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Ma H, Wang L, Sun L, Wang S, Lu L, Zhang C, He Y, Zhu Y. Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma From Multi-Sequence Magnetic Resonance Imaging Based on Deep Fusion Representation Learning. IEEE J Biomed Health Inform 2025; 29:3259-3271. [PMID: 39196745 DOI: 10.1109/jbhi.2024.3451331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/30/2024]
Abstract
Recent studies have identified microvascular invasion (MVI) as the most vital independent biomarker associated with early tumor recurrence. With advancements in medical technology, several computational methods have been developed to predict preoperative MVI using diverse medical images. These existing methods rely on human experience, attribute selection or clinical trial testing, which is often time-consuming and labor-intensive. Leveraging the advantages of deep learning, this study presents a novel end-to-end algorithm for predicting MVI prior to surgery. We devised a series of data preprocessing strategies to fully extract multi-view features from the data while preserving peritumoral information. Notably, a new multi-branch deep fused feature algorithm based on ResNet (DFFResNet) is introduced, which combines Magnetic Resonance Images (MRI) from different sequences to enhance information complementarity and integration. We conducted prediction experiments on a dataset from the Radiology Department of the First Hospital of Lanzhou University, comprising 117 individuals and seven MRI sequences. The model was trained on 80% of the data using 10-fold cross-validation, and the remaining 20% were used for testing. This evaluation was processed in two cases: CROI, containing samples with a complete region of interest (ROI), and PROI, containing samples with a partial ROI region. The robustness results from repeated experiments at both image and patient levels demonstrate the superior performance and improved generalization of the proposed method compared to alternative models. Our approach yields highly competitive prediction results even when the ROI region outline is incomplete, offering a novel and effective multi-sequence fused strategy for predicting preoperative MVI.
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Li L, Wang S, Chen J, Wu C, Chen Z, Ye F, Zhou X, Zhang X, Li J, Zhou J, Lu Y, Su Z. Radiomics Diagnosis of Microvascular Invasion in Hepatocellular Carcinoma Using 3D Ultrasound and Whole-Slide Image Fusion. SMALL METHODS 2025; 9:e2401617. [PMID: 40200669 DOI: 10.1002/smtd.202401617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2024] [Revised: 03/16/2025] [Indexed: 04/10/2025]
Abstract
This study aims to develop a machine learning model that accurately diagnoses microvascular invasion (MVI) in hepatocellular carcinoma by using radiomic features from MVI-positive regions of interest (ROIs). Unlike previous studies, which do not account for the location and distribution of MVI, this research focuses on correlating preoperative imaging with postoperative pathological MVI. This study involves obtaining ex vivo 3D ultrasound images of 36 hepatic specimens from nine rabbits. These images are fused with whole-slide images to localize MVI regions precisely. The identified MVI regions are segmented into MVI-positive ROIs, with a 1:3 ratio of positive to negative ROIs. Radiomic features are extracted from each ROI, and 30 features highly associated with MVI are selected for model development. The performance of several machine learning models is evaluated using metrics such as sensitivity, specificity, accuracy, the area under the curve (AUC), and F1 score. The GBDT model achieves the best results, with an AUC of 0.91, an F1 score of 0.85, a sensitivity of 0.76, a specificity of 0.92, and an accuracy of 0.86. The high diagnostic accuracy of these models highlights the potential for future clinical application in the precise diagnosis of MVI using radiomic features from MVI-positive ROIs.
