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Yang Q, Zhou J, Luo B, Zheng R, Liao J, Tang L, Cheng W, Jing X, Cai W, Cheng Z, Liu F, Han Z, Yu X, Yu J, Liang P. Non-radiomics imaging (US-CEUS) features and clinical text features: correlation with microvascular invasion and tumor grading in hepatocellular carcinoma. Abdom Radiol (NY) 2025; 50:2476-2493. [PMID: 39607454 DOI: 10.1007/s00261-024-04659-0] [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: 07/09/2024] [Revised: 10/25/2024] [Accepted: 10/26/2024] [Indexed: 11/29/2024]
Abstract
OBJECTIVES To predict microvascular invasion (MVI) status and tumor grading of hepatocellular carcinoma (HCC) by evaluating preoperative non-radiomics ultrasound and contrast-enhanced ultrasound (US-CEUS) features and determine the influences of MVI/tumor grading on the category of CEUS LI-RADS for HCC. METHODS A total of 506 HCC patients who underwent preoperative US-CEUS examinations from 8 hospitals between July 2020 and June 2023 were enrolled. According to the MVI status, all the patients were classified, and HCC differentiation was assessed by using Edmondson-Steiner (ES) grading: MVI-negative (M0) and low-grade ES (GI/II) (MN-L, n = 297) and MVI-positive (M1/M2) and/or high-grade ES (GIII/IV) (MP-H, n = 209). Stratified analysis was performed based on fibrosis stage and tumor size. RESULTS The results proved that MN-L HCC was more frequently classified into the LR-5 category (p = 0.034), while MP-H HCC was more frequently classified into the LR-TIV (p = 0.010). The heterogeneously arterial phase hyperenhancement (APHE) is significantly correlated with MVI(+)/high grade-ES (p = 0.003). Compared with MN-L HCC, the onset of washout was earlier, washout rate was higher, and tumor-invasion border was larger (all p < 0.01) in MP-H HCC. In addition, fibrosis stage and tumor size significantly influenced the onset of washout and washout rate of HCC (all p < 0.01). The tumor-invasion border was only positively correlated with tumor size (p < 0.001) rather than fibrosis stage (p > 0.05). CONCLUSIONS MVI status and tumor grading influence the classification of LR-5 and LR-TIV. Heterogeneous APHE, higher washout rate, earlier onset of washout (≤65 s), larger tumor-invasion border (≥3 mm) and higher alpha fetoprotein level indicate the presence of MVI and/or high-grade ES.
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Affiliation(s)
- Qi Yang
- Chinese PLA General Hospital, Beijing, China
- Peking University Shenzhen Hospital, Shenzhen, China
| | - Jianhua Zhou
- Sun Yat-sen University Cancer Center, Guangzhou, China
- Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Baoming Luo
- Sun Yat-sen Memorial Hospital, Guangzhou, China
| | - Rongqin Zheng
- Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | | | - Lina Tang
- Fujian Provincial Cancer Hospital, Fuzhou, China
| | - Wen Cheng
- Harbin Medical University Cancer Hospital, Harbin, China
| | - Xiang Jing
- Tianjin Third Central Hospital, Tianjin, China
| | - Wenjia Cai
- Chinese PLA General Hospital, Beijing, China
| | | | - Fangyi Liu
- Chinese PLA General Hospital, Beijing, China
| | - Zhiyu Han
- Chinese PLA General Hospital, Beijing, China
| | - Xiaoling Yu
- Chinese PLA General Hospital, Beijing, China
| | - Jie Yu
- Chinese PLA General Hospital, Beijing, China
| | - Ping Liang
- Chinese PLA General Hospital, Beijing, China.
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Stocker D, Hectors S, Marinelli B, Carbonell G, Bane O, Hulkower M, Kennedy P, Ma W, Lewis S, Kim E, Wang P, Taouli B. Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach. Abdom Radiol (NY) 2025; 50:2000-2011. [PMID: 39460801 PMCID: PMC11991973 DOI: 10.1007/s00261-024-04606-z] [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: 05/16/2024] [Revised: 09/04/2024] [Accepted: 09/17/2024] [Indexed: 10/28/2024]
Abstract
PURPOSE To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy. METHODS This retrospective study included 154 patients (38 female; mean age 66.8 years) who underwent contrast-enhanced MRI prior to radiation segmentectomy. Radiomics features were manually extracted on volumes of interest on post-contrast T1-weighted images at the portal venous phase (PVP). Tumor-based response assessment was evaluated 6 months post-treatment using mRECIST. A logistic regression model was used to predict binary response outcome [complete response at 6 months with no-re-treatment (response group) against the rest (non-response group, including partial response, progressive disease, stable disease and complete response after re-treatment within 6 months after radiation segmentectomy) using baseline clinical parameters and radiomics features. We accessed the value of different sets of predictors using cross-validation technique. AUCs were compared using DeLong tests. RESULTS A total 168 HCCs (mean size 2.9 ± 1.7 cm) were analyzed in 154 patients. The response group consisted of 113 HCCs and the non-response group of 55 HCCs. Baseline clinical parameters (AUC 0.531; sensitivity, 0.781; specificity, 0.279; positive predictive value (PPV), 0.345; negative predictive value (NPV), 0.724) and AFP (AUC 0.632; sensitivity, 0.833; specificity, 0.466; PPV, 0.432; NPV, 0.851) showed poor performance for response prediction. The model using a combination of radiomics features and clinical parameters/AFP showed the best performance (AUC 0.736; sensitivity, 0.706; specificity, 0.662; PPV 0.504; NPV, 0.822), significantly better than the clinical model (p < 0.001) or AFP alone (p < 0.001). CONCLUSION The combination of radiomics features from pre-treatment MRI with clinical parameters and AFP showed fair performance for predicting HCC response to radiation segmentectomy, better than that of AFP. These results need further validation.
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Affiliation(s)
- Daniel Stocker
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091, Zurich, Switzerland.
| | - Stefanie Hectors
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett Marinelli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Interventional Radiology, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Guillermo Carbonell
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Radiology, University Hospital Virgen de la Arrixaca, Murcia, Spain
| | - Octavia Bane
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Miriam Hulkower
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Paul Kennedy
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Weiping Ma
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sara Lewis
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edward Kim
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pei Wang
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Liver Cancer Program, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Tao R, Lu H, Dong X, Ren QQ, Fan H, Tang Z, Xia X. A nomogram based on quantitative MR signal intensity predicts early response to combined systemic treatment in patients with hepatocellular carcinoma. Front Oncol 2025; 15:1527108. [PMID: 40171262 PMCID: PMC11959652 DOI: 10.3389/fonc.2025.1527108] [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: 11/12/2024] [Accepted: 02/24/2025] [Indexed: 04/03/2025] Open
Abstract
Objective This study aimed to develop and evaluate the value of a nomogram based on quantitative MR signal intensity to predict response to combined systemic therapy of anti-angiogenesis and immune checkpoint inhibitor (ICI) in hepatocellular carcinoma (HCC) patients. Methods 117 HCC patients who underwent the combined systemic treatment at a tertiary hospital between September 2020 and May 2024 were enrolled and divided into a development cohort (n = 82) and a validation cohort (n = 35). The predictive value of the relative signal intensity attenuation index (rSIAI) based on enhanced MR parameters and laboratory parameters on disease control was evaluated using receiver operating characteristic (ROC) curves, with the determination of optimal cut-off values (COVs) accomplished via Youden's index. Univariate and multivariable analyses were conducted to evaluate the association between COVs and disease control. The validity of the COVs was further confirmed through chi-square testing and calculation of Cramer's V coefficient (V). A nomogram was constructed based on the multivariable logistic regression model and evaluated for clinical applicability. Results rSIAI from arterial to portal phase (rSI_ap) in combination with peripheral T-cell subset (CD4+) achieved the most accurate predictive performance for outcome compared to rSI_ap or CD4+ alone, with an area under the curve (AUC) of the ROC of 0.845 (95% CI, 0.748-0.915). A nomogram based on rSI_ap and CD4+ was constructed. Calibration and decision curve analyses confirmed the clinical relevance and value of the nomogram. Conclusion The nomogram based on rSI_ap has the potential to be a non-invasive tool for predicting disease control in advanced HCC patients who have received combined anti-angiogenesis and ICI therapies.
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Affiliation(s)
- Ran Tao
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haohao Lu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangjun Dong
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Qian Ren
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongjie Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhaoming Tang
- Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xiangwen Xia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Qu Q, Liu Z, Lu M, Xu L, Zhang J, Liu M, Jiang J, Gu C, Ma Q, Huang A, Zhang X, Zhang T. Preoperative Gadoxetic Acid-Enhanced MRI Features for Evaluation of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma: Creating Nomograms for Risk Assessment. J Magn Reson Imaging 2024; 60:1094-1110. [PMID: 38116997 DOI: 10.1002/jmri.29187] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/01/2023] [Accepted: 12/02/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Vessels encapsulating tumor cluster (VETC) and microvascular invasion (MVI) have a synergistic effect on prognosis assessment and treatment selection of hepatocellular carcinoma (HCC). Preoperative noninvasive evaluation of VETC and MVI is important. PURPOSE To explore the diagnosis value of preoperative gadoxetic acid (GA)-enhanced magnetic resonance imaging (MRI) features for MVI, VETC, and recurrence-free survival (RFS) in HCC. STUDY TYPE Retrospective. POPULATION 240 post-surgery patients with 274 pathologically confirmed HCC (allocated to training and validation cohorts with a 7:3 ratio) and available tumor marker data from August 2014 to December 2021. FIELD STRENGTH/SEQUENCE 3-T, T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. ASSESSMENT Three radiologists subjectively reviewed preoperative MRI, evaluated clinical and conventional imaging features associated with MVI+, VETC+, and MVI+/VETC+ HCC. Regression-based nomograms were developed for HCC in the training cohort. Based on the nomograms, the RFS prognostic stratification system was further. Follow-up occurred every 3-6 months. STATISTICAL TESTS Chi-squared test or Fisher's exact test, Mann-Whitney U-test or t-test, least absolute shrinkage and selection operator-penalized, multivariable logistic regression analyses, receiver operating characteristic analysis, Harrell's concordance index (C-index), Kaplan-Meier plots. Significance level: P < 0.05. RESULTS In the training group, 44 patients with MVI+ and 74 patients with VETC+ were histologically confirmed. Three nomograms showed good performance in the training (C-indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.892 vs. 0.848 vs. 0.910) and validation (C-indices: MVI+ vs. VETC+ vs. MVI+/VETC+, 0.839 vs. 0.810 vs. 0.855) cohorts. The median follow-up duration for the training cohort was 43.6 (95% CI, 35.0-52.2) months and 25.8 (95% CI, 16.1-35.6) months for the validation cohort. Patients with either pathologically confirmed or nomogram-estimated MVI, VETC, and MVI+/VETC+ suffered higher risk of recurrence. DATA CONCLUSION GA-enhanced MRI and clinical variables might assist in preoperative estimation of MVI, VETC, and MVI+/VETC+ in HCC. EVIDENCE LEVEL 4 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Qi Qu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Zixin Liu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Mengtian Lu
- Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Lei Xu
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Jiyun Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Maotong Liu
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Jifeng Jiang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Chunyan Gu
- Department of Pathology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Qinrong Ma
- Department of Pathology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Aina Huang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Xueqin Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
| | - Tao Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People's Hospital, Nantong, Jiangsu, China
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Wang T, Chen H, Chen Z, Li M, Lu Y. Prediction model of early recurrence of multimodal hepatocellular carcinoma with tensor fusion. Phys Med Biol 2024; 69:125003. [PMID: 38776945 DOI: 10.1088/1361-6560/ad4f45] [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: 12/24/2023] [Accepted: 05/22/2024] [Indexed: 05/25/2024]
Abstract
Objective.In oncology, clinical decision-making relies on a multitude of data modalities, including histopathological, radiological, and clinical factors. Despite the emergence of computer-aided multimodal decision-making systems for predicting hepatocellular carcinoma (HCC) recurrence post-hepatectomy, existing models often employ simplistic feature-level concatenation, leading to redundancy and suboptimal performance. Moreover, these models frequently lack effective integration with clinically relevant data and encounter challenges in integrating diverse scales and dimensions, as well as incorporating the liver background, which holds clinical significance but has been previously overlooked.Approach.To address these limitations, we propose two approaches. Firstly, we introduce the tensor fusion method to our model, which offers distinct advantages in handling multi-scale and multi-dimensional data fusion, potentially enhancing overall performance. Secondly, we pioneer the consideration of the liver background's impact, integrating it into the feature extraction process using a deep learning segmentation-based algorithm. This innovative inclusion aligns the model more closely with real-world clinical scenarios, as the liver background may contain crucial information related to postoperative recurrence.Main results.We collected radiomics (MRI) and histopathological images from 176 cases diagnosed by experienced clinicians across two independent centers. Our proposed network underwent training and 5-fold cross-validation on this dataset before validation on an external test dataset comprising 40 cases. Ultimately, our model demonstrated outstanding performance in predicting early recurrence of HCC postoperatively, achieving an AUC of 0.883.Significance.These findings signify significant progress in addressing challenges related to multimodal data fusion and hold promise for more accurate clinical outcome predictions. In this study, we exploited global 3D liver background into modelling which is crucial to to the prognosis assessment and analyzed the whole liver background in addition to the tumor region. Both MRI images and histopathological images of HCC were fused at high-dimensional feature space using tensor techniques to solve cross-scale data integration issue.
