125, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, 10117, Germany
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Huang XW, Li Y, Jiang LN, Zhao BK, Liu YS, Chen C, Zhao D, Zhang XL, Li ML, Jiang YY, Liu SH, Zhu L, Zhao JM. Nomogram for preoperative estimation of microvascular invasion risk in hepatocellular carcinoma. Transl Oncol 2024; 45:101986. [PMID: 38723299 PMCID: PMC11101742 DOI: 10.1016/j.tranon.2024.101986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 04/22/2024] [Accepted: 05/05/2024] [Indexed: 05/21/2024] Open
Abstract
Microvascular invasion (MVI) is an adverse prognostic indicator of tumor recurrence after surgery for hepatocellular carcinoma (HCC). Therefore, developing a nomogram for estimating the presence of MVI before liver resection is necessary. We retrospectively included 260 patients with pathologically confirmed HCC at the Fifth Medical Center of Chinese PLA General Hospital between January 2021 and April 2024. The patients were randomly divided into a training cohort (n = 182) for nomogram development, and a validation cohort (n = 78) to confirm the performance of the model (7:3 ratio). Significant clinical variables associated with MVI were then incorporated into the predictive nomogram using both univariate and multivariate logistic analyses. The predictive performance of the nomogram was assessed based on its discrimination, calibration, and clinical utility. Serum carnosine dipeptidase 1 ([CNDP1] OR 2.973; 95 % CI 1.167-7.575; p = 0.022), cirrhosis (OR 8.911; 95 % CI 1.922-41.318; p = 0.005), multiple tumors (OR 4.095; 95 % CI 1.374-12.205; p = 0.011), and tumor diameter ≥3 cm (OR 4.408; 95 % CI 1.780-10.919; p = 0.001) were independent predictors of MVI. Performance of the nomogram based on serum CNDP1, cirrhosis, number of tumors and tumor diameter was achieved with a concordance index of 0.833 (95 % CI 0.771-0.894) and 0.821 (95 % CI 0.720-0.922) in the training and validation cohorts, respectively. It fitted well in the calibration curves, and the decision curve analysis further confirmed its clinical usefulness. The nomogram, incorporating significant clinical variables and imaging features, successfully predicted the personalized risk of MVI in HCC preoperatively.
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Affiliation(s)
- Xiao-Wen Huang
- Medical School of Chinese PLA, Beijing, China; Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yan Li
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Li-Na Jiang
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Bo-Kang Zhao
- Department of Hepatology, Center of Infectious Diseases and Pathogen Biology, The First Hospital of Jilin University, Changchun, China
| | - Yi-Si Liu
- First Department of Liver Disease Center, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Chun Chen
- Senior Department of Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Dan Zhao
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xue-Li Zhang
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Mei-Ling Li
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yi-Yun Jiang
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Shu-Hong Liu
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Li Zhu
- Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jing-Min Zhao
- Medical School of Chinese PLA, Beijing, China; Department of Pathology and Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
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Jiang S, Zhu G, Tan Y, Zhou T, Zheng S, Wang F, Lei W, Liu X, Du J, Tian M. Identification of VEGFs-related gene signature for predicting microangiogenesis and hepatocellular carcinoma prognosis. Aging (Albany NY) 2024; 16:10321-10347. [PMID: 38874512 PMCID: PMC11236318 DOI: 10.18632/aging.205931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 04/08/2024] [Indexed: 06/15/2024]
Abstract
Microangiogenesis is an important prognostic factor in various cancers, including hepatocellular carcinoma (HCC). The Vascular Endothelial Growth Factor (VEGF) has been shown to contribute to tumor angiogenesis. Recently, several studies have investigated the regulation of VEGF production by a single gene, with few researchers exploring all genes that affect VEGF production. In this study, we comprehensively analyzed all genes affecting VEGF production in HCC and developed a risk model and gene-based risk score based on VEGF production. Moreover, the model's predictive capacity on prognosis of HCCs was verified using training and validation datasets. The developed model showed good prediction of the overall survival rate. Patients with a higher risk score experienced poor outcomes compared to those with a lower risk score. Furthermore, we identified the immunological causes of the poor prognosis of patients with high-risk scores comparing with those with low-risk scores.
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Affiliation(s)
- Shengpan Jiang
- Department of Interventional Medicine, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, Hubei Province, China
| | - Guoting Zhu
- Department of Neurosurgery, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yiqing Tan
- Department of Interventional Medicine, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, Hubei Province, China
| | - Tao Zhou
- Department of Interventional Medicine, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, Hubei Province, China
| | - Shilin Zheng
- Department of Interventional Medicine, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, Hubei Province, China
| | - Fuhua Wang
- Department of Interventional Medicine, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, Hubei Province, China
| | - Wenfeng Lei
- Department of Interventional Medicine, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, Hubei Province, China
| | - Xuan Liu
- Department of Interventional Medicine, Wuhan Third Hospital (Tongren Hospital of Wuhan University), Wuhan, Hubei Province, China
| | - Jinjun Du
- Department of Hepatology and Gastroenterology, Wuhan Hospital of Traditional Chinese Medicine (The Third Clinical College of Hubei University of Chinese Medicine), Wuhan, Hubei Province, China
| | - Manman Tian
- Department of Hepatology and Gastroenterology, Wuhan Hospital of Traditional Chinese Medicine (The Third Clinical College of Hubei University of Chinese Medicine), Wuhan, Hubei Province, China
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Yang W, Li B, Wei Y, Liu F. ASO Author Reflections: Laparoscopic Anatomic Sectionectomy: Resection Area Selection and Delineation of Resection Boundary. Ann Surg Oncol 2024; 31:4048-4049. [PMID: 38277037 DOI: 10.1245/s10434-024-14966-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 01/27/2024]
Affiliation(s)
- Wugui Yang
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Bo Li
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yonggang Wei
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Fei Liu
- Division of Liver Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China.
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Xie Q, Zhao Z, Yang Y, Wang X, Wu W, Jiang H, Hao W, Peng R, Luo C. A clinical-radiomic-pathomic model for prognosis prediction in patients with hepatocellular carcinoma after radical resection. Cancer Med 2024; 13:e7374. [PMID: 38864473 PMCID: PMC11167608 DOI: 10.1002/cam4.7374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 04/21/2024] [Accepted: 05/28/2024] [Indexed: 06/13/2024] Open
Abstract
PURPOSE Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence. MATERIALS AND METHODS We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5-fold cross-validation to assess the performance and predictive power of the comparative models. RESULTS Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence. CONCLUSIONS The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies.