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Affiliation(s)
- Liujun Li
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
- Department of Ultrasound, The First Affiliated Hospital of University of South China, No. 69 Chuanshan Rd, Hengyang, 421000, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
| | - Shaodong Wang
- School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-Sen University, No 132 Waihuan East Road, Guangzhou, 510006, China
| | - Jiaxin Chen
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
| | - Chaoqun Wu
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
| | - Ziman Chen
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
| | - Feile Ye
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
| | - Xuan Zhou
- Department of Pathology, The Fifth Affiliated Hospital of Sun Yat-sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
| | - Xiaoli Zhang
- Department of Pathology, The First Affiliated Hospital of University of South China, No. 69 Chuanshan Rd, Hengyang, 421000, China
| | - Jianping Li
- Department of Pathology, The First Affiliated Hospital of University of South China, No. 69 Chuanshan Rd, Hengyang, 421000, China
| | - Jia Zhou
- Department of Ultrasound, The First Affiliated Hospital of University of South China, No. 69 Chuanshan Rd, Hengyang, 421000, China
| | - Yao Lu
- School of Computer Science and Engineering, Guangdong Province Key Laboratory of Computational Science, Sun Yat-Sen University, No 132 Waihuan East Road, Guangzhou, 510006, China
| | - Zhongzhen Su
- Department of Ultrasound, The Fifth Affiliated Hospital of Sun Yat-Sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
- Guangdong Provincial Key Laboratory of Biomedical Imaging and Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital of Sun Yat-sen University, No. 52 Meihua Rd, Zhuhai, 519000, China
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Dong M, Chen F, Huang W, Liao Y, Li W, Wang X, Luo S. Multiregional Radiomics to Predict Microvascular Invasion in Hepatocellular Carcinoma Using Multisequence MRI. J Comput Assist Tomogr 2025:00004728-990000000-00442. [PMID: 40165029 DOI: 10.1097/rct.0000000000001752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2025]
Abstract
OBJECTIVES This study aimed to develop a multiregional radiomics-based model using multisequence MRI to predict microvascular invasion in hepatocellular carcinoma. METHODS We enrolled 141 patients with hepatocellular carcinoma, including 61 with microvascular invasion, who were diagnosed between March 2017 and July 2022. Clinical data were compared using the Wilcoxon rank-sum test or χ2 test. Patients were randomly divided into training (n=112, 80%) and test (n=29, 20%) data sets. Four MRI sequences-including T2-weighted imaging, T2-weighted imaging with fat suppression, arterial phase-contrast enhancement, and portal venous phase contrast enhancement-were used to build the radiomics model. The tumor volumes of interest were manually delineated, and the expand-5 mm and expand-10 mm volumes of interest were automatically generated. A total of 1409 radiomic features were extracted from each volume of interest. Feature selection was performed using the least absolute shrinkage and selection operator and Spearman correlation analysis. Three logistic regression models (Tumor, Tumor-Expand5, and Tumor-Expand10) were established based on the radiomic features. Model performance was assessed using receiver operating characteristic analysis and Delong's test. RESULTS Maximum tumor diameter, hepatitis B virus DNA, and aspartate aminotransferase levels were significantly different between the groups. The Tumor-Expand5mm model exhibited the best performance among the 3 models, with areas under the curve of 0.90 and 0.84 in the training and test data sets. CONCLUSIONS The Tumor-Expand5 model based on multisequence MRI shows great potential for predicting microvascular invasion in patients with hepatocellular carcinoma, and may further contribute to personal clinical decision-making.
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Affiliation(s)
- Mengying Dong
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
| | - Yuting Liao
- Department of Clinical and Technical Support, Philips (China) Investment Co, Ltd, Haizhu District, Guangzhou, P.R. China
| | - Wenzhu Li
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
| | - Xiaoyi Wang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
| | - Shishi Luo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan
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Zhao L, Wang J, Song J, Zhang F, Liu J. Combining serum biomarkers and MRI radiomics to predict treatment outcome after thermal ablation in hepatocellular carcinoma. Am J Transl Res 2025; 17:2031-2043. [PMID: 40226017 PMCID: PMC11982863 DOI: 10.62347/tfrf1430] [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: 02/27/2024] [Accepted: 02/09/2025] [Indexed: 04/15/2025]
Abstract
OBJECTIVE To investigate the predictive value of serum alpha - fetoprotein (AFP), lectin-reactive alpha-fetoprotein (AFP-L3), and multimodal magnetic resonance imaging (MRI) radiomics in forecasting therapeutic efficacy and prognosis following radiofrequency ablation (RFA) in patients with hepatocellular carcinoma (HCC). METHODS A retrospective analysis was conducted on HCC patients who underwent RFA between January 2019 and December 2023. Clinical and radiologic features of HCC were analyzed. A predictive model was developed using clinical data and radiomic features collected before surgery, with the goal of predicting prognosis after RFA. The predictive performance of the model was evaluated using AUC values in both training and validation cohorts. RESULTS A total of 298 HCC patients were included in the study, divided into a good prognosis group (n=145) and a poor prognosis group (n=153). Serum AFP and AFP-L3 levels were significantly higher in the poor prognosis group (P=0.007 and P=0.02, respectively). Independent predictive factors included: AFP-L3 (95% CI -1.228, -1.1.61; P<0.001), AFP (95% CI 0.017, 0.036; P<0.001), intratumoral hemorrhage (95% CI 0.380, 0.581; P<0.001), peritumoral arterial tumor enhancement (95% CI 0.193, 0.534; P<0.001) and low signal intensity around liver and gallbladder tumors (95% CI 0.267, 0.489; P<0.001). The combined clinical-radiological-radiomics model demonstrated superior predictive performance, with AUC value of 0.897 in the training set and 0.841 in the validation set, outperforming individual models and sequences. CONCLUSION The integrated clinical-radiological-radiomics model showed excellent predictive performance for the prognosis of HCC patients undergoing RFA, surpassing individual models. Key predictors included serum AFP, AFP-L3 levels, intratumoral hemorrhage, and peritumoral low signal intensity. This multimodal approach offers a promising tool for individualized prognostic assessment and improved clinical decision-making.