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Affiliation(s)
- Tianyi Wang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Haimei Chen
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zebin Chen
- Department of Liver Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Mingkai Li
- Department of Gastroenterology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yao Lu
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China
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Huang Z, Zhu RH, Li SS, Luo HC, Li KY. CEUS in prediction of early recurrence of hepatocellular carcinoma after curative resection and to stratify the risk of early recurrence: a retrospective observational study. Abdom Radiol (NY) 2024; 49:1870-1880. [PMID: 38557770 DOI: 10.1007/s00261-024-04252-5] [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: 12/13/2023] [Revised: 02/09/2024] [Accepted: 02/12/2024] [Indexed: 04/04/2024]
Abstract
PURPOSE Early recurrence (ER) after surgery is related to early death in patients with hepatocellular carcinoma (HCC) after radical resection. To explore the role of preoperative contrast-enhanced ultrasound (CEUS) in predicting ER of HCC after curative resection and to stratify the risk of ER. MATERIALS AND METHODS This study evaluated consecutive 556 patients with HCC who were examined by CEUS during the 2 weeks before curative resection between January 2011 and December 2018. ER was defined as intrahepatic and/or extrahepatic recurrence within 2 year after resection of HCC. Univariate and logistic regression analyses were performed to identify independent risk factors for ER after surgical resection of HCC. Recurrence-free time (RFS) rates were analyzed and compared by log-rank test. RESULTS ER occurred in 307 (55.2%) of the 556 patients. Univariate and multivariate analyses revealed that a tumor size ≥ 30 mm and satellite nodules seen on CEUS, DL(deep learning) radiomics reoccurrence score based on the frame of image with the maximum intensity of CEUS and an elevated alpha-fetoprotein level were significantly associated with ER (P < .05). Based on the number of predictors present, patients with CEUS LR-5 HCC were stratified into three risk subgroups: risk group 3 (high-risk patients, 4 predictors), risk group 2 (medium-risk patients, 2-3 predictors), and risk group 1 (low-risk patients, 0-1 predictor). The 2-year RFS rate was 19.4% in risk group 3, 40.9% in risk group 2, and 48.1% in risk group 1; the corresponding mean RFS times were 14.0 ± 2.9 months, 43.7 ± 6.6 months, and 55.5 ± 2.8 months, respectively (P < .001). CONCLUSIONS Tumor size ≥ 30 mm and satellite nodules seen on CEUS, DL radiomics reoccurrence score based on the frame of image with the maximum intensity of CEUS and an elevated alpha-fetoprotein level can predict ER of HCC.
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Affiliation(s)
- Zhe Huang
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan City, 430030, Hubei Province, China
| | - Rong-Hua Zhu
- Institute of Hepato-Pancreato-Bililary Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan City, 430030, Hubei Province, China
| | - Shan-Shan Li
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan City, 430030, Hubei Province, China
| | - Hong-Chang Luo
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan City, 430030, Hubei Province, China.
| | - Kai-Yan Li
- Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan City, 430030, Hubei Province, China.
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Lee JH, Hwang JA, Gu K, Shin J, Han S, Kim YK. Magnetic resonance elastography as a preoperative assessment for predicting intrahepatic recurrence in patients with hepatocellular carcinoma. Magn Reson Imaging 2024; 109:127-133. [PMID: 38513784 DOI: 10.1016/j.mri.2024.03.014] [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: 01/26/2024] [Revised: 03/03/2024] [Accepted: 03/13/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE Magnetic resonance elastography (MRE) is a noninvasive tool for diagnosing hepatic fibrosis with high accuracy. We investigated the preoperative clinical and imaging predictors of intrahepatic recurrence after curative resection of hepatocellular carcinoma (HCC), and evaluated MRE as a predictor of intrahepatic recurrence. METHODS We retrospectively evaluated 80 patients who underwent preoperative contrast-enhanced magnetic resonance imaging (MRI) with two-dimensional MRE and curative resection for treatment-naïve HCC between May 2019 and December 2021. Liver stiffness (LS) was measured on the elastograms, and the optimal cutoff of LS for predicting intrahepatic recurrence was obtained using receiver operating characteristic (ROC) analysis. An LS above this cutoff was defined as MRE-recurrence. Preoperative imaging features of the tumor were assessed on MRI, including features in the Liver Imaging Reporting and Data System and microvascular invasion (MVI). Recurrence-free survival (RFS) rates were estimated using the Kaplan-Meier method, and differences were compared using the log-rank test. Using a Cox proportional hazards model, we conducted a multivariable analysis to investigate the factors affecting recurrence-free survival. RESULTS During a median follow-up period of 32 months (range, 4-52 months), thirteen patients (16.3%) developed intrahepatic recurrence. ROC analysis determined an LS cutoff of ≥4.35 kPa to define MRE-recurrence. The 4-year RFS rate was significantly higher in patients without MRE-recurrence than in those with MRE-recurrence (93.4% vs. 48.9%; p = 0.001). In multivariable analysis, MRE-recurrence (Hazard ratio [HR], 5.9; 95% confidence interval [CI], 1.5-23.1) and MVI (HR, 3.4; 95% CI, 1.0-11.3) were independent predictors of intrahepatic recurrence. CONCLUSIONS Patients without MRE-recurrence had significantly higher RFS rates than those with MRE-recurrence. MRE-recurrence and MVI were independent predictors of intrahepatic recurrence in patients after curative resection for HCC.
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Affiliation(s)
- Jeong Hyun Lee
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeong Ah Hwang
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Kyowon Gu
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jaeseung Shin
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seungchul Han
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Young Kon Kim
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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Chen JP, Yang RH, Zhang TH, Liao LA, Guan YT, Dai HY. Pre-operative enhanced magnetic resonance imaging combined with clinical features predict early recurrence of hepatocellular carcinoma after radical resection. World J Gastrointest Oncol 2024; 16:1192-1203. [PMID: 38660657 PMCID: PMC11037060 DOI: 10.4251/wjgo.v16.i4.1192] [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: 10/17/2023] [Revised: 01/28/2024] [Accepted: 02/28/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Indentifying predictive factors for postoperative recurrence of hepatocellular carcinoma (HCC) has great significance for patient prognosis. AIM To explore the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) combined with clinical features in predicting early recurrence of HCC after resection. METHODS A total of 161 patients with pathologically confirmed HCC were enrolled. The patients were divided into early recurrence and non-early recurrence group based on the follow-up results. The clinical, laboratory, pathological results and Gd-EOB-DTPA enhanced MRI imaging features were analyzed. RESULTS Of 161 patients, 73 had early recurrence and 88 were had non-early recurrence. Univariate analysis showed that patient age, gender, serum alpha-fetoprotein level, the Barcelona Clinic Liver Cancer stage, China liver cancer (CNLC) stage, microvascular invasion (MVI), pathological satellite focus, tumor size, tumor number, tumor boundary, tumor capsule, intratumoral necrosis, portal vein tumor thrombus, large vessel invasion, nonperipheral washout, peritumoral enhancement, hepatobiliary phase (HBP)/tumor signal intensity (SI)/peritumoral SI, HBP peritumoral low signal and peritumoral delay enhancement were significantly associated with early recurrence of HCC after operation. Multivariate logistic regression analysis showed that patient age, MVI, CNLC stage, tumor boundary and large vessel invasion were independent predictive factors. External data validation indicated that the area under the curve of the combined predictors was 0.861, suggesting that multivariate logistic regression was a reasonable predictive model for early recurrence of HCC. CONCLUSION Gd-EOB-DTPA enhanced MRI combined with clinical features would help predicting the early recurrence of HCC after operation.
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Affiliation(s)
- Jian-Ping Chen
- Department of Intervention, Meizhou People’s Hospital, Meizhou 514031, Guangdong Province, China
| | - Ri-Hui Yang
- Department of Medical Imaging, Meizhou People’s Hospital, Meizhou 514031, Guangdong Province, China
| | - Tian-Hui Zhang
- Department of Medical Imaging, Meizhou People’s Hospital, Meizhou 514031, Guangdong Province, China
| | - Li-An Liao
- Department of Medical Imaging, Meizhou People’s Hospital, Meizhou 514031, Guangdong Province, China
| | - Yu-Ting Guan
- Department of Medical Imaging, Meizhou People’s Hospital, Meizhou 514031, Guangdong Province, China
| | - Hai-Yang Dai
- Department of Medical Imaging, Huizhou Municipal Central Hospital, Huizhou 516001, Guangdong Province, China
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Liu WM, Zhao XY, Gu MT, Song KR, Zheng W, Yu H, Chen HL, Xu XW, Zhou X, Liu AE, Jia NY, Wang PJ. Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma. World J Oncol 2024; 15:58-71. [PMID: 38274720 PMCID: PMC10807913 DOI: 10.14740/wjon1731] [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: 09/19/2023] [Accepted: 11/15/2023] [Indexed: 01/27/2024] Open
Abstract
Background The aim of the study is to demonstrate that radiomics of preoperative multi-sequence magnetic resonance imaging (MRI) can indeed improve the predictive performance of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods A total of 206 patients with pathologically confirmed HCC who underwent preoperative enhanced MRI were retrospectively recruited. Univariate and multivariate logistic regression analysis identified the independent clinicoradiologic predictors of MVI present and constituted the clinicoradiologic model. Recursive feature elimination (RFE) was applied to select radiomics features (extracted from six sequence images) and constructed the radiomics model. Clinicoradiologic model plus radiomics model formed the clinicoradiomics model. Five-fold cross-validation was used to validate the three models. Discrimination, calibration, and clinical utility were used to evaluate the performance. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to compare the prediction accuracy between models. Results The clinicoradiologic model contained alpha-fetoprotein (AFP)_lg10, radiological capsule enhancement, enhancement pattern and arterial peritumoral enhancement, which were independent risk factors of MVI. There were 18 radiomics features related to MVI constructed the radiomics model. The mean area under the receiver operating curve (AUC) of clinicoradiologic, radiomics and clinicoradiomics model were 0.849, 0.925 and 0.950 in the training cohort and 0.846, 0.907 and 0.933 in the validation cohort, respectively. The three models' calibration curves fitted well, and decision curve analysis (DCA) confirmed the clinical usefulness. Compared with the clinicoradiologic model, the NRI of radiomics and clinicoradiomics model increased significantly by 0.575 and 0.825, respectively, and the IDI increased significantly by 0.280 and 0.398, respectively. Conclusions Radiomics of preoperative multi-sequence MRI can improve the predictive performance of MVI in HCC.
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Affiliation(s)
- Wan Min Liu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- These authors contributed equally to this work
| | - Xing Yu Zhao
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- These authors contributed equally to this work
| | - Meng Ting Gu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Kai Rong Song
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Wei Zheng
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Hui Yu
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Hui Lin Chen
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiao Wen Xu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiang Zhou
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Ai E Liu
- Department of Research Center, Shanghai United Imaging Intelligence Co., Ltd, Shanghai, China
| | - Ning Yang Jia
- Department of Radiology, Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Shanghai Naval Military Medical University, Shanghai, China
| | - Pei Jun Wang
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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11
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Deng Y, Yang D, Tan X, Xu H, Xu L, Ren A, Liu P, Yang Z. Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study. BMC Med Imaging 2024; 24:29. [PMID: 38281008 PMCID: PMC10821254 DOI: 10.1186/s12880-024-01206-7] [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: 09/03/2023] [Accepted: 01/18/2024] [Indexed: 01/29/2024] Open
Abstract
PURPOSE To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre. METHOD A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre. RESULTS Among the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774-0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700-0.888) and 0.883 (95% CI: 0.807-0.959), respectively. No statistical difference in AUC values among training set and testing sets was found. CONCLUSION The proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability.
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Affiliation(s)
- Yuhui Deng
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China
- Medical Imaging Division, Heilongjiang Provincial Hospital, Harbin Institute of Technology, Zhongshan Road 82, Xiangfang District, Harbin, 150036, China
| | - Dawei Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China
| | - Xianzheng Tan
- Department of Radiology, Hunan Provincial People's Hospital, the First Affiliated Hospital of Hunan Normal University, Changsha, 410005, China
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China
| | - Lixue Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China
| | - Ahong Ren
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China
| | - Peng Liu
- Department of Radiology, Hunan Provincial People's Hospital, the First Affiliated Hospital of Hunan Normal University, Changsha, 410005, China.
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Yongan Road 95, West District, Beijing, 100050, China.
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Minamiguchi K, Irizato M, Uchiyama T, Taiji R, Nishiofuku H, Marugami N, Tanaka T. Hepatobiliary-phase gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid MRI for pretreatment prediction of efficacy-to-standard-therapies based on Barcelona Clinic Liver Cancer algorithm: an up-to-date review. Eur Radiol 2023; 33:8764-8775. [PMID: 37470828 DOI: 10.1007/s00330-023-09950-0] [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: 02/22/2023] [Revised: 05/15/2023] [Accepted: 06/12/2023] [Indexed: 07/21/2023]
Abstract
Recent advances in systemic therapy have had major impacts on treatment strategies for hepatocellular carcinoma (HCC). The 2022 Barcelona Clinic Liver Cancer (BCLC) guidelines incorporate a new section on clinical decision-making for personalized medicine, although the first treatment suggested by the BCLC guidelines is based on solid scientific evidence. More than ever before, the appropriate treatment strategy must be selected prior to the initiation of therapy for HCC. Gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid magnetic resonance imaging (Gd-EOB-DTPA-MRI) is essential for liver imaging and the hepatobiliary phase (HBP) of EOB-MRI reflects the expression of organic anion transporting polypeptide (OATP) transporters. Molecules associated with OATP expression are relevant in the molecular classification of HCC subclasses, and EOB-MRI is becoming increasingly important with advances in the molecular and genetic understanding of HCC. In this review, we describe imaging findings for the pretreatment prediction of response to standard therapies for HCC based on the BCLC algorithm using the HBP of EOB-MRI, with specific attention to the molecular background of OATPs. A more complete understanding of these findings will help radiologists suggest appropriate treatments and clinical follow-ups and could lead to the development of more personalized treatment strategies in the future. CLINICAL RELEVANCE STATEMENT: In the coming era of personalized medicine, HBP of EOB-MRI reflecting molecular and pathological factors could play a predictive role in the therapeutic efficacy of HCC and contribute to treatment selection. KEY POINTS: • Imaging features of hepatobiliary phase predict treatment efficacy prior to therapy and contribute to treatment choice. • Wnt/β-catenin activation associated with organic anion transporting polypeptide expression is involved in the tumor immune microenvironment and chemo-responsiveness. • Peritumoral hypointensity of hepatobiliary phase reflecting microvascular invasion affects the therapeutic efficacy of locoregional to systemic therapy.