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Affiliation(s)
- Qu Xie
- Department of Hepato‐Pancreato‐Biliary & Gastric Medical OncologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
- Wenzhou Medical UniversityWenzhouZhejiangChina
| | - Zeyin Zhao
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan UniversityChangshaHunanChina
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Yanzhen Yang
- Department of Hepato‐Pancreato‐Biliary & Gastric Medical OncologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
- Wenzhou Medical UniversityWenzhouZhejiangChina
| | - Xiaohong Wang
- Department of Intestinal OncologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Wei Wu
- Department of PathologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Haitao Jiang
- Department of RadiologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Weiyuan Hao
- Department of InterventionZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Ruizi Peng
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
| | - Cong Luo
- Department of Hepato‐Pancreato‐Biliary & Gastric Medical OncologyZhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of SciencesHangzhouZhejiangChina
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Tang M, Zhang S, Yang M, Feng R, Lin J, Chen X, Xu Y, Yu R, Liao X, Li Z, Li X, Li M, Zhang Q, Chen S, Qian W, Liu Y, Song L, Li J. Infiltrative Vessel Co-optive Growth Pattern Induced by IQGAP3 Overexpression Promotes Microvascular Invasion in Hepatocellular Carcinoma. Clin Cancer Res 2024; 30:2206-2224. [PMID: 38470497 DOI: 10.1158/1078-0432.ccr-23-2933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/26/2023] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
PURPOSE Microvascular invasion (MVI) is a major unfavorable prognostic factor for intrahepatic metastasis and postoperative recurrence of hepatocellular carcinoma (HCC). However, the intervention and preoperative prediction for MVI remain clinical challenges due to the absent precise mechanism and molecular marker(s). Herein, we aimed to investigate the mechanisms underlying vascular invasion that can be applied to clinical intervention for MVI in HCC. EXPERIMENTAL DESIGN The histopathologic characteristics of clinical MVI+/HCC specimens were analyzed using multiplex immunofluorescence staining. The liver orthotopic xenograft mouse model and mechanistic experiments on human patient-derived HCC cell lines, including coculture modeling, RNA-sequencing, and proteomic analysis, were used to investigate MVI-related genes and mechanisms. RESULTS IQGAP3 overexpression was correlated significantly with MVI status and reduced survival in HCC. Upregulation of IQGAP3 promoted MVI+-HCC cells to adopt an infiltrative vessel co-optive growth pattern and accessed blood capillaries by inducing detachment of activated hepatic stellate cells (HSC) from the endothelium. Mechanically, IQGAP3 overexpression contributed to HCC vascular invasion via a dual mechanism, in which IQGAP3 induced HSC activation and disruption of the HSC-endothelial interaction via upregulation of multiple cytokines and enhanced the trans-endothelial migration of MVI+-HCC cells by remodeling the cytoskeleton by sustaining GTPase Rac1 activity. Importantly, systemic delivery of IQGAP3-targeting small-interfering RNA nanoparticles disrupted the infiltrative vessel co-optive growth pattern and reduced the MVI of HCC. CONCLUSIONS Our results revealed a plausible mechanism underlying IQGAP3-mediated microvascular invasion in HCC, and provided a potential target to develop therapeutic strategies to treat HCC with MVI.
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Affiliation(s)
- Miaoling Tang
- Department of Oncology, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Shuxia Zhang
- Department of Oncology, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Meisongzhu Yang
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Rongni Feng
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jinbin Lin
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xiaohong Chen
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yingru Xu
- Molecular Diagnosis and Gene Testing Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Ruyuan Yu
- Molecular Diagnosis and Gene Testing Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xinyi Liao
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Ziwen Li
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xincheng Li
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Man Li
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Qiliang Zhang
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Suwen Chen
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Wanying Qian
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yuanji Liu
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Libing Song
- State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jun Li
- Department of Oncology, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Department of Biochemistry, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
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Teufel A, Kudo M, Qian Y, Daza J, Rodriguez I, Reissfelder C, Ridruejo E, Ebert MP. Current Trends and Advancements in the Management of Hepatocellular Carcinoma. Dig Dis 2024; 42:349-360. [PMID: 38599204 DOI: 10.1159/000538815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/08/2024] [Indexed: 04/12/2024]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) remains a significant global health burden with a high mortality rate. Over the past 40 years, significant progress has been achieved in the prevention and management of HCC. SUMMARY Hepatitis B vaccination programs, the development of direct acting antiviral drugs for Hepatitis C, and effective surveillance strategies provide a profound basis for the prevention of HCC. Advanced surgery and liver transplantation along with local ablation techniques potentially offer cure for the disease. Also, just recently, the introduction of immunotherapy opened a new chapter in systemic treatment. Finally, the introduction of the BCLC classification system for HCC, clearly defining patient groups and assigning reasonable treatment options, has standardized treatment and become the basis of almost all clinical trials for HCC. With this review, we provide a comprehensive overview of the evolving landscape of HCC management and also touch on current challenges. KEY MESSAGE A comprehensive and multidisciplinary approach is crucial for effective HCC management. Continued research and clinical trials are imperative to further enhance treatment options and will ultimately reduce the global burden of this devastating disease.
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Affiliation(s)
- Andreas Teufel
- Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health (CPD), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Yuquan Qian
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jimmy Daza
- Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health (CPD), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Isaac Rodriguez
- Division of Hepatology, Division of Clinical Bioinformatics, Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Clinical Cooperation Unit Healthy Metabolism, Center for Preventive Medicine and Digital Health (CPD), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Christoph Reissfelder
- Department of Surgery, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- DKFZ-Hector Cancer Institute, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ezequiel Ridruejo
- Hepatology Section, Department of Medicine, Center for Medical Education and Clinical Research, Buenos Aires, Argentina
| | - Matthias P Ebert
- DKFZ-Hector Cancer Institute, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Molecular Medicine Partnership Unit, EMBL, Heidelberg, Germany
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Liu Y, Zhang Z, Zhang H, Wang X, Wang K, Yang R, Han P, Luan K, Zhou Y. Clinical prediction of microvascular invasion in hepatocellular carcinoma using an MRI-based graph convolutional network model integrated with nomogram. Br J Radiol 2024; 97:938-946. [PMID: 38552308 PMCID: PMC11075980 DOI: 10.1093/bjr/tqae056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 02/07/2024] [Accepted: 03/06/2024] [Indexed: 05/09/2024] Open
Abstract
OBJECTIVES Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram. METHODS We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram. RESULTS 27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P = 0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoural artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927). CONCLUSIONS The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options. ADVANCES IN KNOWLEDGE GCN based on MRI could predict MVI on HCC.