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Affiliation(s)
- Ludong Zhao
- Jinzhou Medical University Postgraduate Training Base of Linyi People’s HospitalLinyi 276000, Shandong, P. R. China
- Department of General Surgery Center, Linyi People’s HospitalLinyi 276000, Shandong, P. R. China
| | - Jing Wang
- Department of Radiology, Linyi People’s HospitalLinyi 276000, Shandong, P. R. China
| | - Jinna Song
- Jinzhou Medical University Postgraduate Training Base of Linyi People’s HospitalLinyi 276000, Shandong, P. R. China
| | - Fenghua Zhang
- Department of Operating Room, Linyi People’s HospitalLinyi 276000, Shandong, P. R. China
| | - Jinghua Liu
- Jinzhou Medical University Postgraduate Training Base of Linyi People’s HospitalLinyi 276000, Shandong, P. R. China
- Department of General Surgery Center, Linyi People’s HospitalLinyi 276000, Shandong, P. R. China
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Wang L, Xu HX, Wang R, Zhang F, Deng D, Zhu X, Tan Q, Yang H. Advances in multi-omics studies of microvascular invasion in hepatocellular carcinoma. Eur J Med Res 2025; 30:165. [PMID: 40075448 PMCID: PMC11905518 DOI: 10.1186/s40001-025-02421-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2024] [Accepted: 03/01/2025] [Indexed: 03/14/2025] Open
Abstract
Microvascular invasion (MVI) represents a pivotal independent prognostic factor for the recurrence of hepatocellular carcinoma (HCC) after surgery. It contributes to early intervention for potentially recurrent HCC to enhance patient outcomes and increase survival rates. Traditionally, the diagnosis of MVI has relied on postoperative pathological analysis, and accurate preoperative detection methodologies are lacking. Recent research suggests that multi-omics strategies play a role in definitively diagnosing MVI before surgery and offering personalized selection for clinical decision-making in HCC management. This review meticulously examines a multi-omics approach for the preoperative prediction of MVI in HCC patients, aiming to innovate diagnostic paradigms to anticipate postsurgical recurrence, thereby facilitating earlier and more personalized therapeutic strategies.
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Affiliation(s)
- Lili Wang
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China.
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China.
| | - Han Xin Xu
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Rui Wang
- State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People's Republic of China
| | - Fachang Zhang
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Diandian Deng
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Xiaoyang Zhu
- Second Clinical Medical School of Lanzhou University, Lanzhou, 730000, China
| | - Qi Tan
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Heng Yang
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
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Huang Y, Qian H. Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis. J Hepatocell Carcinoma 2024; 11:2159-2168. [PMID: 39525830 PMCID: PMC11546143 DOI: 10.2147/jhc.s493227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2024] [Accepted: 10/25/2024] [Indexed: 11/16/2024] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary liver cancer and is associated with high mortality rates due to late detection and aggressive progression. Peritumoral radiomics, an emerging technique that quantitatively analyzes the tissue surrounding the tumor, has shown significant potential in enhancing the management of HCC. This paper examines the role of peritumoral radiomics in improving diagnostic accuracy, guiding personalized treatment strategies, and refining prognostic assessments. By offering unique insights into the tumor microenvironment, peritumoral radiomics enables more precise patient stratification and informs clinical decision-making. However, the integration of peritumoral radiomics into routine clinical practice faces several challenges. Addressing these challenges through continued research and innovation is crucial for the successful implementation of peritumoral radiomics in HCC management, ultimately leading to improved patient outcomes.