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Affiliation(s)
- Kiyoyuki Minamiguchi
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara, Nara, 634-8522, Japan.
| | - Mariko Irizato
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara, Nara, 634-8522, Japan
| | - Tomoko Uchiyama
- Department of Diagnostic Pathology, Nara Medical University, Kashihara, Nara, Japan
| | - Ryosuke Taiji
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara, Nara, 634-8522, Japan
| | - Hideyuki Nishiofuku
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara, Nara, 634-8522, Japan
| | - Nagaaki Marugami
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara, Nara, 634-8522, Japan
| | - Toshihiro Tanaka
- Department of Diagnostic and Interventional Radiology, Nara Medical University, Shijyocho 840, Kashihara, Nara, 634-8522, Japan
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Zhang K, Zhang L, Li WC, Xie SS, Cui YZ, Lin LY, Shen ZW, Zhang HM, Xia S, Ye ZX, He K, Shen W. Radiomics nomogram for the prediction of microvascular invasion of HCC and patients' benefit from postoperative adjuvant TACE: a multi-center study. Eur Radiol 2023; 33:8936-8947. [PMID: 37368104 DOI: 10.1007/s00330-023-09824-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/15/2023] [Accepted: 03/26/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVES To evaluate the performance of a radiomics nomogram developed based on gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA) MRI for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization (PA-TACE). METHODS A total of 260 eligible patients were retrospectively enrolled from three hospitals (140, 65, and 55 in training, standardized external, and non-standardized external validation cohort). Radiomics features and image characteristics were extracted from Gd-EOB-DTPA MRI image before hepatectomy for each lesion. In the training cohort, a radiomics nomogram which incorporated the radiomics signature and radiological predictors was developed. The performance of the radiomics nomogram was assessed with respect to discrimination calibration, and clinical usefulness with external validation. A score (m-score) was constructed to stratify the patients and explored whether it could accurately predict patient who benefit from PA-TACE. RESULTS A radiomics nomogram integrated with the radiomics signature, max-D(iameter) > 5.1 cm, peritumoral low intensity (PTLI), incomplete capsule, and irregular morphology had favorable discrimination in the training cohort (AUC = 0.982), the standardized external validation cohort (AUC = 0.969), and the non-standardized external validation cohort (AUC = 0.981). Decision curve analysis confirmed the clinical usefulness of the novel radiomics nomogram. The log-rank test revealed that PA-TACE significantly decreased the early recurrence in the high-risk group (p = 0.006) with no significant effect in the low-risk group (p = 0.270). CONCLUSIONS The novel radiomics nomogram combining the radiomics signature and clinical radiological features achieved preoperative non-invasive MVI risk prediction and patient benefit assessment after PA-TACE, which may help clinicians implement more appropriate interventions. CLINICAL RELEVANCE STATEMENT Our radiomics nomogram could represent a novel biomarker to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization, which may help clinicians to implement more appropriate interventions and perform individualized precision therapies. KEY POINTS • The novel radiomics nomogram developed based on Gd-EOB-DTPA MRI achieved preoperative non-invasive MVI risk prediction. • An m-score based on the radiomics nomogram could stratify HCC patients and further identify individuals who may benefit from the PA-TACE. • The radiomics nomogram could help clinicians to implement more appropriate interventions and perform individualized precision therapies.
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Affiliation(s)
- Kun Zhang
- Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, 300192, China
| | - Lei Zhang
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China
| | - Wen-Cui Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Shuang-Shuang Xie
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Ying-Zhu Cui
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China
| | - Li-Ying Lin
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Zhi-Wei Shen
- Philips Healthcare, Beijing, The World Profit Centre, No. 16 Tianze Road, Chaoyang District, Beijing, 100125, China
| | - Hui-Mao Zhang
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China
| | - Shuang Xia
- Department of Radiology, Medical Imaging Institute of Tianjin, Tianjin First Central Hospital, School of Medicine, Nankai University, Tianjin, 300192, China
| | - Zhao-Xiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
- Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
| | - Kan He
- Department of Radiology, The First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, 130012, China.
| | - Wen Shen
- Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China.
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Park JW, Lee H, Hong H, Seong J. Efficacy of Radiomics in Predicting Oncologic Outcome of Liver-Directed Combined Radiotherapy in Locally Advanced Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:5405. [PMID: 38001665 PMCID: PMC10670316 DOI: 10.3390/cancers15225405] [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: 10/11/2023] [Revised: 11/05/2023] [Accepted: 11/10/2023] [Indexed: 11/26/2023] Open
Abstract
PURPOSE We investigated whether radiomic features extracted from three-phase dynamic contrast-enhanced computed tomography (CECT) can be used to predict clinical outcomes, including objective treatment response (OR) and in-field failure-free survival rate (IFFR), in patients with hepatocellular carcinoma (HCC) who received liver-directed combined radiotherapy (LD-CRT). METHODS We included 409 patients, and they were randomly divided into training (n = 307) and validation (n = 102) cohorts. For radiomics models, we extracted 116 radiomic features from the region of interest on the CECT images. Significant clinical prognostic factors are identified to predict the OR and IFFR in the clinical models. We developed clinical models, radiomics models, and a combination of both features (CCR model). RESULTS Among the radiomic models evaluated for OR, the OR-PVP-Peri-1cm model showed favorable predictive performance with an area under the curve (AUC) of 0.647. The clinical model showed an AUC of 0.729, whereas the CCR model showed better performance (AUC 0.759). For the IFFR, the IFFR-PVP-Peri-1cm model showed an AUC of 0.673, clinical model showed 0.687, and the CCR model showed 0.736. We also developed and validated a prognostic nomogram based on CCR models. CONCLUSION In predicting the OR and IFFR in patients with HCC undergoing LD-CRT, CCR models performed better than clinical and radiomics models. Moreover, the constructed nomograms based on these models may provide valuable information on the prognosis of these patients.
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Affiliation(s)
- Jong Won Park
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea;
| | - Hansang Lee
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea;
| | - Helen Hong
- Department of Software Convergence, College of Interdisciplinary Studies for Emerging Industries, 621 Hwarang-ro, Nowon-gu, Seoul 01797, Republic of Korea
| | - Jinsil Seong
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea;
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Liu MT, Zhang JY, Xu L, Qu Q, Lu MT, Jiang JF, Zhao XC, Zhang XQ, Zhang T. A multivariate model based on gadoxetic acid-enhanced MRI using Li-RADS v2018 and other imaging features for preoperative prediction of dual‑phenotype hepatocellular carcinoma. LA RADIOLOGIA MEDICA 2023; 128:1333-1346. [PMID: 37740839 DOI: 10.1007/s11547-023-01715-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/25/2023] [Indexed: 09/25/2023]
Abstract
OBJECTIVE To investigate the diagnostic value of liver imaging reporting and data system (LI-RADS) v2018 and other imaging features in dual-phenotype hepatocellular carcinoma (DPHCC), establish a prediagnostic model based on gadoxetic acid-enhanced MRI, and explore the prognostic significance after surgery of the DPHCC. MATERIALS AND METHODS Preoperative enhanced MRI findings and the clinical and pathological data of patients with surgically confirmed HCC were analysed retrospectively. Image analysis was based on LI-RADS v2018 and other image features. Univariate analysis was used to screen for predictive factors of DPHCC, and multivariate logistic regression analysis was used to determine the predictive factors. A regression diagnostic model was established. Receiver operating characteristic (ROC) curve analysis was used to determine the critical value, area under curve (AUC), and the corresponding 95% confidence interval (95% CI). The diagnostic performance was verified by fivefold cross-validation. Cox regression analysis was used to determine the prognostic factors associated with early recurrence after surgical resection. RESULTS In total, 158 patients were included, of whom 79 had DPHCC and 79 had non-DPHCC. Multivariate analysis showed that rim arterial phase hyperenhancement (Rim APHE) and targetoid restriction were independent risk factors for DPHCC (P < 0.05). The AUC (95% CI) of the model was 0.862 (0.807-0.918), sensitivity was 81.01%, and specificity was 89.874%. Cox regression analysis showed that DPHCC, microvascular invasion, tumour diameter, and an increase of alpha-fetoprotein were independent factors for recurrence. CONCLUSION Rim APHE and targetoid restriction were sensitive imaging features of DPHCC before surgery, and the identification of DPHCC has important prognostic significance for early recurrence.
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Affiliation(s)
- Mao-Tong Liu
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Ji-Yun Zhang
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Lei Xu
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Qi Qu
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Meng-Tian Lu
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Ji-Feng Jiang
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China
| | - Xian-Ce Zhao
- Philips Healthcare Shanghai, Shanghai, People's Republic of China
| | - Xue-Qin Zhang
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China.
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China.
| | - Tao Zhang
- Department of Radiology, Nantong Third People's Hospital, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China.
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, #99 Youth Middle Road, Chong chuan District, Nantong, 226000, Jiangsu, China.
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Cannella R, Santinha J, Bèaufrere A, Ronot M, Sartoris R, Cauchy F, Bouattour M, Matos C, Papanikolaou N, Vilgrain V, Dioguardi Burgio M. Performances and variability of CT radiomics for the prediction of microvascular invasion and survival in patients with HCC: a matter of chance or standardisation? Eur Radiol 2023; 33:7618-7628. [PMID: 37338558 DOI: 10.1007/s00330-023-09852-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 03/28/2023] [Accepted: 04/21/2023] [Indexed: 06/21/2023]
Abstract
OBJECTIVES To measure the performance and variability of a radiomics-based model for the prediction of microvascular invasion (MVI) and survival in patients with resected hepatocellular carcinoma (HCC), simulating its sequential development and application. METHODS This study included 230 patients with 242 surgically resected HCCs who underwent preoperative CT, of which 73/230 (31.7%) were scanned in external centres. The study cohort was split into training set (158 patients, 165 HCCs) and held-out test set (72 patients, 77 HCCs), stratified by random partitioning, which was repeated 100 times, and by a temporal partitioning to simulate the sequential development and clinical use of the radiomics model. A machine learning model for the prediction of MVI was developed with least absolute shrinkage and selection operator (LASSO). The concordance index (C-index) was used to assess the value to predict the recurrence-free (RFS) and overall survivals (OS). RESULTS In the 100-repetition random partitioning cohorts, the radiomics model demonstrated a mean AUC of 0.54 (range 0.44-0.68) for the prediction of MVI, mean C-index of 0.59 (range 0.44-0.73) for RFS, and 0.65 (range 0.46-0.86) for OS in the held-out test set. In the temporal partitioning cohort, the radiomics model yielded an AUC of 0.50 for the prediction of MVI, a C-index of 0.61 for RFS, and 0.61 for OS, in the held-out test set. CONCLUSIONS The radiomics models had a poor performance for the prediction of MVI with a large variability in the model performance depending on the random partitioning. Radiomics models demonstrated good performance in the prediction of patient outcomes. CLINICAL RELEVANCE STATEMENT Patient selection within the training set strongly influenced the performance of the radiomics models for predicting microvascular invasion; therefore, a random approach to partitioning a retrospective cohort into a training set and a held-out set seems inappropriate. KEY POINTS • The performance of the radiomics models for the prediction of microvascular invasion and survival widely ranged (AUC range 0.44-0.68) in the randomly partitioned cohorts. • The radiomics model for the prediction of microvascular invasion was unsatisfying when trying to simulate its sequential development and clinical use in a temporal partitioned cohort imaged with a variety of CT scanners. • The performance of the radiomics models for the prediction of survival was good with similar performances in the 100-repetition random partitioning and temporal partitioning cohorts.
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Affiliation(s)
- Roberto Cannella
- Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France
- Section of Radiology-BiND, University Hospital 'Paolo Giaccone', Palermo, Italy
- Department of Health Promotion Sciences Maternal and Infant Care, Internal Medicine and Medical Specialties, PROMISE, University of Palermo, Palermo, Italy
| | - Joao Santinha
- Champalimaud Foundation-Centre for the Unknown, 1400-038, Lisbon, Portugal
| | | | - Maxime Ronot
- Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France
- Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France
| | - Riccardo Sartoris
- Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France
- Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France
| | - Francois Cauchy
- Department of HPB Surgery and Liver Transplantation, Hôpital Beaujon, Clichy, France
| | | | - Celso Matos
- Champalimaud Foundation-Centre for the Unknown, 1400-038, Lisbon, Portugal
| | | | - Valérie Vilgrain
- Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France
- Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France
| | - Marco Dioguardi Burgio
- Department of Radiology, Hôpital Beaujon, 100 Boulevard du Général Leclerc, 92110, Clichy, France.
- Université de Paris, INSERM U1149 'centre de recherche sur l'inflammation', CRI, Paris, France.
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Wang L, Liang M, Feng B, Li D, Cong R, Chen Z, Wang S, Ma X, Zhao X. Microvascular invasion-negative hepatocellular carcinoma: Prognostic value of qualitative and quantitative Gd-EOB-DTPA MRI analysis. Eur J Radiol 2023; 168:111146. [PMID: 37832198 DOI: 10.1016/j.ejrad.2023.111146] [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: 08/16/2023] [Revised: 09/27/2023] [Accepted: 10/06/2023] [Indexed: 10/15/2023]
Abstract
OBJECTIVES The purpose of this study was to establish a model for predicting the prognosis of patients with microvascular invasion (MVI)-negative hepatocellular carcinoma (HCC) based on qualitative and quantitative analyses of Gd-EOB-DTPA magnetic resonance imaging (MRI). MATERIALS AND METHODS Consecutive patients with MVI-negative HCC who underwent preoperative Gd-EOB-DTPA MRI between January 2015 and December 2019 were retrospectively enrolled.In total, 122 patients were randomly assigned to the training and validation groups at a ratio of 7:3. Univariate and multivariate logistic regression analyses were performed to identify significant clinical parameters and MRI features, including quantitative and qualitative parameters associated with prognosis, which were incorporated into a predictive nomogram. The end-point of this study was recurrence-free survival. Outcomes were compared between groups using the Kaplan-Meier method with the log-rank test. RESULTS During a median follow-up period of 58.86 months, 38 patients (31.15 %) experienced recurrence. Multivariate analysis revealed that lower relative enhancement ratio (RER), hepatobiliary phase hypointensity without arterial phase hyperenhancement, Liver Imaging Reporting and Data System category, mild-moderate T2 hyperintensity, and higher aspartate aminotransferase levels were risk factors associated with prognosis and then incorporated into the prognostic model. C-indices for training and validation groups were 0.732 and 0.692, respectively. The most appropriate cut-off value for RER was 1.197. Patients with RER ≤ 1.197 had significantly higher postoperative recurrence rates than those with RER > 1.197 (p = 0.004). CONCLUSION The model integrating qualitative and quantitative imaging parameters and clinical parameters satisfactorily predicted the prognosis of patients with MVI-negative HCC.
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Affiliation(s)
- 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - Sicong Wang
- Sicong Wang, Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing 100176, China
| | - 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.
| | - 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.
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Zhao Y, Zhang J, Wang N, Xu Q, Liu Y, Liu J, Zhang Q, Zhang X, Chen A, Chen L, Sheng L, Song Q, Wang F, Guo Y, Liu A. Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma. BMC Cancer 2023; 23:1026. [PMID: 37875815 PMCID: PMC10594790 DOI: 10.1186/s12885-023-11491-0] [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: 10/21/2022] [Accepted: 10/08/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Noninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients. METHODS A total of 138 patients with HCC who received TACE were retrospectively included and randomly divided into training and validation cohorts at a ratio of 7:3. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. The inter- and intraclass correlation coefficients, the spearman's rank correlation test, and the gradient boosting decision tree algorithm were used for radiomics feature selection. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively. RESULTS The T-PTR radiomics models performed better than the TR and PTR models, and the T-PTR (3 mm) radiomics model demonstrated preferable performance with the AUCs of 0.884 (95%CI, 0.821-0.936) and 0.911 (95%CI, 0.825-0.975) in both training and validation cohorts. The T-PTR (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were fused to construct a combined nomogram. The combined nomogram [AUC: 0.910 (95%CI, 0.854-0.958) and 0.918 (95%CI, 0.831-0.986)] outperformed the clinical-radiological model [AUC: 0.789 (95%CI, 0.709-0.863) and 0.782 (95%CI, 0.660-0.902)] in the both cohorts and achieved good calibration capability and clinical utility. CONCLUSIONS CE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.