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Affiliation(s)
- Yang Liu
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150010, Heilongjiang, China
| | - Ziqian Zhang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150010, Heilongjiang, China
| | - Hongxia Zhang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150010, Heilongjiang, China
| | - Xinxin Wang
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150010, Heilongjiang, China
| | - Kun Wang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Rui Yang
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Nangang District, Harbin 150081, Heilongjiang Province, China
| | - Peng Han
- Department of Surgical Oncology, Harbin Medical University Cancer Hospital, No.150 Haping Road, Nangang District, Harbin 150081, Heilongjiang Province, China
| | - Kuan Luan
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Yang Zhou
- Department of Radiology, Harbin Medical University Cancer Hospital, Harbin 150010, Heilongjiang, China
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Lei Y, Feng B, Wan M, Xu K, Cui J, Ma C, Sun J, Yao C, Gan S, Shi J, Cui E. Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model. Abdom Radiol (NY) 2024; 49:1397-1410. [PMID: 38433144 DOI: 10.1007/s00261-024-04202-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 01/04/2024] [Accepted: 01/12/2024] [Indexed: 03/05/2024]
Abstract
PURPOSE To investigate the value of a multimodal deep learning (MDL) model based on computed tomography (CT) and magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHODS A total of 287 patients with HCC from our institution and 58 patients from another individual institution were included. Among these, 119 patients with only CT data and 116 patients with only MRI data were selected for single-modality deep learning model development, after which select parameters were migrated for MDL model development with transfer learning (TL). In addition, 110 patients with simultaneous CT and MRI data were divided into a training cohort (n = 66) and a validation cohort (n = 44). We input the features extracted from DenseNet121 into an extreme learning machine (ELM) classifier to construct a classification model. RESULTS The area under the curve (AUC) of the MDL model was 0.844, which was superior to that of the single-phase CT (AUC = 0.706-0.776, P < 0.05), single-sequence MRI (AUC = 0.706-0.717, P < 0.05), single-modality DL model (AUCall-phase CT = 0.722, AUCall-sequence MRI = 0.731; P < 0.05), clinical (AUC = 0.648, P < 0.05), but not to that of the delay phase (DP) and in-phase (IP) MRI and portal venous phase (PVP) CT models. The MDL model achieved better performance than models described above (P < 0.05). When combined with clinical features, the AUC of the MDL model increased from 0.844 to 0.871. A nomogram, combining deep learning signatures (DLS) and clinical indicators for MDL models, demonstrated a greater overall net gain than the MDL models (P < 0.05). CONCLUSION The MDL model is a valuable noninvasive technique for preoperatively predicting MVI in HCC.
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Affiliation(s)
- Yan Lei
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
- Zunyi Medical University, 1 Xiaoyuan Road, Zunyi, People's Republic of China
| | - Bao Feng
- Laboratory of Intelligent Detection and Information Processing, School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, People's Republic of China
| | - Meiqi Wan
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
- Zunyi Medical University, 1 Xiaoyuan Road, Zunyi, People's Republic of China
| | - Kuncai Xu
- Laboratory of Intelligent Detection and Information Processing, School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, People's Republic of China
| | - Jin Cui
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
| | - Changyi Ma
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
| | - Junqi Sun
- Department of Radiology, Yuebei People's Hospital, 133 Huimin Street, Shaoguan, People's Republic of China
| | - Changyin Yao
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
- Guangdong Medical University, 2 Wenming East Road, Zhanjiang, People's Republic of China
| | - Shiman Gan
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China
- Guangdong Medical University, 2 Wenming East Road, Zhanjiang, People's Republic of China
| | - Jiangfeng Shi
- Laboratory of Intelligent Detection and Information Processing, School of Electronic Information and Automation, Guilin University of Aerospace Technology, 2 Jinji Road, Guilin, People's Republic of China
| | - Enming Cui
- Department of Radiology, Jiangmen Central Hospital, 23 Beijie Haibang Street, Jiangmen, People's Republic of China.
- Zunyi Medical University, 1 Xiaoyuan Road, Zunyi, People's Republic of China.
- Guangdong Medical University, 2 Wenming East Road, Zhanjiang, People's Republic of China.
- Jiangmen Key Laboratory of Artificial Intelligence in Medical Image Computation and Application, 23 Beijie Haibang Street, Jiangmen, People's Republic of China.
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Ma Z, Xiao Z, Yin P, Wen K, Wang W, Yan Y, Lin Z, Li Z, Wang H, Zhang J, Mao K. Comparison of survival benefit and safety between surgery following conversion therapy versus surgery alone in patients with surgically resectable hepatocellular carcinoma at CNLC IIb/IIIa stage: a propensity score matching study. Int J Surg 2024; 110:2910-2921. [PMID: 38353702 DOI: 10.1097/js9.0000000000001193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 01/31/2024] [Indexed: 05/16/2024]
Abstract
OBJECTIVE The objective of this study is to evaluate and compare the survival benefit and safety of surgery following conversion therapy versus surgery alone in patients diagnosed with surgically resectable hepatocellular carcinoma (HCC) at China Liver Cancer Staging (CNLC) IIb/IIIa stage. METHODS A total of 95 patients diagnosed with surgically resectable CNLC IIb/IIIa HCC were retrospectively enrolled in our study from November 2018 to December 2022. Among them, 30 patients underwent conversion therapy followed by hepatectomy, while the remaining 65 received surgery alone. The primary endpoint was recurrence-free survival (RFS). Propensity score matching was employed to minimize bias in the retrospective analysis. RESULTS Compared to the surgery alone group, the conversion therapy group demonstrated a significantly prolonged median RFS (17.1 vs. 7.0 months; P =0.014), a reduced incidence of microvascular invasion (MVI, 23.3 vs. 81.5%; P <0.001), and a comparable rate of achieving Textbook Outcome in Liver Surgery (TOLS, 83.3 vs. 76.9%; P =0.476). Multivariate analysis indicated that conversion therapy was independently associated with improved RFS after hepatectomy (HR=0.511, P =0.027). The same conclusions were obtained after propensity score matching. CONCLUSIONS The findings of our study offer preliminary evidence that preoperative conversion therapy significantly prolongs RFS in patients with surgically resectable HCC at CNLC IIb/IIIa stage. Furthermore, combining conversion therapy and hepatectomy represents a relatively safe treatment strategy.