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Affiliation(s)
- Yanhua Huang
- Department of Ultrasound, Shaoxing People’s Hospital, Shaoxing, People’s Republic of China
| | - Hongwei Qian
- Department of Hepatobiliary and Pancreatic Surgery, Shaoxing People’s Hospital, Shaoxing, People’s Republic of China
- Shaoxing Key Laboratory of Minimally Invasive Abdominal Surgery and Precise Treatment of Tumor, Shaoxing, People’s Republic of China
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Wei G, Fang G, Guo P, Fang P, Wang T, Lin K, Liu J. Preoperative prediction of microvascular invasion risk in hepatocellular carcinoma with MRI: peritumoral versus tumor region. Insights Imaging 2024; 15:188. [PMID: 39090456 PMCID: PMC11294513 DOI: 10.1186/s13244-024-01760-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Accepted: 06/23/2024] [Indexed: 08/04/2024] Open
Abstract
OBJECTIVES To explore the predictive performance of tumor and multiple peritumoral regions on dynamic contrast-enhanced magnetic resonance imaging (MRI), to identify optimal regions of interest for developing a preoperative predictive model for the grade of microvascular invasion (MVI). METHODS A total of 147 patients who were surgically diagnosed with hepatocellular carcinoma, and had a maximum tumor diameter ≤ 5 cm were recruited and subsequently divided into a training set (n = 117) and a testing set (n = 30) based on the date of surgery. We utilized a pre-trained AlexNet to extract deep learning features from seven different regions of the maximum transverse cross-section of tumors in various MRI sequence images. Subsequently, an extreme gradient boosting (XGBoost) classifier was employed to construct the MVI grade prediction model, with evaluation based on the area under the curve (AUC). RESULTS The XGBoost classifier trained with data from the 20-mm peritumoral region showed superior AUC compared to the tumor region alone. AUC values consistently increased when utilizing data from 5-mm, 10-mm, and 20-mm peritumoral regions. Combining arterial and delayed-phase data yielded the highest predictive performance, with micro- and macro-average AUCs of 0.78 and 0.74, respectively. Integration of clinical data further improved AUCs values to 0.83 and 0.80. CONCLUSION Compared with those of the tumor region, the deep learning features of the peritumoral region provide more important information for predicting the grade of MVI. Combining the tumor region and the 20-mm peritumoral region resulted in a relatively ideal and accurate region within which the grade of MVI can be predicted. CLINICAL RELEVANCE STATEMENT The 20-mm peritumoral region holds more significance than the tumor region in predicting MVI grade. Deep learning features can indirectly predict MVI by extracting information from the tumor region and directly capturing MVI information from the peritumoral region. KEY POINTS We investigated tumor and different peritumoral regions, as well as their fusion. MVI predominantly occurs in the peritumoral region, a superior predictor compared to the tumor region. The peritumoral 20 mm region is reasonable for accurately predicting the three-grade MVI.
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Affiliation(s)
- Guangya Wei
- Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China
| | - Guoxu Fang
- Department of Hepatopancreatobiliary Surgery, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Pengfei Guo
- Southeast Big Data Institute of Hepatobiliary Health, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, China
| | - Peng Fang
- Department of Radiology, Henan Province Hospital of TCM, Zhengzhou, China
| | - Tongming Wang
- Department of Radiology, Henan Province Hospital of TCM, Zhengzhou, China
| | - Kecan Lin
- Department of Hepatopancreatobiliary Surgery, First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Jingfeng Liu
- Department of Hepatopancreatobiliary Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China.