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Affiliation(s)
- Ying Zhao
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China
| | - Jian Zhang
- Department of Interventional Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Nan Wang
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China
| | - Qihao Xu
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China
| | - Yuhui Liu
- College of Medical Imaging, Dalian Medical University, Dalian, China
| | - Jinghong Liu
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China
| | - Qinhe Zhang
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China
| | - Xinyuan Zhang
- College of Medical Imaging, Dalian Medical University, Dalian, China
| | - Anliang Chen
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China
| | - Lihua Chen
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China
| | - Liuji Sheng
- College of Medical Imaging, Dalian Medical University, Dalian, China
| | - Qingwei Song
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China
| | - Feng Wang
- Department of Interventional Radiology, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Yan Guo
- GE Healthcare (China), Shanghai, China
| | - Ailian Liu
- Department of Radiology, The First Affiliated Hospital, Dalian Medical University, No. 222 Zhongshan Road, Xigang District, Dalian, Liaoning, China.
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Li J, Su X, Xu X, Zhao C, Liu A, Yang L, Song B, Song H, Li Z, Hao X. Preoperative prediction and risk assessment of microvascular invasion in hepatocellular carcinoma. Crit Rev Oncol Hematol 2023; 190:104107. [PMID: 37633349 DOI: 10.1016/j.critrevonc.2023.104107] [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: 05/24/2023] [Accepted: 08/22/2023] [Indexed: 08/28/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most common and highly lethal tumors worldwide. Microvascular invasion (MVI) is a significant risk factor for recurrence and poor prognosis after surgical resection for HCC patients. Accurately predicting the status of MVI preoperatively is critical for clinicians to select treatment modalities and improve overall survival. However, MVI can only be diagnosed by pathological analysis of postoperative specimens. Currently, numerous indicators in serology (including liquid biopsies) and imaging have been identified to effective in predicting the occurrence of MVI, and the multi-indicator model based on deep learning greatly improves accuracy of prediction. Moreover, several genes and proteins have been identified as risk factors that are strictly associated with the occurrence of MVI. Therefore, this review evaluates various predictors and risk factors, and provides guidance for subsequent efforts to explore more accurate predictive methods and to facilitate the conversion of risk factors into reliable predictors.
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Affiliation(s)
- Jian Li
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Xin Su
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Xiao Xu
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Changchun Zhao
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Ang Liu
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China; Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China
| | - Liwen Yang
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Baoling Song
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Hao Song
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Zihan Li
- The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou 730000, China
| | - Xiangyong Hao
- Department of General Surgery, Gansu Provincial Hospital, Lanzhou 730000, China.
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Zhang N, Wu M, Zhou Y, Yu C, Shi D, Wang C, Gao M, Lv Y, Zhu S. Radiomics nomogram for prediction of glypican-3 positive hepatocellular carcinoma based on hepatobiliary phase imaging. Front Oncol 2023; 13:1209814. [PMID: 37841420 PMCID: PMC10570799 DOI: 10.3389/fonc.2023.1209814] [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: 04/21/2023] [Accepted: 09/12/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction The hepatobiliary-specific phase can help in early detection of changes in lesion tissue density, internal structure, and microcirculatory perfusion at the microscopic level and has important clinical value in hepatocellular carcinoma (HCC). Therefore, this study aimed to construct a preoperative nomogram for predicting the positive expression of glypican-3 (GPC3) based on gadoxetic acid-enhanced (Gd-EOB-DTPA) MRI hepatobiliary phase (HBP) radiomics, imaging and clinical feature. Methods We retrospectively included 137 patients with HCC who underwent Gd-EOB-DTPA-enhanced MRI and subsequent liver resection or puncture biopsy at our hospital from January 2017 to December 2021 as training cohort. Subsequently collected from January 2022 to June 2023 as a validation cohort of 49 patients, Radiomic features were extracted from the entire tumor region during the HBP using 3D Slicer software and screened using a t-test and least absolute shrinkage selection operator algorithm (LASSO). Then, these features were used to construct a radiomics score (Radscore) for each patient, which was combined with clinical factors and imaging features of the HBP to construct a logistic regression model and subsequent nomogram model. The clinicoradiologic, radiomics and nomogram models performance was assessed by the area under the curve (AUC), calibration, and decision curve analysis (DCA). In the validation cohort,the nomogram performance was assessed by the area under the curve (AUC). Results In the training cohort, a total of 1688 radiomics features were extracted from each patient. Next, radiomics with ICCs<0.75 were excluded, 1587 features were judged as stable using intra- and inter-class correlation coefficients (ICCs), 26 features were subsequently screened using the t-test, and 11 radiomics features were finally screened using LASSO. The nomogram combining Radscore, age, serum alpha-fetoprotein (AFP) >400ng/mL, and non-smooth tumor margin (AUC=0.888, sensitivity 77.7%, specificity 91.2%) was superior to the radiomics (AUC=0.822, sensitivity 81.6%, specificity 70.6%) and clinicoradiologic (AUC=0.746, sensitivity 76.7%, specificity 64.7%) models, with good consistency in calibration curves. DCA also showed that the nomogram had the highest net clinical benefit for predicting GPC3 expression.In the validation cohort, the ROC curve results showed predicted GPC3-positive expression nomogram model AUC, sensitivity, and specificity of 0.800, 58.5%, and 100.0%, respectively. Conclusion HBP radiomics features are closely associated with GPC3-positive expression, and combined clinicoradiologic factors and radiomics features nomogram may provide an effective way to non-invasively and individually screen patients with GPC3-positive HCC.
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Affiliation(s)
- Ning Zhang
- Henan Provincial People’s Hospital, Xinxiang Medical University, Xinxiang, China
| | - Minghui Wu
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yiran Zhou
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Changjiang Yu
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Dandan Shi
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Cong Wang
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Miaohui Gao
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yuanyuan Lv
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Shaocheng Zhu
- Henan Provincial People’s Hospital, Xinxiang Medical University, Xinxiang, China
- Department of Medical Imaging, Henan Provincial People’s Hospital, Zhengzhou, China
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Risk stratification of solitary hepatocellular carcinoma ≤ 5 cm without microvascular invasion: prognostic values of MR imaging features based on LI-RADS and clinical parameters. Eur Radiol 2023; 33:3592-3603. [PMID: 36884087 DOI: 10.1007/s00330-023-09484-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/05/2023] [Accepted: 02/07/2023] [Indexed: 03/09/2023]
Abstract
OBJECTIVES To estimate the potential of preoperative MR imaging features and clinical parameters in the risk stratification of patients with solitary hepatocellular carcinoma (HCC) ≤ 5 cm without microvascular invasion (MVI) after hepatectomy. METHODS The study enrolled 166 patients with histopathological confirmed MVI-negative HCC retrospectively. The MR imaging features were evaluated by two radiologists independently. The risk factors associated with recurrence-free survival (RFS) were identified by univariate Cox regression analysis and the least absolute shrinkage and selection operator Cox regression analysis. A predictive nomogram was developed based on these risk factors, and the performance was tested in the validation cohort. The RFS was analyzed by using the Kaplan-Meier survival curves and log-rank test. RESULTS Among the 166 patients with solitary MVI-negative HCC, 86 patients presented with postoperative recurrence. Multivariate Cox regression analysis indicated that cirrhosis, tumor size, hepatitis, albumin, arterial phase hyperenhancement (APHE), washout, and mosaic architecture were risk factors associated with poor RFS and then incorporated into the nomogram. The nomogram achieved good performance with C-index values of 0.713 and 0.707 in the development and validation cohorts, respectively. Furthermore, patients were stratified into high- and low-risk subgroups, and significant prognostic differences were found between the different subgroups in both cohorts (p < 0.001 and p = 0.024, respectively). CONCLUSION The nomogram incorporated preoperative MR imaging features, and clinical parameters can be a simple and reliable tool for predicting RFS and achieving risk stratification in patients with solitary MVI-negative HCC. KEY POINTS • Application of preoperative MR imaging features and clinical parameters can effectively predict RFS in patients with solitary MVI-negative HCC. • Risk factors including cirrhosis, tumor size, hepatitis, albumin, APHE, washout, and mosaic architecture were associated with worse prognosis in patients with solitary MVI-negative HCC. • Based on the nomogram incorporating these risk factors, the MVI-negative HCC patients could be stratified into two subgroups with significant different prognoses.
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Qu Q, Lu M, Xu L, Zhang J, Liu M, Jiang J, Zhao X, Zhang X, Zhang T. A model incorporating histopathology and preoperative gadoxetic acid-enhanced MRI to predict early recurrence of hepatocellular carcinoma without microvascular invasion after curative hepatectomy. Br J Radiol 2023; 96:20220739. [PMID: 36877238 PMCID: PMC10078874 DOI: 10.1259/bjr.20220739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 02/10/2023] [Accepted: 02/14/2023] [Indexed: 03/07/2023] Open
Abstract
OBJECTIVES To assess the predictive value of preoperative gadoxetic acid (GA)-enhanced magnetic resonance imaging (MRI) features and postoperative histopathological grading for early recurrence of hepatocellular carcinoma (HCC) without microvascular invasion (MVI) after curative hepatectomy. METHODS A total of 85 MVI-negative HCC cases were retrospectively analyzed. Cox analyses were used to identify the independent predictors of early recurrence (within a 24 months span). The clinical prediction Model-1 or Model-2 was established without or with postoperative pathological factor, respectively. Nomogram models were constructed and receiver operating characteristic (ROC) curve analysis was used to assess the models' predictive ability. Internal validation of the prediction models for early HCC recurrence was performed using a bootstrap re-sampling approach. RESULTS In the multivariate cox regression analysis, Edmondson-Steiner grade, peritumoral hypointensity on hepatobiliary phase (HBP), and relative intensity ratio (RIR) in HBP were identified as independent variables associated with early recurrence. The C-index of the nomogram models and internal validation were both between 0.7 and 0.8, showing good model fitting and calibration effects. The area under the ROC curve (AUC) was 0.781 for Model-1 based on the two preoperative MRI factors. When a third factor, the Edmondson-Steiner grade, was included (Model-2), the AUC increased to 0.834, and the sensitivity increased from 71.4 to 96.4%. CONCLUSIONS Edmondson-Steiner grade, peritumoral hypointensity on HBP, and RIR on HBP can help predict early recurrence of MVI-negative HCC. In comparison with Model-1 (only imaging features), Model-2 (imaging features + histopathological grades) increases the sensitivity in predicting early recurrence of HCC without MVI. ADVANCES IN KNOWLEDGE Preoperative GA-enhanced MRI signs are of great value in predicting early postoperative recurrence of HCC without MVI, and a combined pathological model was established to evaluate the feasibility and effectiveness of this technique.
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Affiliation(s)
- Qi Qu
- Nantong University, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People’s Hospital, Nantong, 226000, Jiangsu, China
| | - Mengtian Lu
- Nantong University, Nantong, 226000, Jiangsu, China
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People’s Hospital, Nantong, 226000, Jiangsu, China
| | - Lei Xu
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People’s Hospital, Nantong, 226000, Jiangsu, China
| | - Jiyun Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People’s Hospital, Nantong, 226000, Jiangsu, China
| | - Maotong Liu
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People’s Hospital, Nantong, 226000, Jiangsu, China
| | - Jifeng Jiang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People’s Hospital, Nantong, 226000, Jiangsu, China
| | | | - Xueqin Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People’s Hospital, Nantong, 226000, Jiangsu, China
| | - Tao Zhang
- Department of Radiology, Affiliated Nantong Hospital 3 of Nantong University, Nantong Third People’s Hospital, Nantong, 226000, Jiangsu, China
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Park S, Kim JH, Kim J, Joseph W, Lee D, Park SJ. Development of a deep learning-based auto-segmentation algorithm for hepatocellular carcinoma (HCC) and application to predict microvascular invasion of HCC using CT texture analysis: preliminary results. Acta Radiol 2023; 64:907-917. [PMID: 35570797 DOI: 10.1177/02841851221100318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Automatic segmentation has recently been developed to yield objective data. Prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) using radiomics has been reported. PURPOSE To develop a deep learning-based auto-segmentation algorithm (DL-AS) for the detection of HCC and to predict MVI using computed tomography (CT) texture analysis. MATERIAL AND METHODS We retrospectively collected training data from 249 patients with HCC and validation set from 35 patients. Lesions of the training set were manually drawn by radiologist, in the delayed phase. 2D U-Net was selected as the DL architecture. Using the validation set, one radiologist manually drew 2D and 3D regions of interest twice, and the developed DL-AS was performed twice with a one-month time interval. The reproducibility was calculated using intraclass correlation coefficients (ICC). Logistic regression was performed to predict MVI. RESULTS ICC was in the range of 0.190-0.998/0.341-0.997 in the manual 3D/2D segmentation. In contrast, it was perfect in 3D/2D using DL-AS, with a success rate of 88.6% for the detection of HCC. For predicting MVI, sphericity was a significant parameter (odds ratio <0.001; 95% confidence interval <0.001-0.206; P = 0.020) for predicting MVI using 2D DL-AS. However, 3D DL-AS segmentation did not yield a predictive parameter. CONCLUSION The auto-segmentation of HCC using DL-AS provides perfect reproducibility, although it failed to detect 11.4% (4/35). However, the extracted parameters yielded different important predictors of MVI in HCC. Sphericity was a significant predictor in 2D DL-AS and 3D manual segmentation, while discrete compactness was a significant predictor in 2D manual segmentation.