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Affiliation(s)
- Zifeng Ma
- Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Zhiyu Xiao
- Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Pengfei Yin
- Department of Hepatopancreatobiliary Surgery, Chongqing General Hospital, Chongqing
| | - Kai Wen
- Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Weidong Wang
- Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Yongcong Yan
- Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Zijian Lin
- Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Zonglin Li
- Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Haikuo Wang
- Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Jianlong Zhang
- Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
| | - Kai Mao
- Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou
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Zheng L, Wang Y, Liu Z, Wang Z, Tao C, Wu A, Li H, Xiao T, Li Z, Rong W. Identification of molecular characteristics of hepatocellular carcinoma with microvascular invasion based on deep targeted sequencing. Cancer Med 2024; 13:e7043. [PMID: 38572921 PMCID: PMC10993708 DOI: 10.1002/cam4.7043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 01/29/2024] [Accepted: 02/13/2024] [Indexed: 04/05/2024] Open
Abstract
BACKGROUND As an indicator of tumor invasiveness, microvascular invasion (MVI) is a crucial risk factor for postoperative relapse, metastasis, and unfavorable prognosis in hepatocellular carcinoma (HCC). Nevertheless, the genetic mechanisms underlying MVI, particularly for Chinese patients, remain mostly uncharted. METHODS We applied deep targeted sequencing on 66 Chinese HCC samples. Focusing on the telomerase reverse transcriptase (TERT) promoter (TERTp) and TP53 co-mutation (TERTp+/TP53+) group, gene set enrichment analysis (GSEA) was used to explore the potential molecular mechanisms of the TERTp+/TP53+ group on tumor progression and metastasis. Additionally, we evaluated the tumor immune microenvironment of the TERTp+/TP53+ group in HCC using multiplex immunofluorescence (mIF) staining. RESULTS Among the 66 HCC samples, the mutated genes that mostly appeared were TERT, TP53, and CTNNB1. Of note, we found 10 cases with TERTp+/TP53+, of which nine were MVI-positive and one was MVI-negative, and there was a co-occurrence of TERTp and TP53 (p < 0.05). Survival analysis demonstrated that patients with the TERTp+/TP53+ group had lower the disease-free survival (DFS) (p = 0.028). GSEA results indicated that telomere organization, telomere maintenance, DNA replication, positive regulation of cell cycle, and negative regulation of immune response were significantly enriched in the TERTp+/TP53+ group (all adjusted p-values (p.adj) < 0.05). mIF revealed that the TERTp+/TP53+ group decreased CD8+ T cells infiltration (p = 0.25) and enhanced PDL1 expression (p = 0.55). CONCLUSIONS TERTp+/TP53+ was significantly enriched in MVI-positive patients, leading to poor prognosis for HCC patients by promoting proliferation of HCC cell and inhibiting infiltration of immune cell surrounding HCC. TERTp+/TP53+ can be utilized as a potential indicator for predicting MVI-positive patients and poor prognosis, laying a preliminary foundation for further exploration of co-mutation in HCC with MVI and clinical treatment.
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Affiliation(s)
- Linlin Zheng
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Yaru Wang
- State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhenrong Liu
- State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhihao Wang
- Department of Hepatobiliary Hernia SurgeryLiaocheng Dongcangfu People's HospitalLiaochengChina
| | - Changcheng Tao
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Anke Wu
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Haiyang Li
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Ting Xiao
- State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhuo Li
- Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Weiqi Rong
- Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
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Li H, Lu D, Chen J, Zhang J, Zhuo J, Lin Z, Cao C, Shen W, He C, Chen H, Hu Z, Sun Y, Wei X, Zhuang L, Zheng S, Xu X. Post-transplant hepatitis B virus reactivation impacts the prognosis of patients with hepatitis B-related hepatocellular carcinoma: a dual-centre retrospective cohort study in China. Int J Surg 2024; 110:2263-2274. [PMID: 38348848 PMCID: PMC11019990 DOI: 10.1097/js9.0000000000001141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/25/2024] [Indexed: 04/18/2024]
Abstract
BACKGROUND Highly active hepatitis B virus (HBV) is known to be associated with poor outcomes in patients with hepatocellular carcinoma (HCC). This study aims to investigate the relationship between HBV status and HCC recurrence after liver transplantation. METHODS The study retrospectively analyzed HCC patients undergoing liver transplantation in two centres between January 2015 and December 2020. The authors reviewed post-transplant HBV status and its association with outcomes. RESULTS The prognosis of recipients with hepatitis B surface antigen (HBsAg) reappearance ( n =58) was poorer than those with HBsAg persistent negative ( n =351) and positive ( n =53). In HBsAg persistent positive group, recipients with HBV DNA reappearance or greater than 10-fold increase above baseline had worse outcomes than those without ( P <0.01). HBV reactivation was defined as (a) HBsAg reappearance or (b) HBV DNA reappearance or greater than 10-fold increase above baseline. After propensity score matching, the 5-year overall survival rate and recurrence-free survival rate after liver transplantation in recipients with HBV reactivation were significantly lower than those without (32.0% vs. 62.3%; P <0.01, and 16.4% vs. 63.1%; P <0.01, respectively). Moreover, HBV reactivation was significantly related to post-transplant HCC recurrence, especially lung metastasis. Cox regression analysis revealed that beyond Milan criteria, microvascular invasion and HBsAg-positive graft were independent risk factors for post-transplant HBV reactivation, and a novel nomogram was established accordingly with a good predictive efficacy (area under the time-dependent receiver operating characteristic curve=0.78, C-index =0.73). CONCLUSIONS Recipients with HBV reactivation had worse outcomes and higher tumour recurrence rates than those without. The nomogram could be used to evaluate the risk of post-transplant HBV reactivation effectively.
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Affiliation(s)
- Huigang Li
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | - Di Lu
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | - Jinyan Chen
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | | | - Jianyong Zhuo
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | - Zuyuan Lin
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | - Chenghao Cao
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | - Wei Shen
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | - Chiyu He
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | - Hao Chen
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | - Zhihang Hu
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | - Yiyang Sun
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou
| | - Xuyong Wei
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
| | - Li Zhuang
- The Fourth School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou
| | - Shusen Zheng
- Department of Hepatobiliary and Pancreatic Surgery, Shulan (Hangzhou) Hospital, Hangzhou
- Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou
- National Center for Healthcare Quality Management in Liver Transplant, Hangzhou China
| | - Xiao Xu
- Zhejiang University School of Medicine
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Hangzhou
- National Center for Healthcare Quality Management in Liver Transplant, Hangzhou China
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Liu Y, Sun S, Chu Z, Liu C, Chen L, Ruan Z. Comparison of outcomes between preoperative and postoperative systemic treatment in patients with hepatocellular carcinoma: a SEER database-based study. Front Oncol 2024; 14:1324392. [PMID: 38567153 PMCID: PMC10985153 DOI: 10.3389/fonc.2024.1324392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/16/2024] [Indexed: 04/04/2024] Open
Abstract
Background Significant advancements in systemic treatment for hepatocellular carcinoma have been made in recent years. However, the optimal timing of systemic treatment before or after surgery remains unknown. This study aims to evaluate the impact of sequencing systemic treatment and surgical intervention on the long-term prognosis of hepatocellular carcinoma patients. Methods In our study, we analyzed data from patients diagnosed with primary liver cancer (2004-2015) extracted from the SEER database. Patients who underwent both systemic treatment and surgical intervention were selected, divided into preoperative and postoperative systemic therapy groups. The primary endpoint of the study is overall survival(OS), and the secondary endpoint is cancer-specific survival (CSS). Propensity score matching (PSM) reduced the influence of confounding factors, while Kaplan-Meier curves and a multivariable Cox proportional hazards model accounted for variables during survival analysis. Results A total of 1918 eligible HCC patients were included, with 1406 cases in the preoperative systemic treatment group and 512 cases in the postoperative systemic treatment group. Survival analysis showed that both the preoperative group demonstrated longer median overall survival (OS) and median cancer-specific survival (CSS) before and after PSM. After conducting multivariate COX regression analysis with stepwise adjustment of input variables, the postoperative systemic treatment group continued to exhibit a higher risk of all-cause mortality (HR: 1.84, 95% CI: 1.55-2.1) and cancer-specific mortality (HR: 2.10, 95% CI: 1.73-2.54). Subgroup analysis indicated consistent results for overall survival (OS) across different subgroups. Conclusions Hepatocellular carcinoma patients from the SEER database who received preoperative systemic therapy had superior OS and CSS compared to those who received postoperative systemic therapy.