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Wang F, Numata K, Funaoka A, Liu X, Kumamoto T, Takeda K, Chuma M, Nozaki A, Ruan L, Maeda S. Establishment of nomogram prediction model of contrast-enhanced ultrasound and Gd-EOB-DTPA-enhanced MRI for vessels encapsulating tumor clusters pattern of hepatocellular carcinoma. Biosci Trends 2024; 18:277-288. [PMID: 38866488 DOI: 10.5582/bst.2024.01112] [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] [Indexed: 06/14/2024]
Abstract
To establish clinical prediction models of vessels encapsulating tumor clusters (VETC) pattern using preoperative contrast-enhanced ultrasound (CEUS) and gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid magnetic resonance imaging (EOB-MRI) in patients with hepatocellular carcinoma (HCC). A total of 111 resected HCC lesions from 101 patients were included. Preoperative imaging features of CEUS and EOB-MRI, postoperative recurrence, and survival information were collected from medical records. The best subset regression and multivariable Cox regression were used to select variables to establish the prediction model. The VETC-positive group had a statistically lower survival rate than the VETC-negative group. The selected variables were peritumoral enhancement in the arterial phase (AP), hepatobiliary phase (HBP) on EOB-MRI, intratumoral branching enhancement in the AP of CEUS, intratumoral hypoenhancement in the portal phase of CEUS, incomplete capsule, and tumor size. A nomogram was developed. High and low nomogram scores with a cutoff value of 168 points showed different recurrence-free survival rates and overall survival rates. The area under the curve (AUC) and accuracy were 0.804 and 0.820, respectively, indicating good discrimination. Decision curve analysis showed a good clinical net benefit (threshold probability > 5%), while the Hosmer-Lemeshow test yielded excellent calibration (P = 0.6759). The AUC of the nomogram model combining EOB-MRI and CEUS was higher than that of the models with EOB-MRI factors only (0.767) and CEUS factors only (0.7). The nomogram verified by bootstrapping showed AUC and calibration curves similar to those of the nomogram model. The Prediction model based on CEUS and EOB-MRI is effective for preoperative noninvasive diagnosis of VETC.
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Affiliation(s)
- Feiqian Wang
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan
| | - Kazushi Numata
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan
| | - Akihiro Funaoka
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan
| | - Xi Liu
- Department of Pathology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Takafumi Kumamoto
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan
| | - Kazuhisa Takeda
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan
| | - Makoto Chuma
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan
| | - Akito Nozaki
- Gastroenterological Center, Yokohama City University Medical Center, Yokohama, Kanagawa, Japan
| | - Litao Ruan
- Department of Ultrasound, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Shin Maeda
- Division of Gastroenterology, Yokohama City University Graduate School of Medicine, Yokohama, Kanagawa, Japan
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Wen J, Wang X, Xia M, Wei B, Yang H, Hou Y. Radiomics features based on dual-area CT predict the expression levels of fatty acid binding protein 4 and outcome in hepatocellular carcinoma. Abdom Radiol (NY) 2024; 49:1905-1917. [PMID: 38453791 DOI: 10.1007/s00261-023-04177-5] [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: 09/02/2023] [Revised: 12/24/2023] [Accepted: 12/27/2023] [Indexed: 03/09/2024]
Abstract
RATIONALE AND OBJECTIVES To evaluate the predictive value of tumor and peritumor radiomics in the fatty acid binding protein 4 (FABP4) expression levels and overall survival in patients with hepatocellular carcinoma. MATERIALS AND METHODS The genomic data of HCC patients were obtained from The Cancer Genome Atlas. The Dual-area CT images of corresponding patients were downloaded from The Cancer Imaging Archive, for radiomics feature extraction, model construction and prognosis analysis. Simultaneously, using patients from Sichuan Provincial People's Hospital, the prognostic value of the radiomics model in HCC patients was validated. RESULTS In the TCIA database, the area under the curve (AUC) values of the volumes of interest (VOI)whole model in the training set and internal validation set were 0.812 and 0.754, respectively, and the AUC value of VOIwhole+periphery in the training set and internal validation set were 0.866 and 0.779, respectively. In the VOIwhole and the VOIwhole+periphery model of the independent cohort, there were significant differences in OS between the high and low rad-score groups (P = 0.009, P = 0.021, respectively). Significant positive correlations can be observed between FABP4 expression and correlations with rad-score of VOIwhole model (r = 0.691) and VOIwhole+periphery model (r = 0.732) in the independent cohort. CONCLUSION Radiomics models of tumor and peritumor Dual-area CT images could predict stably the expression levels of FABP4 and may be helping in personalized treatment strategies.