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Affiliation(s)
- Sungeun Park
- Department of Radiology, 119754Konkuk University Medical Center, Seoul, Republic of Korea
| | - Jung Hoon Kim
- Department of Radiology, 58927Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, 37990Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jieun Kim
- Department of Radiology, 58927Seoul National University Hospital, Seoul, Republic of Korea
| | | | - Doohee Lee
- Medical IP Co., Ltd, Seoul, Republic of Korea
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Li Q, Wei Y, Zhang T, Che F, Yao S, Wang C, Shi D, Tang H, Song B. Predictive models and early postoperative recurrence evaluation for hepatocellular carcinoma based on gadoxetic acid-enhanced MR imaging. Insights Imaging 2023; 14:4. [PMID: 36617581 PMCID: PMC9826770 DOI: 10.1186/s13244-022-01359-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 12/17/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND The prognosis of hepatocellular carcinoma (HCC) is still poor largely due to the high incidence of recurrence. We aimed to develop and validate predictive models of early postoperative recurrence for HCC using clinical and gadoxetic acid-enhanced magnetic resonance (MR) imaging-based findings. METHODS In this retrospective case-control study, 209 HCC patients, who underwent gadoxetic acid-enhanced MR imaging before curative-intent resection, were enrolled. Boruta algorithm and backward stepwise selection with Akaike information criterion (AIC) were used for variables selection Random forest, Gradient-Boosted decision tree and logistic regression model analysis were used for model development. The area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis were used to evaluate model's performance. RESULTS One random forest model with Boruta algorithm (RF-Boruta) was developed consisting of preoperative serum ALT and AFP levels and six MRI findings, while preoperative serum AST and AFP levels and four MRI findings were included in one logistic regression model with backward stepwise selection method (Logistic-AIC).The two predictive models demonstrated good discrimination performance in both the training set (RF-Boruta: AUC, 0.820; Logistic-AIC: AUC, 0.853), internal validation set (RF-Boruta: AUC, 0.857, Logistic-AIC: AUC, 0.812) and external validation set(RF-Boruta: AUC, 0.805, Logistic-AIC: AUC, 0.789). Besides, in both the internal validation and external validation sets, the RF-Boruta model outperformed Barcelona Clinic Liver Cancer (BCLC) stage (p < 0.05). CONCLUSIONS The RF-Boruta and Logistic-AIC models with good prediction performance for early postoperative recurrence may lead to optimal and comprehensive treatment approaches, and further improve the prognosis of HCC after resection.
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Affiliation(s)
- Qian Li
- grid.412901.f0000 0004 1770 1022Department of Radiology, Sichuan University, West China Hospital, No. 37, GUOXUE Alley, Chengdu, 610041 Sichuan Province People’s Republic of China
| | - Yi Wei
- grid.412901.f0000 0004 1770 1022Department of Radiology, Sichuan University, West China Hospital, No. 37, GUOXUE Alley, Chengdu, 610041 Sichuan Province People’s Republic of China
| | - Tong Zhang
- grid.412901.f0000 0004 1770 1022Department of Radiology, Sichuan University, West China Hospital, No. 37, GUOXUE Alley, Chengdu, 610041 Sichuan Province People’s Republic of China
| | - Feng Che
- grid.412901.f0000 0004 1770 1022Department of Radiology, Sichuan University, West China Hospital, No. 37, GUOXUE Alley, Chengdu, 610041 Sichuan Province People’s Republic of China
| | - Shan Yao
- grid.412901.f0000 0004 1770 1022Department of Radiology, Sichuan University, West China Hospital, No. 37, GUOXUE Alley, Chengdu, 610041 Sichuan Province People’s Republic of China
| | - Cong Wang
- grid.414011.10000 0004 1808 090XDepartment of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province People’s Republic of China
| | - Dandan Shi
- grid.414011.10000 0004 1808 090XDepartment of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan Province People’s Republic of China
| | - Hehan Tang
- grid.412901.f0000 0004 1770 1022Department of Radiology, Sichuan University, West China Hospital, No. 37, GUOXUE Alley, Chengdu, 610041 Sichuan Province People’s Republic of China
| | - Bin Song
- grid.412901.f0000 0004 1770 1022Department of Radiology, Sichuan University, West China Hospital, No. 37, GUOXUE Alley, Chengdu, 610041 Sichuan Province People’s Republic of China ,Department of Radiology, Sanya People’s Hospital, Sanya, 572000 People’s Republic of China
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Yang X, Yuan C, Zhang Y, Li K, Wang Z. Predicting hepatocellular carcinoma early recurrence after ablation based on magnetic resonance imaging radiomics nomogram. Medicine (Baltimore) 2022; 101:e32584. [PMID: 36596081 PMCID: PMC9803514 DOI: 10.1097/md.0000000000032584] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The aim of this study is to investigate a model for predicting the early recurrence of hepatocellular carcinoma (HCC) after ablation. METHODS A total of 181 patients with HCC after ablation (train group was 119 cases; validation group was 62 cases) were enrolled. The cases of early recurrence in the set of train and validation were 63 and 31, respectively. Radiomics features were extracted from the enhanced magnetic resonance imaging scanning, including pre-contrast injection, arterial phase, late arterial phase, portal venous phase, and delayed phase. The least absolute shrinkage and selection operator cox proportional hazards regression after univariate and multivariate analysis was used to screen radiomics features and build integrated models. The nomograms predicting recurrence and survival of patients of HCC after ablation were established based on the clinical, imaging, and radiomics features. The area under the curve (AUC) of the receiver operating characteristic curve and C-index for the train and validation group was used to evaluate model efficacy. RESULTS Four radiomics features were selected out of 34 texture features to formulate the rad-score. Multivariate analyses suggested that the rad-score, number of lesions, integrity of the capsule, pathological type, and alpha-fetoprotein were independent influencing factors. The AUC of predicting early recurrence at 1, 2, and 3 years in the train group was 0.79 (95% CI: 0.72-0.88), 0.72 (95% CI: 0.63-0.82), and 0.71 (95% CI: 0.61-0.83), respectively. The AUC of predicting early recurrence at 1, 2, and 3 years in the validation group was 0.72 (95% CI: 0.58-0.84), 0.61 (95% CI: 0.45-0.78) and 0.64 (95% CI: 0.40-0.87). CONCLUSION The model for early recurrence of HCC after ablation based on the clinical, imaging, and radiomics features presented good predictive performance. This may facilitate the early treatment of patients.
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Affiliation(s)
- Xiaozhen Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- Department of Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Chunwang Yuan
- Department of Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Yinghua Zhang
- Department of Center of Interventional Oncology and Liver Diseases, Beijing Youan Hospital, Capital Medical University, Beijing, China
| | - Kang Li
- Biomedical Information Center, Beijing You’An Hospital, Capital Medical University, Beijing, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- * Correspondence: Zhenchang Wang, Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95, Yong An Road, Xicheng District, Beijing 100050, China (e-mail: )
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Niu ZS, Wang WH, Niu XJ. Recent progress in molecular mechanisms of postoperative recurrence and metastasis of hepatocellular carcinoma. World J Gastroenterol 2022; 28:6433-6477. [PMID: 36569275 PMCID: PMC9782839 DOI: 10.3748/wjg.v28.i46.6433] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 10/31/2022] [Accepted: 11/21/2022] [Indexed: 12/08/2022] Open
Abstract
Hepatectomy is currently considered the most effective option for treating patients with early and intermediate hepatocellular carcinoma (HCC). Unfortunately, the postoperative prognosis of patients with HCC remains unsatisfactory, predominantly because of high postoperative metastasis and recurrence rates. Therefore, research on the molecular mechanisms of postoperative HCC metastasis and recurrence will help develop effective intervention measures to prevent or delay HCC metastasis and recurrence and to improve the long-term survival of HCC patients. Herein, we review the latest research progress on the molecular mechanisms underlying postoperative HCC metastasis and recurrence to lay a foundation for improving the understanding of HCC metastasis and recurrence and for developing more precise prevention and intervention strategies.
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Affiliation(s)
- Zhao-Shan Niu
- Laboratory of Micromorphology, School of Basic Medicine, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Wen-Hong Wang
- Department of Pathology, School of Basic Medicine, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Xiao-Jun Niu
- Department of Internal Medicine, Qingdao Shibei District People's Hospital, Qingdao 266033, Shandong Province, China
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Fahmy D, Alksas A, Elnakib A, Mahmoud A, Kandil H, Khalil A, Ghazal M, van Bogaert E, Contractor S, El-Baz A. The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14246123. [PMID: 36551606 PMCID: PMC9777232 DOI: 10.3390/cancers14246123] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/08/2022] [Accepted: 12/09/2022] [Indexed: 12/15/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning.
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Affiliation(s)
- Dalia Fahmy
- Diagnostic Radiology Department, Mansoura University Hospital, Mansoura 35516, Egypt
| | - Ahmed Alksas
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
| | - Heba Kandil
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Faculty of Computer Sciences and Information, Mansoura University, Mansoura 35516, Egypt
| | - Ashraf Khalil
- College of Technological Innovation, Zayed University, Abu Dhabi 4783, United Arab Emirates
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates
| | - Eric van Bogaert
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40292, USA
- Correspondence:
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Sim JZT, Hui TCH, Chuah TK, Low HM, Tan CH, Shelat VG. Efficacy of texture analysis of pre-operative magnetic resonance imaging in predicting microvascular invasion in hepatocellular carcinoma. World J Clin Oncol 2022; 13:918-928. [PMID: 36483976 PMCID: PMC9724184 DOI: 10.5306/wjco.v13.i11.918] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/13/2022] [Accepted: 11/04/2022] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND Presence of microvascular invasion (MVI) indicates poorer prognosis post-curative resection of hepatocellular carcinoma (HCC), with an increased chance of tumour recurrence. By present standards, MVI can only be diagnosed post-operatively on histopathology. Texture analysis potentially allows identification of patients who are considered 'high risk' through analysis of pre-operative magnetic resonance imaging (MRI) studies. This will allow for better patient selection, improved individualised therapy (such as extended surgical margins or adjuvant therapy) and pre-operative prognostication. AIM This study aims to evaluate the accuracy of texture analysis on pre-operative MRI in predicting MVI in HCC. METHODS Retrospective review of patients with new cases of HCC who underwent hepatectomy between 2007 and 2015 was performed. Exclusion criteria: No pre-operative MRI, significant movement artefacts, loss-to-follow-up, ruptured HCCs, previous hepatectomy and adjuvant therapy. Fifty patients were divided into MVI (n = 15) and non-MVI (n = 35) groups based on tumour histology. Selected images of the tumour on post-contrast-enhanced T1-weighted MRI were analysed. Both qualitative (performed by radiologists) and quantitative data (performed by software) were obtained. Radiomics texture parameters were extracted based on the largest cross-sectional area of each tumor and analysed using MaZda software. Five separate methods were performed. Methods 1, 2 and 3 exclusively made use of features derived from arterial, portovenous and equilibrium phases respectively. Methods 4 and 5 made use of the comparatively significant features to attain optimal performance. RESULTS Method 5 achieved the highest accuracy of 87.8% with sensitivity of 73% and specificity of 94%. CONCLUSION Texture analysis of tumours on pre-operative MRI can predict presence of MVI in HCC with accuracies of up to 87.8% and can potentially impact clinical management.
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Affiliation(s)
- Jordan Zheng Ting Sim
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Terrence Chi Hong Hui
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Tong Kuan Chuah
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
| | - Hsien Min Low
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
| | - Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
| | - Vishal G Shelat
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 308232, Singapore
- Department of General Surgery, Tan Tock Seng Hospital, Singapore 308433, Singapore
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Zhou W, Lv Y, Hu X, Luo Y, Li J, Zhu H, Hai Y. Study on the changes of CT texture parameters before and after HCC treatment in the efficacy evaluation and survival predication of patients with HCC. Front Oncol 2022; 12:957737. [PMID: 36387217 PMCID: PMC9650244 DOI: 10.3389/fonc.2022.957737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 10/17/2022] [Indexed: 11/26/2022] Open
Abstract
Objective To investigate texture parameters of contrast-enhanced computed tomography (CT) images before and after transarterial chemoembolization (TACE) as a tool for assessing the therapeutic response and survival predication in hepatocellular carcinoma (HCC). Materials and methods Data of 77 HCC patients who underwent three-phase dynamic contrast-enhanced CT examination within 4 weeks before and 4–8 weeks after TACE were collected and efficacy evaluation was performed according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST) standard. The remission group consisted of 31 patients (12 with complete remission+19 with partial remission), while the non-remission group consisted of 46 patients (27 with stable disease+19 with progressive disease). Full-volume manual delineation of the region of interest (ROI) and texture analysis of the ROI were performed on the CT images using FireVoxel software. Changes in the 48 texture parameters from three-phase CT images before and after TACE were calculated and compared between the two groups. The receiver operating characteristic (ROC) curve and the areas under the curve (AUC) were used to analyze the diagnostic performance of texture parameters. A multifactorial Cox model was used for predicting survival. The C-indices of texture parameter difference values with predictive value, texture features model, and texture features combined with mRECIST in predicting OS were compared with those of mRECIST. Results A total of 41 changes in texture parameters were statistically significant between the remission and non-remission groups. The receiver operating characteristic (ROC) curve showed that the AUC of changes in the 90th percentile in the arterial phase was the largest at 0.842. When the cut-off value was 70.50, the Youden index was the largest (0.621), and the sensitivity and specificity were 0.710 and 0.911, respectively. Three changes in texture parameters were independent factors associated with patient survival, with a hazard of 0.173, 2.068, and 1.940, respectively. The C-index of the OS predicted by the texture features model was not statistically different from that of the mRECIST (0.695 vs. 0.668, p=0.493). While the C-indices of skewness in the portal venous phase combined with mRECIST (0.729, p=0.015), skewness in the delayed phase combined with mRECIST (0.715, p=0.044), and skewness in both two phases combined with mRECIST (0.728, p=0.017) were statistically different. Conclusion Changes in the texture parameters of CT images before and after TACE treatment can be used to obtain relevant grayscale histogram parameters for evaluating the early efficacy of TACE in HCC treatment. And the texture analysis combined with mRECIST may be superior to the mRECIST alone in predicting survival in HCC after TACE treatment.
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Affiliation(s)
- Wei Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yinzhang Lv
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- *Correspondence: Yinzhang Lv,
| | - Xuemei Hu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yan Luo
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiali Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Haidan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yucheng Hai
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China
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Zhang K, Li WC, Xie SS, Lin LY, Shen ZW, Ye ZX, Shen W. Preoperative determination of pathological grades of primary single HCC: development and validation of a scoring model. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3468-3477. [PMID: 35842888 DOI: 10.1007/s00261-022-03606-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 06/24/2022] [Accepted: 06/24/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE This study aimed to establish a reliable diagnostic score model for the preoperative determination of pathological grade in HCC based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI and biochemical indicators. METHODS In this retrospective study, we analyzed 139 patients with HCC who underwent Gd-EOB-DTPA MRI between 2014 and 2020, including an establishment cohort of 76 patients and a validation cohort of 63 patients. Based on the imaging features demonstrated on Gd-EOB-DTPA MRI images and biochemical indicators of the establishment cohort, a scoring model based on logistic regression was developed, and compared with postoperative pathological findings in terms of effective determination of pathological grade. The validity of the scoring model was assessed by ROC curves and an independent external validation cohort. RESULTS Three parameters related to pathological grades were identified, including maximum diameter of the tumor, peritumoral hypointensity in the hepatobiliary phase, and [alkaline phosphatase (U/L) + gamma glutamyl transpeptidase (U/L)]/ lymphocyte count (× 109/L) (AGLR) ratios. Based on these three parameters, a scoring model was developed. ROC curve showed that a score of > 5 was set as the threshold for determining pathological grades with accuracy, sensitivity, specificity, PPV, and NPV of 89.5%, 75.0%, 95.1%, 85.7%, and 90.7%, respectively. CONCLUSION The study provided the groundwork for a promising and easily implementable scoring model for preoperative determination of HCC pathological grades, for which further validation should be pursued.