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Affiliation(s)
- Yadi Liu
- Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Shuangshuang Sun
- Department of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Zhaoyin Chu
- Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Caixia Liu
- Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lina Chen
- Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhengshang Ruan
- Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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He X, Xu Y, Zhou C, Song R, Liu Y, Zhang H, Wang Y, Fan Q, Wang D, Chen W, Wang J, Guo D. Prediction of microvascular invasion and pathological differentiation of hepatocellular carcinoma based on a deep learning model. Eur J Radiol 2024; 172:111348. [PMID: 38325190 DOI: 10.1016/j.ejrad.2024.111348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/12/2024] [Accepted: 01/25/2024] [Indexed: 02/09/2024]
Abstract
PURPOSE To develop a deep learning (DL) model based on preoperative contrast-enhanced computed tomography (CECT) images to predict microvascular invasion (MVI) and pathological differentiation of hepatocellular carcinoma (HCC). METHODS This retrospective study included 640 consecutive patients who underwent surgical resection and were pathologically diagnosed with HCC at two medical institutions from April 2017 to May 2022. CECT images and relevant clinical parameters were collected. All the data were divided into 368 training sets, 138 test sets and 134 validation sets. Through DL, a segmentation model was used to obtain a region of interest (ROI) of the liver, and a classification model was established to predict the pathological status of HCC. RESULTS The liver segmentation model based on the 3D U-Network had a mean intersection over union (mIoU) score of 0.9120 and a Dice score of 0.9473. Among all the classification prediction models based on the Swin transformer, the fusion models combining image information and clinical parameters exhibited the best performance. The area under the curve (AUC) of the fusion model for predicting the MVI status was 0.941, its accuracy was 0.917, and its specificity was 0.908. The AUC values of the fusion model for predicting poorly differentiated, moderately differentiated and highly differentiated HCC based on the test set were 0.962, 0.957 and 0.996, respectively. CONCLUSION The established DL models established can be used to noninvasively and effectively predict the MVI status and the degree of pathological differentiation of HCC, and aid in clinical diagnosis and treatment.
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Affiliation(s)
- Xiaojuan He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
| | - Yang Xu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
| | - Chaoyang Zhou
- Department of Radiology, The First Affiliated Hospital of Army Military Medical University, Chongqing 400038, PR China.
| | - Rao Song
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
| | - Yangyang Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
| | - Haiping Zhang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
| | - Yudong Wang
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, PR China.
| | - Qianrui Fan
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, PR China.
| | - Dawei Wang
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, PR China.
| | - Weidao Chen
- Institute of Research, InferVision, Ocean International Center, Chaoyang District, Beijing 100025, PR China.
| | - Jian Wang
- Department of Radiology, The First Affiliated Hospital of Army Military Medical University, Chongqing 400038, PR China.
| | - Dajing Guo
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, PR China.
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Barat M, Pellat A, Hoeffel C, Dohan A, Coriat R, Fishman EK, Nougaret S, Chu L, Soyer P. CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence. Jpn J Radiol 2024; 42:246-260. [PMID: 37926780 DOI: 10.1007/s11604-023-01504-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/12/2023] [Indexed: 11/07/2023]
Abstract
Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
| | - Anna Pellat
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Christine Hoeffel
- Department of Radiology, Hopital Robert Debré, CHU Reims, Université Champagne-Ardennes, 51092, Reims, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
| | - Romain Coriat
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Stéphanie Nougaret
- Department of Radiology, Montpellier Cancer Institute, 34000, Montpellier, France
- PINKCC Lab, IRCM, U1194, 34000, Montpellier, France
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France.
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France.
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Zhang X, Yu X, Liang W, Zhang Z, Zhang S, Xu L, Zhang H, Feng Z, Song M, Zhang J, Feng S. Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images. Cancer Med 2024; 13:e7104. [PMID: 38488408 PMCID: PMC10941532 DOI: 10.1002/cam4.7104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/13/2023] [Accepted: 03/03/2024] [Indexed: 03/18/2024] Open
Abstract
BACKGROUND Microvascular invasion (MVI) is an independent prognostic factor that is associated with early recurrence and poor survival after resection of hepatocellular carcinoma (HCC). However, the traditional pathology approach is relatively subjective, time-consuming, and heterogeneous in the diagnosis of MVI. The aim of this study was to develop a deep-learning model that could significantly improve the efficiency and accuracy of MVI diagnosis. MATERIALS AND METHODS We collected H&E-stained slides from 753 patients with HCC at the First Affiliated Hospital of Zhejiang University. An external validation set with 358 patients was selected from The Cancer Genome Atlas database. The deep-learning model was trained by simulating the method used by pathologists to diagnose MVI. Model performance was evaluated with accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. RESULTS We successfully developed a MVI artificial intelligence diagnostic model (MVI-AIDM) which achieved an accuracy of 94.25% in the independent external validation set. The MVI positive detection rate of MVI-AIDM was significantly higher than the results of pathologists. Visualization results demonstrated the recognition of micro MVIs that were difficult to differentiate by the traditional pathology. Additionally, the model provided automatic quantification of the number of cancer cells and spatial information regarding MVI. CONCLUSIONS We developed a deep learning diagnostic model, which performed well and improved the efficiency and accuracy of MVI diagnosis. The model provided spatial information of MVI that was essential to accurately predict HCC recurrence after surgery.
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Affiliation(s)
- Xiuming Zhang
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Xiaotian Yu
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Wenjie Liang
- Department of Radiology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Zhongliang Zhang
- School of ManagementHangzhou Dianzi UniversityHangzhouP. R. China
| | - Shengxuming Zhang
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Linjie Xu
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Han Zhang
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Zunlei Feng
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Mingli Song
- Department of Computer Science and TechnologyZhejiang UniversityHangzhouP. R. China
| | - Jing Zhang
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
| | - Shi Feng
- Department of Pathology, The First Affiliated Hospital, College of MedicineZhejiang UniversityHangzhouP. R. China
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Pei YX, Su CG, Liao Z, Li WW, Wang ZX, Liu JL. Comparative effectiveness of several adjuvant therapies after hepatectomy for hepatocellular carcinoma patients with microvascular invasion. World J Gastrointest Surg 2024; 16:554-570. [PMID: 38463369 PMCID: PMC10921205 DOI: 10.4240/wjgs.v16.i2.554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/24/2023] [Accepted: 01/18/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND For resectable hepatocellular carcinoma (HCC), radical hepatectomy is commonly used as a curative treatment. However, postoperative recurrence significantly diminishes the overall survival (OS) of HCC patients, especially with microvascular invasion (MVI) as an independent high-risk factor for recurrence. While some studies suggest that postoperative adjuvant therapy may decrease the risk of recurrence following liver resection in HCC patients, the specific role of adjuvant therapies in those with MVI remains unclear. AIM To conduct a network meta-analysis (NMA) to evaluate the efficacy of various adjuvant therapies and determine the optimal adjuvant regimen. METHODS A systematic literature search was conducted on PubMed, EMBASE, and Web of Science until April 6, 2023. Studies comparing different adjuvant therapies or comparing adjuvant therapy with hepatectomy alone were included. Hazard ratios (HRs) with 95% confidence intervals were used to combine data on recurrence free survival and OS in both pairwise meta-analyses and NMA. RESULTS Fourteen eligible trials (2268 patients) reporting five different therapies were included. In terms of reducing the risk of recurrence, radiotherapy (RT) [HR = 0.34 (0.23, 0.5); surface under the cumulative ranking curve (SUCRA) = 97.7%] was found to be the most effective adjuvant therapy, followed by hepatic artery infusion chemotherapy [HR = 0.52 (0.35, 0.76); SUCRA = 65.1%]. Regarding OS improvement, RT [HR: 0.35 (0.2, 0.61); SUCRA = 93.1%] demonstrated the highest effectiveness, followed by sorafenib [HR = 0.48 (0.32, 0.69); SUCRA = 70.9%]. CONCLUSION Adjuvant therapy following hepatectomy may reduce the risk of recurrence and provide a survival benefit for HCC patients with MVI. RT appears to be the most effective adjuvant regimen.
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Affiliation(s)
- Yin-Xuan Pei
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Chengde Medical University, Chengde 067000, Hebei Province, China
| | - Chen-Guang Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Chengde Medical University, Chengde 067000, Hebei Province, China
| | - Zheng Liao
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Chengde Medical University, Chengde 067000, Hebei Province, China
| | - Wei-Wei Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Chengde Medical University, Chengde 067000, Hebei Province, China
| | - Zi-Xiang Wang
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Chengde Medical University, Chengde 067000, Hebei Province, China
| | - Jin-Long Liu
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Chengde Medical University, Chengde 067000, Hebei Province, China
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Hong S, Zhang J, Liu S, Jin Q, Li J, Xia A, Xu J. Protein profiles reveal MSH6/MSH2 as a potential biomarker for hepatocellular carcinoma with microvascular invasion. Hepatol Res 2024; 54:189-200. [PMID: 37776019 DOI: 10.1111/hepr.13971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/23/2023] [Accepted: 09/26/2023] [Indexed: 10/01/2023]
Abstract
AIM Microvascular invasion (MVI) is an independent risk factor for postoperative recurrence and metastasis in hepatocellular carcinoma (HCC). However, the specific protein expression profiles that differentiate HCC with MVI from those without MVI remain unclear. METHODS The profiles of proteins in early-stage HCC tissues and normal liver tissues were characterized by quantitative proteomics techniques. Immunohistochemical (IHC) staining was undertaken on tissue microarrays from 80 HCC patients to assess the expression of MSH2 and MSH6. Cell counting, colony formation, migration, and invasion assays were carried out in vitro. RESULTS We identified 5164 proteins in both HCC tissues and adjacent normal liver tissues. Compared to HCC without MVI, 148 upregulated proteins and 97 downregulated proteins were found in HCC with MVI. Particularly noteworthy was the remarkable upregulation of MSH6/MSH2 among these dysregulated proteins in HCC with MVI. Further validation through bioinformatics prediction and IHC confirmed the elevated expression of MSH6/MSH2, which correlated with aggressive disease characteristics and poor prognosis. Receiver operating characteristic curve analyses revealed a substantial area under the curve of 0.761 (specificity 71.79%, sensitivity 73.17%) for the combined use of MSH6/MSH2. Knockdown of MSH6/MSH2 significantly inhibited HCC cell proliferation and invasion in vitro. CONCLUSIONS Our study establishes MSH6 or MSH2 as an oncogene that is prominently overexpressed during HCC progression, which provides new targets for HCC with MVI.