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Affiliation(s)
- Kun Zhang
- Department of Radiology, Tianjin First Central Hospital, funded by Tianjin Key Medical Discipline (Specialty) Construction Project, Tianjin Institute of imaging medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Wen-Cui Li
- Department of Radiology, Liver Cancer Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
| | - Shuang-Shuang Xie
- Department of Radiology, Tianjin First Central Hospital, funded by Tianjin Key Medical Discipline (Specialty) Construction Project, Tianjin Institute of imaging medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China
| | - Li-Ying Lin
- First Central Clinical College, Tianjin Medical University, 22 Qixiangtai Road, Heping District, Tianjin, 300070, China
| | - Zhi-Wei Shen
- Philips Healthcare, Beijing, The world profit centre, No. 16 Tianze Road, Chaoyang Dustrict, Beijing, 100125, China
| | - Zhao-Xiang Ye
- Department of Radiology, Liver Cancer Center, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, 300060, China. .,Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China.
| | - Wen Shen
- Department of Radiology, Tianjin First Central Hospital, funded by Tianjin Key Medical Discipline (Specialty) Construction Project, Tianjin Institute of imaging medicine, 24 Fukang Road, Nankai District, Tianjin, 300192, China.
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Lu XY, Zhang JY, Zhang T, Zhang XQ, Lu J, Miao XF, Chen WB, Jiang JF, Ding D, Du S. Using pre-operative radiomics to predict microvascular invasion of hepatocellular carcinoma based on Gd-EOB-DTPA enhanced MRI. BMC Med Imaging 2022; 22:157. [PMID: 36057576 PMCID: PMC9440540 DOI: 10.1186/s12880-022-00855-w] [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: 02/23/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
Objectives We aimed to investigate the value of performing gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) radiomics for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on multiple sequences. Methods We randomly allocated 165 patients with HCC who underwent partial hepatectomy to training and validation sets. Stepwise regression and the least absolute shrinkage and selection operator algorithm were used to select significant variables. A clinicoradiological model, radiomics model, and combined model were constructed using multivariate logistic regression. The performance of the models was evaluated, and a nomogram risk-prediction model was built based on the combined model. A concordance index and calibration curve were used to evaluate the discrimination and calibration of the nomogram model. Results The tumour margin, peritumoural hypointensity, and seven radiomics features were selected to build the combined model. The combined model outperformed the radiomics model and the clinicoradiological model and had the highest sensitivity (90.89%) in the validation set. The areas under the receiver operating characteristic curve were 0.826, 0.755, and 0.708 for the combined, radiomics, and clinicoradiological models, respectively. The nomogram model based on the combined model exhibited good discrimination (concordance index = 0.79) and calibration. Conclusions The combined model based on radiomics features of Gd-EOB-DTPA enhanced MRI, tumour margin, and peritumoural hypointensity was valuable for predicting HCC microvascular invasion. The nomogram based on the combined model can intuitively show the probabilities of MVI. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00855-w.
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Affiliation(s)
- Xin-Yu Lu
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China.,The First People's Hospital of Taicang, Taicang, Suzhou, Jiangsu, China
| | - Ji-Yun Zhang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Tao Zhang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China.
| | - Xue-Qin Zhang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China.
| | - Jian Lu
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Xiao-Fen Miao
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | | | - Ji-Feng Jiang
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Ding Ding
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
| | - Sheng Du
- Department of Radiology, Nantong Third Hospital Affiliated to Nantong University, #60 Youth Middle Road, Chongchuan District, Nantong, Jiangsu, China
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Wang L, Feng B, Wang S, Hu J, Liang M, Li D, Wang S, Ma X, Zhao X. Diagnostic value of whole-tumor apparent diffusion coefficient map radiomics analysis in predicting early recurrence of solitary hepatocellular carcinoma ≤ 5 cm. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3290-3300. [PMID: 35776146 DOI: 10.1007/s00261-022-03582-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 06/05/2022] [Accepted: 06/06/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To evaluate the role of whole-tumor radiomics analysis of apparent diffusion coefficient (ADC) maps in predicting early recurrence (ER) of solitary hepatocellular carcinoma (HCC) ≤ 5 cm and compare the diagnostic efficiency of whole-tumor and single-slice ADC measurements. METHODS One hundred and seventy patients with primary HCC were randomly divided into the training set (n = 119) and the test set (n = 51). The diagnostic efficiency was compared between the whole-tumor and single-slice ADC measurements. The clinical-radiological model was established by selected significant clinical characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. The significant clinical-radiological risk factors and radiomics features were integrated to develop the combined model. Receiver operating characteristic (ROC) curves were used for evaluating the predictive performance. RESULTS Cirrhosis, age, and albumin were significantly associated with ER in the clinical-radiological model selected by the random forest classifier. The diagnostic efficiency of the whole-tumor ADC measurements was slight higher than that of the single-slice (AUC = 0.602 and 0.586, respectively). The clinical-radiological model (AUC = 0.84 and 0.82 in the training and test sets, respectively) showed better diagnostic performance than the radiomics model (AUC = 0.70 and 0.69 in the training and test sets, respectively) in predicting ER. The combined model showed optimal predictive performance with the highest AUC values of 0.88 and 0.85 in the training and test sets, respectively. CONCLUSIONS The whole-tumor ADC measurements performed better than the single-slice ADC measurements. The clinical-radiological model performed better than the radiomics model for predicting ER in patients with solitary HCC ≤ 5 cm.
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Affiliation(s)
- 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
| | - 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
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare (China), Beijing, 100176, China
| | - Jiesi Hu
- Institute of Electronical and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, 518055, China
| | - 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
| | - 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
| | - 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
| | - 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.
| | - 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.
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Zhang S, Huo L, Zhang J, Feng Y, Liu Y, Wu Y, Jia N, Liu W. A preoperative model based on gadobenate-enhanced MRI for predicting microvascular invasion in hepatocellular carcinomas (≤ 5 cm). Front Oncol 2022; 12:992301. [PMID: 36110937 PMCID: PMC9470230 DOI: 10.3389/fonc.2022.992301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/05/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose The present study aimed to develop and validate a preoperative model based on gadobenate-enhanced magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) size of ≤5 cm. In order to provide preoperative guidance for clinicians to optimize treatment options. Methods 164 patients with pathologically confirmed HCC and preoperative gadobenate-enhanced MRI from July 2016 to December 2020 were retrospectively included. Univariate and multivariate logistic regression (forward LR) analyses were used to determine the predictors of MVI and the model was established. Four-fold cross validation was used to verify the model, which was visualized by nomograms. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical utility. Results Elevated alpha-fetoprotein (HR 1.849, 95% CI: 1.193, 2.867, P=0.006), atypical enhancement pattern (HR 3.441, 95% CI: 1.523, 7.772, P=0.003), peritumoral hypointensity on HBP (HR 7.822, 95% CI: 3.317, 18.445, P<0.001), and HBP hypointensity (HR 3.258, 95% CI: 1.381, 7.687, P=0.007) were independent risk factors to MVI and constituted the HBP model. The mean area under the curve (AUC), sensitivity, specificity, and accuracy values for the HBP model were as follows: 0.830 (95% CI: 0.784, 0.876), 0.71, 0.78, 0.81 in training set; 0.826 (95% CI:0.765, 0.887), 0.8, 0.7, 0.79 in test set. The decision curve analysis (DCA) curve showed that the HBP model achieved great clinical benefits. Conclusion In conclusion, the HBP imaging features of Gd-BOPTA-enhanced MRI play an important role in predicting MVI for HCC. A preoperative model, mainly based on HBP imaging features of gadobenate-enhanced MRI, was able to excellently predict the MVI for HCC size of ≤5cm. The model may help clinicians preoperatively assess the risk of MVI in HCC patients so as to guide clinicians to optimize treatment options.
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Affiliation(s)
- Sisi Zhang
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Lei Huo
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Juan Zhang
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yayuan Feng
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yiping Liu
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Yuxian Wu
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
| | - Ningyang Jia
- Department of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, The Third Affiliated Hospital of Naval Medical University, Shanghai, China
- *Correspondence: Ningyang Jia, ; Wanmin Liu,
| | - Wanmin Liu
- Department of Radiology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
- *Correspondence: Ningyang Jia, ; Wanmin Liu,
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Deng PZ, Zhao BG, Huang XH, Xu TF, Chen ZJ, Wei QF, Liu XY, Guo YQ, Yuan SG, Liao WJ. Preoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma. World J Gastroenterol 2022; 28:4376-4389. [PMID: 36159012 PMCID: PMC9453776 DOI: 10.3748/wjg.v28.i31.4376] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/14/2022] [Accepted: 07/22/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement.
AIM To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy.
METHODS A total of 150 HCC patients were randomly divided into a training cohort (n = 107) and a validation cohort (n = 43). Radiomics features were extracted from the entire tumour lesion. The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature. Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram, incorporating clinicopathological characteristics and the radiomics signature. The accuracy of the nomogram was assessed with the concordance index, receiver operating characteristic (ROC) curve and calibration curve. The clinical utility was evaluated by decision curve analysis (DCA). Kaplan–Meier methodology was used to compare the survival between the low- and high-risk subgroups.
RESULTS In total, seven radiomics features were selected to construct the radiomics signature. According to the results of univariate and multivariate Cox regression analyses, alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR) and radiomics signature were included to build the nomogram. The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774, respectively. ROC curve analysis for predicting 1-, 3-, and 5-year OS confirmed satisfactory accuracy [training cohort, area under the curve (AUC) = 0.850, 0.791 and 0.823, respectively; validation cohort, AUC = 0.905, 0.884 and 0.911, respectively]. The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival. DCA curves suggested that the nomogram had more benefit than traditional staging system models. Kaplan–Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival (all P < 0.0001).
CONCLUSION The nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.
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Affiliation(s)
- Peng-Zhan Deng
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Bi-Geng Zhao
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Xian-Hui Huang
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Ting-Feng Xu
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Zi-Jun Chen
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Qiu-Feng Wei
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Xiao-Yi Liu
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Yu-Qi Guo
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Sheng-Guang Yuan
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
| | - Wei-Jia Liao
- Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin 541001, Guangxi Zhuang Autonomous Region, China
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Detecting and monitoring tumors in orthotopic colorectal liver metastatic animal models with high-resolution ultrasound. Clin Exp Metastasis 2022; 39:771-781. [PMID: 35918622 DOI: 10.1007/s10585-022-10177-4] [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: 03/01/2022] [Accepted: 06/09/2022] [Indexed: 11/03/2022]
Abstract
The ability to noninvasively detect and monitor the growth of orthotopic liver transplantation tumors is critical for replicating advanced colorectal cancer liver metastases (CRLMs) in animal models. We assessed the use of high-resolution ultrasound (HRU) to monitor CRLMs transplanted using various cell concentrations. Sixty BALB/c female mice were randomly divided into 3 groups, and murine colonic CT26 cells were injected into the left liver lobe at concentrations of 1 × 102 (group 1), 1 × 103 (group 2), or 1 × 104 (group 3). Tumor presentation, location, number, size, shape, and echogenicity were assessed daily with 24-MHz center frequency HRU starting 6 days after injection. Animals were sacrificed when the largest tumor was ≥ 1 cm in diameter. Sensitivity, specificity, and area under curve (AUC) of CRLMs diagnosed with HRU were calculated using receiver operating characteristic curve analysis. In group 1, 94% of mice formed < 5 tumors, and 41% formed a single tumor. Tumors were first detected with HRU on day 12 in group 1, day 10 in group 2, and day 7 in group 3; tumor volume doubling times were 14-15 days, 11-12 days, and 7-8 days, respectively. With a long diameter threshold of 2.4 mm, diagnostic sensitivity and specificity of HRU were 94.1% and 88.7%, respectively, and the AUC was 0.962. These findings suggest that HRU can be used to accurately detect and monitor the growth of CRLMs in an orthotopic transplantation mouse model, especially when a lower concentration of cells is used.
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Wang L, Ma X, Feng B, Wang S, Liang M, Li D, Wang S, Zhao X. Multi-Sequence MR-Based Radiomics Signature for Predicting Early Recurrence in Solitary Hepatocellular Carcinoma ≤5 cm. Front Oncol 2022; 12:899404. [PMID: 35756618 PMCID: PMC9213728 DOI: 10.3389/fonc.2022.899404] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 05/17/2022] [Indexed: 01/27/2023] Open
Abstract
Purpose To investigate the value of radiomics features derived from preoperative multi-sequence MR images for predicting early recurrence (ER) in patients with solitary hepatocellular carcinoma (HCC) ≤5 cm. Methods One hundred and ninety HCC patients were enrolled and allocated to training and validation sets (n = 133:57). The clinical–radiological model was established by significant clinical risk characteristics and qualitative imaging features. The radiomics model was constructed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm in the training set. The combined model was formed by integrating the clinical–radiological risk factors and selected radiomics features. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC). Results Arterial peritumoral hyperenhancement, non-smooth tumor margin, satellite nodules, cirrhosis, serosal invasion, and albumin showed a significant correlation with ER. The AUC of the clinical–radiological model was 0.77 (95% CI: 0.69–0.85) and 0.76 (95% CI: 0.64–0.88) in the training and validation sets, respectively. The radiomics model constructed using 12 radiomics features selected by LASSO regression had an AUC of 0.85 (95% CI: 0.79–0.91) and 0.84 (95% CI: 0.73–0.95) in the training and validation sets, respectively. The combined model further improved the prediction performance compared with the clinical–radiological model, increasing AUC to 0.90 (95% CI: 0.85–0.95) in the training set and 0.88 (95% CI: 0.80–0.97) in the validation set (p < 0.001 and p = 0.012, respectively). The calibration curve fits well with the standard curve. Conclusions The predictive model incorporated the clinical–radiological risk factors and radiomics features that could adequately predict the individualized ER risk in patients with solitary HCC ≤5 cm.