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Affiliation(s)
- Shengqian Hong
- Department of Hepatobiliary Surgery, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Jialing Zhang
- Department of Hepatobiliary Surgery, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Shiqi Liu
- Department of Hepatobiliary Surgery, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Quan Jin
- Department of Hepatobiliary Surgery, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Jingqi Li
- Department of Pathology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, China
| | - Anliang Xia
- Department of Hepatobiliary Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - JianBo Xu
- Department of Hepatobiliary Surgery, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an, China
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Luo ZY, Tian Q, Cheng NM, Liu WH, Yang Y, Chen W, Zhang XZ, Zheng XY, Chen MS, Zhuang QY, Zhao BX, Liu CS, Liu XL, Li Q, Wang YC. Pien Tze Huang Inhibits Migration and Invasion of Hepatocellular Carcinoma Cells by Repressing PDGFRB/YAP/CCN2 Axis Activity. Chin J Integr Med 2024; 30:115-124. [PMID: 35947230 DOI: 10.1007/s11655-022-3533-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/28/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To investigate the effects of Pien Tze Huang (PZH) on the migration and invasion of HCC cells and underlying molecular mechanism. METHODS Cell counting kit-8 (CCK-8) was applied to evaluate the cell viabilities of SMMC-7721, SK-Hep-1, C3A and HL-7702 (6 × 103 cells/well) co-incubated with different concentrations of PZH (0, 0.2, 0.4, 0.6, 0.8 mg/mL) for 24 h. Transwell, wound healing assay, CCK-8 and Annexin V-FITC/PI staining were conducted to investigate the effects of PZH on the migration, invasion, proliferation and apoptosis of SK-Hep-1 and SMMC-7721 cells (650 µ g/mL for SK-Hep-1 cells and 330 µ g/mL for SMMC-7721 cells), respectively. In vivo, lung metastasis mouse model constructed by tail vein injection of HCC cells was used for evaluating the anti-metastasis function of PZH. SK-Hep-1 cells (106 cells/200 µ L per mice) were injected into B-NDG mice via tail vein. Totally 8 mice were randomly divided into PZH and control groups, 4 mice in each group. After 2-d inoculation, mice in the PZH group were administered with PZH (250 mg/kg, daily) and mice in the control group received only vehicle (PBS) from the 2nd day after xenograft to day 17. Transcriptome analysis based on RNA-seq was subsequently used for deciphering anti-tumor mechanism of PZH. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot were applied to verify RNA-seq results. Luciferase reporter assay was performed to examine the transcriptional activity of yes-associated protein (YAP). RESULTS PZH treatment significantly inhibited the migration, invasion, proliferation and promoted the apoptosis of HCC cells in vitro and in vivo (P<0.01). Transcriptome analysis indicated that Hippo signaling pathway was associated with anti-metastasis function of PZH. Mechanical study showed PZH significantly inhibited the expressions of platelet derived growth factor receptor beta (PDGFRB), YAP, connective tissue growth factor (CCN2), N-cadherin, vimentin and matrix metallopeptidase 2 (MMP2, P<0.01). Meanwhile, the phosphorylation of YAP was also enhanced by PZH treatment in vitro and in vivo. Furthermore, PZH played roles in inhibiting the transcriptional activity of YAP. CONCLUSION PZH restrained migration, invasion and epithelial-mesenchymal transition of HCC cells through repressing PDGFRB/YAP/CCN2 axis.
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Affiliation(s)
- Zhi-Yi Luo
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
- Fujian Pien Tze Huang Enterprise Key Laboratory of Natural Medicine Research and Development, Zhangzhou Pien Tze Huang Pharmaceutical Co., Ltd., Zhangzhou, Fujian Province, 363099, China
| | - Qi Tian
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
- College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, 350116, China
| | - Niang-Mei Cheng
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
- College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, 350116, China
| | - Wen-Han Liu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Ye Yang
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
- College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, 350116, China
| | - Wei Chen
- Department of Internal Medicine, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Xiang-Zhi Zhang
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
- College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, 350116, China
| | - Xiao-Yuan Zheng
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
- College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, 350116, China
| | - Ming-Sheng Chen
- Department of Internal Medicine, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
| | - Qiu-Yu Zhuang
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
- College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, 350116, China
| | - Bi-Xing Zhao
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
- College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, 350116, China
| | - Cong-Sheng Liu
- Fujian Pien Tze Huang Enterprise Key Laboratory of Natural Medicine Research and Development, Zhangzhou Pien Tze Huang Pharmaceutical Co., Ltd., Zhangzhou, Fujian Province, 363099, China
| | - Xiao-Long Liu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
- College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, 350116, China
| | - Qin Li
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China.
- College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, 350116, China.
- Department of Internal Medicine, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China.
| | - Ying-Chao Wang
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China
- College of Biological Science and Engineering and Mengchao Med-X Center, Fuzhou University, Fuzhou, 350116, China
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Bardol T, Pageaux GP, Assenat E, Alix-Panabières C. Circulating Tumor DNA Clinical Applications in Hepatocellular Carcinoma: Current Trends and Future Perspectives. Clin Chem 2024; 70:33-48. [PMID: 37962158 DOI: 10.1093/clinchem/hvad168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 09/13/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND Globally, liver cancers are the second most lethal malignancy after lung cancer (0.83 million deaths in 2020). Hepatocellular carcinoma (HCC) is the predominant type of primary liver cancer and is typically associated with liver fibrosis or cirrhosis. HCC diagnosis relies on histologic examination of surgical specimens or conventional tissue biopsy material. However, standard tissue biopsies are invasive and often do not accurately reflect the tumor heterogeneity. On the other hand, the use of liquid biopsies, represented mainly by circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs), has greatly increased in the past 2 decades. Indeed, liquid biopsies are a noninvasive, repeatable, and sensitive approach to studying tumor biology. CONTENT This review describes current clinical applications of ctDNA analysis in the management of patients with chronic liver disease, cirrhosis, and HCC. There is a substantial clinical potential of ctDNA, but interventional studies are still lacking for the moment. SUMMARY Detection of ctDNA in both asymptomatic individuals and high-risk patients (with chronic liver disease or cirrhosis) contributes to the early diagnosis of HCC. ctDNA analysis also offer tremendous information on the tumor burden and on the risk of early recurrence. The implementation of ctDNA analysis, in association with classical tumor markers (e.g., alpha-fetoprotein), may improve (a) HCC screening in high-risk patients, (b) stratification of the recurrence risk after surgery, and (c) prognosis evaluation of patients with HCC.
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Affiliation(s)
- Thomas Bardol
- Laboratory of Rare Human Circulating Cells, University Hospital Center, University of Montpellier, Montpellier, France
- CREEC, MIVEGEC, University of Montpellier, CNRS, IRD, Montpellier, France
- Department of Digestive Surgery and Transplantation, Digestive and Mini-invasive Surgery Unit, Montpellier University Hospital, Montpellier University, Montpellier, France
| | - Georges-Philippe Pageaux
- Hepatology and Liver Transplant Unit, Saint Eloi University Hospital, Montpellier University, Montpellier, France
| | - Eric Assenat
- Department of Medical Oncology, Saint Eloi University Hospital Center, Montpellier University, Montpellier, France
| | - Catherine Alix-Panabières
- Laboratory of Rare Human Circulating Cells, University Hospital Center, University of Montpellier, Montpellier, France
- CREEC, MIVEGEC, University of Montpellier, CNRS, IRD, Montpellier, France
- European Liquid Biopsy Society (ELBS), Hamburg, Germany
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Li Z, Pai R, Gupta S, Currenti J, Guo W, Di Bartolomeo A, Feng H, Zhang Z, Li Z, Liu L, Singh A, Bai Y, Yang B, Mishra A, Yang K, Qiao L, Wallace M, Yin Y, Xia Q, Chan JKY, George J, Chow PKH, Ginhoux F, Sharma A. Presence of onco-fetal neighborhoods in hepatocellular carcinoma is associated with relapse and response to immunotherapy. NATURE CANCER 2024; 5:167-186. [PMID: 38168935 DOI: 10.1038/s43018-023-00672-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 10/16/2023] [Indexed: 01/05/2024]
Abstract
Onco-fetal reprogramming of the tumor ecosystem induces fetal developmental signatures in the tumor microenvironment, leading to immunosuppressive features. Here, we employed single-cell RNA sequencing, spatial transcriptomics and bulk RNA sequencing to delineate specific cell subsets involved in hepatocellular carcinoma (HCC) relapse and response to immunotherapy. We identified POSTN+ extracellular matrix cancer-associated fibroblasts (EM CAFs) as a prominent onco-fetal interacting hub, promoting tumor progression. Cell-cell communication and spatial transcriptomics analysis revealed crosstalk and co-localization of onco-fetal cells, including POSTN+ CAFs, FOLR2+ macrophages and PLVAP+ endothelial cells. Further analyses suggest an association between onco-fetal reprogramming and epithelial-mesenchymal transition (EMT), tumor cell proliferation and recruitment of Treg cells, ultimately influencing early relapse and response to immunotherapy. In summary, our study identifies POSTN+ CAFs as part of the HCC onco-fetal niche and highlights its potential influence in EMT, relapse and immunotherapy response, paving the way for the use of onco-fetal signatures for therapeutic stratification.