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Affiliation(s)
- 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, China
| | - 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, China
| | - 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, China
| | - 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, China
| | - 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, China
| | - 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, China
| | - Sicong Wang
- Magnetic Resonance Imaging Research, General Electric Healthcare, Beijing, China
| | - 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, China
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Chu T, Zhao C, Zhang J, Duan K, Li M, Zhang T, Lv S, Liu H, Wei F. Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels that Encapsulate Tumor Clusters in Hepatocellular Carcinoma. Ann Surg Oncol 2022; 29:6774-6783. [PMID: 35754067 PMCID: PMC9492610 DOI: 10.1245/s10434-022-12000-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022]
Abstract
Background Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer death worldwide, and the prognosis remains dismal. In this study, two pivotal factors, microvascular invasion (MVI) and vessels encapsulating tumor clusters (VETC) were preoperatively predicted simultaneously to assess prognosis. Methods A total of 133 HCC patients who underwent surgical resection and preoperative gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) were included. The statuses of MVI and VETC were obtained from the pathological report and CD34 immunohistochemistry, respectively. A three-dimensional convolutional neural network (3D CNN) for single-task learning aimed at MVI prediction and for multitask learning aimed at simultaneous prediction of MVI and VETC was established by using multiphase Gd-EOB-DTPA-enhanced MRI. Results The 3D CNN for single-task learning achieved an area under receiver operating characteristics curve (AUC) of 0.896 (95% CI: 0.797–0.994). Multitask learning with simultaneous extraction of MVI and VETC features improved the performance of MVI prediction, with an AUC value of 0.917 (95% CI: 0.825–1.000), and achieved an AUC value of 0.860 (95% CI: 0.728–0.993) for the VETC prediction. The multitask learning framework could stratify high- and low-risk groups regarding overall survival (p < 0.0001) and recurrence-free survival (p < 0.0001), revealing that patients with MVI+/VETC+ were associated with poor prognosis. Conclusions A deep learning framework based on 3D CNN for multitask learning to predict MVI and VETC simultaneously could improve the performance of MVI prediction while assessing the VETC status. This combined prediction can stratify prognosis and enable individualized prognostication in HCC patients before curative resection. Supplementary Information The online version contains supplementary material available at 10.1245/s10434-022-12000-6.
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Affiliation(s)
- Tongjia Chu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Chen Zhao
- College of Computer Science and Technology, Jilin University, Changchun, People's Republic of China
| | - Jian Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Kehang Duan
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Mingyang Li
- Department of Radiology, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Tianqi Zhang
- Department of Radiology, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Shengnan Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Huan Liu
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China
| | - Feng Wei
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, People's Republic of China.
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Wu Y, Zhu M, Liu Y, Cao X, Zhang G, Yin L. Peritumoral Imaging Manifestations on Gd-EOB-DTPA-Enhanced MRI for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:907076. [PMID: 35814461 PMCID: PMC9263828 DOI: 10.3389/fonc.2022.907076] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
PURPOSE The aim was to investigate the association between microvascular invasion (MVI) and the peritumoral imaging features of gadolinium ethoxybenzyl DTPA-enhanced magnetic resonance imaging (Gd-EOB-DTPA-enhanced MRI) in hepatocellular carcinoma (HCC). METHODS Up until Feb 24, 2022, the PubMed, Embase, and Cochrane Library databases were carefully searched for relevant material. The software packages utilized for this meta-analysis were Review Manager 5.4.1, Meta-DiSc 1.4, and Stata16.0. Summary results are presented as sensitivity (SEN), specificity (SPE), diagnostic odds ratios (DORs), area under the receiver operating characteristic curve (AUC), and 95% confidence interval (CI). The sources of heterogeneity were investigated using subgroup analysis. RESULTS An aggregate of nineteen articles were remembered for this meta-analysis: peritumoral enhancement on the arterial phase (AP) was described in 13 of these studies and peritumoral hypointensity on the hepatobiliary phase (HBP) in all 19 studies. The SEN, SPE, DOR, and AUC of the 13 investigations on peritumoral enhancement on AP were 0.59 (95% CI, 0.41-0.58), 0.80 (95% CI, 0.75-0.85), 4 (95% CI, 3-6), and 0.73 (95% CI, 0.69-0.77), respectively. The SEN, SPE, DOR, and AUC of 19 studies on peritumoral hypointensity on HBP were 0.55 (95% CI, 0.45-0.64), 0.87 (95% CI, 0.81-0.91), 8 (95% CI, 5-12), and 0.80 (95% CI, 0.76-0.83), respectively. The subgroup analysis of two imaging features identified ten and seven potential factors for heterogeneity, respectively. CONCLUSION The results of peritumoral enhancement on the AP and peritumoral hypointensity on HBP showed high SPE but low SEN. This indicates that the peritumoral imaging features on Gd-EOB-DTPA-enhanced MRI can be used as a noninvasive, excluded diagnosis for predicting hepatic MVI in HCC preoperatively. Moreover, the results of this analysis should be updated when additional data become available. Additionally, in the future, how to improve its SEN will be a new research direction.
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Affiliation(s)
- Ying Wu
- Department of Radiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Meilin Zhu
- Department of Radiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yiming Liu
- Department of Radiology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xinyue Cao
- Department of Radiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Guojin Zhang
- Department of Radiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Longlin Yin
- Department of Radiology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
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Tan CH, Chou SC, Inmutto N, Ma K, Sheng R, Shi Y, Zhou Z, Yamada A, Tateishi R. Gadoxetate-Enhanced MRI as a Diagnostic Tool in the Management of Hepatocellular Carcinoma: Report from a 2020 Asia-Pacific Multidisciplinary Expert Meeting. Korean J Radiol 2022; 23:697-719. [PMID: 35555884 PMCID: PMC9240294 DOI: 10.3348/kjr.2021.0593] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 02/21/2022] [Accepted: 03/17/2022] [Indexed: 12/04/2022] Open
Abstract
Gadoxetate magnetic resonance imaging (MRI) is widely used in clinical practice for liver imaging. For optimal use, we must understand both its advantages and limitations. This article is the outcome of an online advisory board meeting and subsequent discussions by a multidisciplinary group of experts on liver diseases across the Asia-Pacific region, first held on September 28, 2020. Here, we review the technical considerations for the use of gadoxetate, its current role in the management of patients with hepatocellular carcinoma (HCC), and its relevance in consensus guidelines for HCC imaging diagnosis. In the latter part of this review, we examine recent evidence evaluating the impact of gadoxetate on clinical outcomes on a continuum from diagnosis to treatment decision-making and follow-up. In conclusion, we outline the potential future roles of gadoxetate MRI based on an evolving understanding of the clinical utility of this contrast agent in the management of patients at risk of, or with, HCC.
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Affiliation(s)
- Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore.,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
| | - Shu-Cheng Chou
- Division of General Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei City & Institute of Clinical Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Nakarin Inmutto
- Department of Radiology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Ke Ma
- Department of Infectious Disease, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - RuoFan Sheng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Medical Imaging, Shanghai, China
| | - YingHong Shi
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhongguo Zhou
- Department of Hepatobiliary Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Japan
| | - Ryosuke Tateishi
- Department of Gastroenterology, The University of Tokyo Hospital, Tokyo, Japan
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Zhao QY, Liu SS, Fan MX. Prediction of early recurrence of hepatocellular carcinoma after resection based on Gd-EOB-DTPA enhanced magnetic resonance imaging: a preliminary study. J Gastrointest Oncol 2022; 13:792-801. [PMID: 35557582 PMCID: PMC9086065 DOI: 10.21037/jgo-22-224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/11/2022] [Indexed: 08/10/2023] Open
Abstract
BACKGROUND Early recurrence (ER) after radical resection of hepatocellular carcinoma (HCC) affects the prognosis of patients. Gadolinium ethoxybenzyl-diethylenetriaminepentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) can improve the detection rate of small HCC. This study innovatively introduces a new quantitative index combined with qualitative index to compare the differences in clinical and imaging characteristics between ER and non-ER groups and evaluate the feasibility of Gd-EOB-DTPA-enhanced MRI in predicting ER. METHODS A total of 68 patients with HCC confirmed by operation and pathology in the Shandong Cancer Hospital and Institute were included retrospectively. All participants were examined by Gd-EOB-DTPA-enhanced MRI within 3 weeks before surgery. Regular follow-up was performed every 2 months within 1 year after operation. Among them, 18 cases with new lesions were in ER group, and 50 cases without new lesions were in non-ER group. The clinical and imaging data of the 2 groups were collected, and the differences of clinical data and preoperative MRI signs between the ER group and non-ER group were compared. The predictive factors of ER after HCC were analyzed by multivariate logistic regression. RESULTS The quantitative parameter lesion-to-liver contrast enhancement ratio (LLCER) can predict the pathological grade of HCC (P=0.023). The results of univariate analysis between the ER group and non-ER group showed that there were significant differences in pathological grade (P=0.008), lesion morphology (P=0.011), peritumoral low signal intensity in hepatobiliary phase (HBP) (P<0.001), satellite nodules (P<0.001), and LLCER (P<0.001) between the 2 groups. Multivariate logistic regression analysis showed that HBP peritumoral low signal intensity [odds ratio (OR) =7.214, 95% confidence interval (CI): 1.230-42.312, P=0.029], satellite nodules (OR =9.198, 95% CI: 1.402-60.339, P=0.021), and parameter LLCER value (OR =0.906, 95% CI: 0.826-0.995, P=0.039) were independent predictors of ER of HCC after resection. CONCLUSIONS Preoperative Gd-EOB-DTPA enhanced MRI has important predictive value for early recurrence after radical resection of hepatocellular carcinoma.
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Affiliation(s)
- Qi-Yu Zhao
- Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Shi-Shun Liu
- Medical Imaging Department, Jinan Second People’s Hospital, Jinan, China
| | - Ming-Xin Fan
- Department of Radiology, The Third Affiliated Hospital of Shandong First Medical University (Affiliated Hospital of Shandong Academy of Medical Sciences), Jinan, China
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He Y, Hu B, Zhu C, Xu W, Ge Y, Hao X, Dong B, Chen X, Dong Q, Zhou X. A Novel Multimodal Radiomics Model for Predicting Prognosis of Resected Hepatocellular Carcinoma. Front Oncol 2022; 12:745258. [PMID: 35321432 PMCID: PMC8936674 DOI: 10.3389/fonc.2022.745258] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 02/04/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To explore a new model to predict the prognosis of liver cancer based on MRI and CT imaging data. METHODS A retrospective study of 103 patients with histologically proven hepatocellular carcinoma (HCC) was conducted. Patients were randomly divided into training (n = 73) and validation (n = 30) groups. A total of 1,217 radiomics features were extracted from regions of interest on CT and MR images of each patient. Univariate Cox regression, Spearman's correlation analysis, Pearson's correlation analysis, and least absolute shrinkage and selection operator Cox analysis were used for feature selection in the training set, multivariate Cox proportional risk models were established to predict disease-free survival (DFS) and overall survival (OS), and the models were validated using validation cohort data. Multimodal radiomics scores, integrating CT and MRI data, were applied, together with clinical risk factors, to construct nomograms for individualized survival assessment, and calibration curves were used to evaluate model consistency. Harrell's concordance index (C-index) values were calculated to evaluate the prediction performance of the models. RESULTS The radiomics score established using CT and MR data was an independent predictor of prognosis (DFS and OS) in patients with HCC (p < 0.05). Prediction models illustrated by nomograms for predicting prognosis in liver cancer were established. Integrated CT and MRI and clinical multimodal data had the best predictive performance in the training and validation cohorts for both DFS [(C-index (95% CI): 0.858 (0.811-0.905) and 0.704 (0.563-0.845), respectively)] and OS [C-index (95% CI): 0.893 (0.846-0.940) and 0.738 (0.575-0.901), respectively]. The calibration curve showed that the multimodal radiomics model provides greater clinical benefits. CONCLUSION Multimodal (MRI/CT) radiomics models can serve as effective visual tools for predicting prognosis in patients with liver cancer. This approach has great potential to improve treatment decisions when applied for preoperative prediction in patients with HCC.
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Affiliation(s)
- Ying He
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Bin Hu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Chengzhan Zhu
- Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Wenjian Xu
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | | | - Xiwei Hao
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Bingzi Dong
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xin Chen
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Qian Dong
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
- Shandong College Collaborative Innovation Center of Digital Medicine Clinical Treatment and Nutrition Health, Qingdao University, Qingdao, China
| | - Xianjun Zhou
- Department of Pediatric Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
- Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China
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Huang H, Ruan SM, Xian MF, Li MD, Cheng MQ, Li W, Huang Y, Xie XY, Lu MD, Kuang M, Wang W, Hu HT, Chen LD. Contrast-enhanced ultrasound-based ultrasomics score: a potential biomarker for predicting early recurrence of hepatocellular carcinoma after resection or ablation. Br J Radiol 2022; 95:20210748. [PMID: 34797687 PMCID: PMC8822579 DOI: 10.1259/bjr.20210748] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
OBJECTIVES This study aimed to construct a prediction model based on contrast-enhanced ultrasound (CEUS) ultrasomics features and investigate its efficacy in predicting early recurrence (ER) of primary hepatocellular carcinoma (HCC) after resection or ablation. METHODS This study retrospectively included 215 patients with primary HCC, who were divided into a developmental cohort (n = 139) and a test cohort (n = 76). Four representative images-grayscale ultrasound, arterial phase, portal venous phase and delayed phase-were extracted from each CEUS video. Ultrasomics features were extracted from tumoral and peritumoral area inside the region of interest. Logistic regression was used to establish models, including a tumoral model, a peritumoral model and a combined model with additional clinical risk factors. The performance of the three models in predicting recurrence within 2 years was verified. RESULTS The combined model performed best in predicting recurrence within 2 years, with an area under the curve (AUC) of 0.845, while the tumoral model had an AUC of 0.810 and the peritumoral model one of 0.808. For prediction of recurrence-free survival, the 2-year cumulative recurrence rate was significant higher in the high-risk group (76.5%) than in the low-risk group (9.5%; p < 0.0001). CONCLUSION These CEUS ultrasomics models, especially the combined model, had good efficacy in predicting early recurrence of HCC. The combined model has potential for individual survival assessment for HCC patients undergoing resection or ablation. ADVANCES IN KNOWLEDGE CEUS ultrasomics had high sensitivity, specificity and PPV in diagnosing early recurrence of HCC, and high efficacy in predicting early recurrence of HCC (AUC > 0.8). The combined model performed better than the tumoral ultrasomics model and peritumoral ultrasomics model in predicting recurrence within 2 years. Recurrence was more likely to occur in the high-risk group than in the low-risk group, with 2-year cumulative recurrence rates, respectively, 76.5% and 9.5% (p < 0.0001).