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Affiliation(s)
- Ziyi Li
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rhea Pai
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, Perth, Western Australia, Australia
- Curtin Medical School, Curtin University, Perth, Western Australia, Australia
| | - Saurabh Gupta
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, Perth, Western Australia, Australia
- Curtin Medical School, Curtin University, Perth, Western Australia, Australia
| | - Jennifer Currenti
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, Perth, Western Australia, Australia
- Curtin Medical School, Curtin University, Perth, Western Australia, Australia
| | - Wei Guo
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Anna Di Bartolomeo
- Storr Liver Centre, The Westmead Institute for Medical Research and Westmead Hospital, University of Sydney, Sydney, New South Wales, Australia
| | - Hao Feng
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Institute of Transplantation, Shanghai, China
| | - Zijie Zhang
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhizhen Li
- Department of Biliary Tract Surgery I, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - Longqi Liu
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, P. R. China
| | - Abhishek Singh
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, Perth, Western Australia, Australia
| | - Yinqi Bai
- BGI-Shenzhen, Beishan Industrial Zone, Shenzhen, P. R. China
| | | | - Archita Mishra
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
- Telethon Kids Institute, University of Western Australia, Perth Children's Hospital, Nedlands, Western Australia, Australia
| | - Katharine Yang
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Liang Qiao
- Storr Liver Centre, The Westmead Institute for Medical Research and Westmead Hospital, University of Sydney, Sydney, New South Wales, Australia
| | - Michael Wallace
- Department of Hepatology and Western Australian Liver Transplant Service, Sir Charles Gairdner Hospital, Nedlands, Western Australia, Australia
- Medical School, University of Western Australia, Nedlands, Western Australia, Australia
| | - Yujia Yin
- Department of Obstetrics and Gynecology, Xinhua Hospital Affiliated to Shanghai Jiaotong University Medicine School, Shanghai, China
| | - Qiang Xia
- Department of Liver Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Shanghai Institute of Transplantation, Shanghai, China
| | - Jerry Kok Yen Chan
- Department of Reproductive Medicine, KK Women's and Children's Hospital, Singapore, Singapore
- Academic Clinical Program in Obstetrics and Gynaecology, Duke-NUS Medical School, Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Jacob George
- Storr Liver Centre, The Westmead Institute for Medical Research and Westmead Hospital, University of Sydney, Sydney, New South Wales, Australia
| | - Pierce Kah-Hoe Chow
- Department of Hepatopancreatobiliary and Transplant Surgery, Singapore General Hospital and National Cancer Centre Singapore, Singapore, Singapore.
- Surgery Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore.
| | - Florent Ginhoux
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore.
- Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore.
- Gustave Roussy Cancer Campus, Villejuif, France.
| | - Ankur Sharma
- Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, Perth, Western Australia, Australia.
- Curtin Medical School, Curtin University, Perth, Western Australia, Australia.
- Institute of Molecular and Cell Biology, A∗STAR, Singapore, Singapore.
- KK Research Centre, KK Women's and Children's Hospital, Singapore, Singapore.
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Sheng L, Wei H, Yang T, Yang J, Zhang L, Zhu X, Jiang H, Song B. Extracellular contrast agent-enhanced MRI is as effective as gadoxetate disodium-enhanced MRI for predicting microvascular invasion in HCC. Eur J Radiol 2024; 170:111200. [PMID: 37995512 DOI: 10.1016/j.ejrad.2023.111200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/31/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023]
Abstract
PURPOSE To compare the performances of gadoxetate disodium-enhanced MRI (EOB-MRI) and extracellular contrast agent-enhanced MRI (ECA-MRI) for predicting microvascular invasion (MVI) in HCC. MATERIALS AND METHODS From November 2009 to December 2021, consecutive HCC patients who underwent preoperative contrast-enhanced MRI were retrospectively enrolled into either an ECA-MRI or EOB-MRI cohort. In the ECA-MRI cohort, a preoperative MVI score was constructed in the training dataset using a logistic regression model that evaluated pathological type. In a propensity score-matched testing dataset of the ECA-MRI cohort, the MVI score was validated and compared with a previously proposed EOB-MRI-based MVI score calculated in the EOB-MRI cohort. Time-to-early recurrence survival was evaluated by the Kaplan-Meier method with the log-rank test. RESULTS A total of 536 patients were included (478 men; 53 years, interquartile range, 46-62 years), 322 (60.1 %) with pathologically confirmed MVI. Based on the training dataset, independent variables associated with MVI included serum alpha-fetoprotein > 400 ng/ml (odds ratio [OR] = 2.3), infiltrative appearance (OR = 4.9), internal artery (OR = 2.5) and nodule-in-nodule architecture (OR = 2.4), which were incorporated into the ECA-MRI-based MVI score. The testing dataset AUC of the ECA-MRI score was 0.720, which was comparable to that of the EOB-MRI-based MVI score (AUC = 0.721; P =.99). Patients from either the ECA-MRI or the EOB-MRI cohort with model-predicted MVI had significantly shorter time-to-early recurrence than those without MVI (P <.001). CONCLUSION Based on the preoperative serum alpha-fetoprotein and three MRI features, ECA-MRI demonstrated comparable performance to EOB-MRI for predicting MVI in HCC.
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Affiliation(s)
- Liuji Sheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ting Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jie Yang
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lin Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiaomei Zhu
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Radiology, Sanya People's Hospital, Sanya, Hainan, China.
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