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Affiliation(s)
- Hui Huang
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Si-min Ruan
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Meng-fei Xian
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming-de Li
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Mei-qing Cheng
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wei Li
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yang Huang
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiao-yan Xie
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | | | | | - Wei Wang
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hang-tong Hu
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Li-Da Chen
- Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
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Mao Y, Wang J, Zhu Y, Chen J, Mao L, Kong W, Qiu Y, Wu X, Guan Y, He J. Gd-EOB-DTPA-enhanced MRI radiomic features for predicting histological grade of hepatocellular carcinoma. Hepatobiliary Surg Nutr 2022; 11:13-24. [PMID: 35284527 PMCID: PMC8847875 DOI: 10.21037/hbsn-19-870] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 05/11/2020] [Indexed: 01/03/2023]
Abstract
BACKGROUND Prediction models for the histological grade of hepatocellular carcinoma (HCC) remain unsatisfactory. The purpose of this study is to develop preoperative models to predict histological grade of HCC based on gadolinium-ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) radiomics. And to compare the performance between artificial neural network (ANN) and logistic regression model. METHODS A total of 122 HCCs were randomly assigned to the training set (n=85) and the test set (n=37). There were 242 radiomic features extracted from volumetric of interest (VOI) of arterial and hepatobiliary phases images. The radiomic features and clinical parameters [gender, age, alpha-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), alanine aminotransferase (ALT), aspartate transaminase (AST)] were selected by permutation test and decision tree. ANN of arterial phase (ANN-AP), logistic regression model of arterial phase (LR-AP), ANN of hepatobiliary phase (ANN-HBP), logistic regression mode of hepatobiliary phase (LR-HBP), ANN of combined arterial and hepatobiliary phases (ANN-AP + HBP), and logistic regression model of combined arterial and hepatobiliary phases (LR-AP + HBP) were built to predict HCC histological grade. Those prediction models were assessed and compared. RESULTS ANN-AP and LR-AP were composed by AST and radiomic features based on arterial phase. ANN-HBP and LR-HBP were composed by AFP and radiomic features based on hepatobiliary phase. ANN-AP + HBP and LR-AP + HBP were composed by AST and radiomic features based on arterial and hepatobiliary phases. The prediction models could distinguish between high-grade tumors [Edmondson-Steiner (E-S) grade III and IV] and low-grade tumors (E-S grade I and II) in both training set and test set. In the test set, the AUCs of ANN-AP, LR-AP, ANN-HBP, LR-HBP, ANN-AP + HBP and LR-AP + HBP were 0.889, 0.777, 0.941, 0.819, 0.944 and 0.792 respectively. The ANN-HBP was significantly superior to LR-HBP (P=0.001). And the ANN-AP + HBP was significantly superior to LR-AP + HBP (P=0.007). CONCLUSIONS Prediction models consisting of clinical parameters and Gd-EOB-DTPA-enhanced MRI radiomic features (based on arterial phase, hepatobiliary phase, and combined arterial and hepatobiliary phases) could distinguish between high-grade HCCs and low-grade HCCs. And the ANN was superior to logistic regression model in predicting histological grade of HCC.
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Affiliation(s)
- Yingfan Mao
- Department of Radiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Jincheng Wang
- Department of Hepatobiliary Surgery, Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Yong Zhu
- Department of Radiology, Jiangsu Province Hospital of Traditional Chinese Medicine, the Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Jun Chen
- Department of Pathology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Liang Mao
- Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Weiwei Kong
- Department of Oncology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Yudong Qiu
- Department of Hepatopancreatobiliary Surgery, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Xiaoyan Wu
- Department of Radiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Yue Guan
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jian He
- Department of Radiology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China
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Beaufrère A, Caruso S, Calderaro J, Poté N, Bijot JC, Couchy G, Cauchy F, Vilgrain V, Zucman-Rossi J, Paradis V. Gene expression signature as a surrogate marker of microvascular invasion on routine hepatocellular carcinoma biopsies. J Hepatol 2022; 76:343-352. [PMID: 34624411 DOI: 10.1016/j.jhep.2021.09.034] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 09/13/2021] [Accepted: 09/16/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Microvascular invasion (MVI), a major risk factor for tumor recurrence after surgery in hepatocellular carcinoma (HCC), is only detectable by microscopic examination of the surgical specimen. We aimed to define a transcriptomic signature associated with MVI in HCC than can be applied to formalin-fixed paraffin-embedded (FFPE) biopsies for use in clinical practice. METHODS To identify a gene expression signature related to MVI by using NanoString technology, we selected a set of 200 genes according to the literature and RNA-sequencing data obtained from a cohort of 150 frozen HCC samples previously published. We used 178 FFPE-archived HCC samples, including 109 surgical samples for the training set and 69 paired pre-operative biopsies for the validation set. In 14 cases of the training set, a paired biopsy was available and was also analyzed. RESULTS We identified a 6-gene signature (ROS1, UGT2B7, FAS, ANGPTL7, GMNN, MKI67) strongly associated with MVI in the training set of FFPE surgical HCC samples, with 82% accuracy (sensitivity 82%, specificity 81%, AUC 0.82). The NanoString gene expression was highly correlated in 14 paired surgical/biopsy HCC samples (mean R: 0.97). In the validation set of 69 FFPE HCC biopsies, the 6-gene NanoString signature predicted MVI with 74% accuracy (sensitivity 73%, specificity 76%, AUC 0.74). Moreover, on multivariate analysis, the MVI signature was associated with overall survival in both sets (hazard ratio 2.29; 95% CI 1.03-5.07; p = 0.041). CONCLUSION We defined a 6-gene signature that can accurately predict MVI in FFPE HCC biopsy samples, which is also associated with overall survival, although its survival impact must be confirmed by extensive study with further clinical data. LAY SUMMARY Microvascular invasion, a major risk factor for tumor recurrence after surgery in hepatocellular carcinoma, is only detectable by microscopic examination of a surgical specimen. In this study, we defined a relevant surrogate signature of microvascular invasion in hepatocellular carcinoma that may be applied in clinical practice with routine tumor biopsy and integrated into the therapeutic strategy.
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Affiliation(s)
- Aurélie Beaufrère
- Université de Paris, Paris, France; APHP, Department of Pathology, Hôpital Beaujon, 100 boulevard du Général Leclerc, Clichy, 92110, France; INSERM UMR 1149, Centre de Recherche sur l'Inflammation, 16 rue Henri Huchard, Paris, 75018, France
| | - Stefano Caruso
- Centre de Recherche des Cordeliers, INSERM, Functional Genomics of Solid Tumors laboratory, F-75006 Paris, France
| | - Julien Calderaro
- Department of Pathology, Hôpital Henri Mondor, AP-HP, 51 Avenue du Maréchal de Lattre de Tassigny, Créteil, 94010, France
| | - Nicolas Poté
- Université de Paris, Paris, France; Department of Pathology, Hôpital Bichat, AP-HP.Nord, 46 Rue Henri Huchard, Paris, 75018, France
| | - Jean-Charles Bijot
- Université de Paris, Paris, France; Department of Radiology, Hôpital Beaujon, AP-HP, 100 boulevard du Général Leclerc, Clichy, 92110, France
| | - Gabielle Couchy
- Université de Paris, Paris, France; Centre de Recherche des Cordeliers, INSERM, Functional Genomics of Solid Tumors laboratory, F-75006 Paris, France
| | - François Cauchy
- Université de Paris, Paris, France; INSERM UMR 1149, Centre de Recherche sur l'Inflammation, 16 rue Henri Huchard, Paris, 75018, France; Department of HPB and Pancreatic surgery, Beaujon AP-HP, Clichy, 92110, France
| | - Valérie Vilgrain
- Université de Paris, Paris, France; INSERM UMR 1149, Centre de Recherche sur l'Inflammation, 16 rue Henri Huchard, Paris, 75018, France; Department of Radiology, Hôpital Beaujon, AP-HP, 100 boulevard du Général Leclerc, Clichy, 92110, France
| | - Jessica Zucman-Rossi
- Université de Paris, Paris, France; Centre de Recherche des Cordeliers, INSERM, Functional Genomics of Solid Tumors laboratory, F-75006 Paris, France; Department of Oncology, Hopital Européen Georges Pompidou, AP-HP, F-75015, Paris, France
| | - Valérie Paradis
- Université de Paris, Paris, France; APHP, Department of Pathology, Hôpital Beaujon, 100 boulevard du Général Leclerc, Clichy, 92110, France; INSERM UMR 1149, Centre de Recherche sur l'Inflammation, 16 rue Henri Huchard, Paris, 75018, France.
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Microvascular invasion of small hepatocellular carcinoma can be preoperatively predicted by the 3D quantification of MRI. Eur Radiol 2022; 32:4198-4209. [DOI: 10.1007/s00330-021-08495-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/11/2021] [Accepted: 11/29/2021] [Indexed: 12/16/2022]
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Prediction of microvascular invasion in HCC by a scoring model combining Gd-EOB-DTPA MRI and biochemical indicators. Eur Radiol 2022; 32:4186-4197. [DOI: 10.1007/s00330-021-08502-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 11/15/2021] [Accepted: 12/01/2021] [Indexed: 12/17/2022]
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Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021; 54:890-901. [PMID: 34390014 PMCID: PMC8435007 DOI: 10.1111/apt.16563] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/08/2021] [Accepted: 07/25/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology. AIMS To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation. METHODS We performed a systematic review of all full-text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies. RESULTS A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c-statistics 0.66-0.95), and to predict microvascular invasion (c-statistic 0.76-0.92), early recurrence after hepatectomy (c-statistics 0.71-0.86), and prognosis after locoregional or systemic therapies (c-statistics 0.74-0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard. CONCLUSIONS Quantitative image analysis demonstrates promise as a non-invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.
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Affiliation(s)
- Emily Harding-Theobald
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Jeremy Louissaint
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Bharat Maraj
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Edward Cuaresma
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Whitney Townsend
- Division of Library Sciences, University of Michigan, Ann Arbor, MI, USA
| | | | - Amit G Singal
- Division of Digestive and Liver Diseases, University of Texas Southwestern, Dallas, TX, USA
| | - Grace L Su
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Anna S Lok
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
| | - Neehar D Parikh
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Michigan Health System, Ann Arbor, MI, USA
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Gong XQ, Tao YY, Wu Y, Liu N, Yu X, Wang R, Zheng J, Liu N, Huang XH, Li JD, Yang G, Wei XQ, Yang L, Zhang XM. Progress of MRI Radiomics in Hepatocellular Carcinoma. Front Oncol 2021; 11:698373. [PMID: 34616673 PMCID: PMC8488263 DOI: 10.3389/fonc.2021.698373] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/31/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively. OBJECTIVE This study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC. METHODS A literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis. RESULTS Radiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients. CONCLUSION Radiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.
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Affiliation(s)
- Xue-Qin Gong
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yun-Yun Tao
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Yao–Kun Wu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ning Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xi Yu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Ran Wang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jing Zheng
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Nian Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Hua Huang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Jing-Dong Li
- Department of Hepatocellular Surgery, Institute of Hepato-Biliary-Intestinal Disease, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Gang Yang
- Department of Hepatocellular Surgery, Institute of Hepato-Biliary-Intestinal Disease, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Qin Wei
- School of Medical Imaging, North Sichuan Medical College, Nanchong, China
| | - Lin Yang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiao-Ming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
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Deng Y, Yang D, Xu H, Ren A, Yang Z. Diagnostic performance of imaging features in the HBP of gadoxetate disodium-enhanced MRI for microvascular invasion in hepatocellular carcinoma: a meta-analysis. Acta Radiol 2021; 63:1303-1314. [PMID: 34459669 DOI: 10.1177/02841851211038806] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Microvascular invasion (MVI) is a major risk factor for early recurrence in patients with hepatocellular carcinoma (HCC). Preoperative accurate evaluation of the presence of MVI could enormously benefit its treatment and prognosis. PURPOSE To evaluate and compare the diagnostic performance of two imaging features (non-smooth tumor margin and peritumor hypointensity) in the hepatobiliary phase (HBP) to preoperatively diagnose the presence of MVI in HCC. MATERIAL AND METHODS Original articles were collected from Medline/PubMed, Web of Science, EMBASE, and the Cochrane Library up to 17 January 2021 linked to gadoxetate disodium-enhanced magnetic resonance imaging (MRI) on 1.5 or 3.0 T. The pooled sensitivity, specificity, and summary area under the receiver operating characteristic curve (AUC) were calculated and meta-regression analyses were performed. RESULTS A total of 14 original articles involving 2193 HCCs were included. The pooled sensitivity and specificity of non-smooth tumor margin and peritumor hypointensity were 73% and 61%, and 43% and 90%, respectively, for the diagnosis of MVI in HCC. The summary AUC of non-smooth tumor margin (0.74) was comparable to that of peritumor hypointensity (0.76) (z = 0.693, P = 0.488). The meta-regression analysis identified four covariates as possible sources of heterogeneity: average size; time interval between index test and reference test; blindness to index test during reference test; and risk of bias score. CONCLUSION This meta-analysis showed moderate and comparable accuracy for predicting MVI in HCC using either non-smooth tumor margin or peritumor hypointensity in HBP. Four discovered covariates accounted for the heterogeneity.
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Affiliation(s)
- Yuhui Deng
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, PR China
- Medical Imaging Division, Heilongjiang Provincial Hospital, Harbin Institute of Technology, Harbin, PR China
- Equal contributors
| | - Dawei Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, PR China
- Equal contributors
| | - Hui Xu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, PR China
| | - Ahong Ren
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, PR China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, PR China
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Feng B, Ma XH, Wang S, Cai W, Liu XB, Zhao XM. Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives. World J Gastroenterol 2021; 27:5341-5350. [PMID: 34539136 PMCID: PMC8409162 DOI: 10.3748/wjg.v27.i32.5341] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/15/2021] [Accepted: 07/27/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor in China. Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions. Artificial intelligence (AI), such as machine learning (ML) and deep learning, has recently gained attention for its capability to reveal quantitative information on images. Currently, AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine. This review summarizes the applications of AI in various aspects of preoperative imaging of HCC, including segmentation, differential diagnosis, prediction of histopathology, early detection of recurrence after curative treatment, and evaluation of treatment response. We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.
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Affiliation(s)
- 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
| | - Xiao-Hong 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
| | - 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
| | - 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
| | - Xia-Bi Liu
- Beijing Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Xin-Ming 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
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