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Affiliation(s)
- Jingyu Wen
- Department of Medical Insurance, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Wang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Organ Transplantation, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Mingge Xia
- Department of Medical Insurance, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Bowen Wei
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Organ Transplantation, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongji Yang
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
- Department of Organ Transplantation, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Clinical Immunology Translational Medicine Key Laboratory of Sichuan Province & Organ Transplantation Center, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
| | - Yifu Hou
- Department of Organ Transplantation, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
- Clinical Immunology Translational Medicine Key Laboratory of Sichuan Province & Organ Transplantation Center, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, 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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Zhou HY, Cheng JM, Chen TW, Zhang XM, Ou J, Cao JM, Li HJ. A Systematic Review and Meta-Analysis of MRI Radiomics for Predicting Microvascular Invasion in Patients with Hepatocellular Carcinoma. Curr Med Imaging 2024; 20:1-11. [PMID: 38389371 DOI: 10.2174/0115734056256824231204073534] [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: 04/26/2023] [Revised: 07/28/2023] [Accepted: 09/08/2023] [Indexed: 02/24/2024]
Abstract
BACKGROUND The prediction power of MRI radiomics for microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains uncertain. OBJECTIVE To investigate the prediction performance of MRI radiomics for MVI in HCC. METHODS Original studies focusing on preoperative prediction performance of MRI radiomics for MVI in HCC, were systematically searched from databases of PubMed, Embase, Web of Science and Cochrane Library. Radiomics quality score (RQS) and risk of bias of involved studies were evaluated. Meta-analysis was carried out to demonstrate the value of MRI radiomics for MVI prediction in HCC. Influencing factors of the prediction performance of MRI radiomics were identified by subgroup analyses. RESULTS 13 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement were eligible for this systematic review and meta-analysis. The studies achieved an average RQS of 14 (ranging from 11 to 17), accounting for 38.9% of the total points. MRI radiomics achieved a pooled sensitivity of 0.82 (95%CI: 0.78 - 0.86), specificity of 0.79 (95%CI: 0.76 - 0.83) and area under the summary receiver operator characteristic curve (AUC) of 0.88 (95%CI: 0.84 - 0.91) to predict MVI in HCC. Radiomics models combined with clinical features achieved superior performances compared to models without the combination (AUC: 0.90 vs 0.85, P < 0.05). CONCLUSION MRI radiomics has the potential for preoperative prediction of MVI in HCC. Further studies with high methodological quality should be designed to improve the reliability and reproducibility of the radiomics models for clinical application. The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42022333822).
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Affiliation(s)
- Hai-Ying Zhou
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan, China
| | - Jin-Mei Cheng
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan, China
| | - Tian-Wu Chen
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan, China
- Department of Radiology, the Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Xiao-Ming Zhang
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan, China
| | - Jing Ou
- Sichuan Key Laboratory of Medical Imaging, and Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan, China
| | - Jin-Ming Cao
- Department of Radiology, Nanchong Central Hospital/Second School of Clinical Medicine, North Sichuan Medical College, Nanchong 637000, Sichuan, China
| | - Hong-Jun Li
- Department of Radiology, Beijing YouAn Hospital, Capital Medical University, Beijing 100069, China
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12
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Wang L, Zhang Y, Li J, Guo S, Ren J, Li Z, Zhuang X, Xue J, Lei J. A Nomogram of Magnetic Resonance Imaging for Preoperative Assessment of Microvascular Invasion and Prognosis of Hepatocellular Carcinoma. Dig Dis Sci 2023; 68:4521-4535. [PMID: 37794295 DOI: 10.1007/s10620-023-08022-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 06/23/2023] [Indexed: 10/06/2023]
Abstract
BACKGROUND Microvascular invasion (MVI) is a predictor of recurrence and overall survival in hepatocellular carcinoma (HCC), the preoperative diagnosis of MVI through noninvasive methods play an important role in clinical treatment. AIMS To investigate the effectiveness of radiomics features in evaluating MVI in HCC before surgery. METHODS We included 190 patients who had undergone contrast-enhanced MRI and curative resection for HCC between September 2015 and November 2021 from two independent institutions. In the training cohort of 117 patients, MVI-related radiomics models based on multiple sequences and multiple regions from MRI were constructed. An independent cohort of 73 patients was used to validate the proposed models. A final Clinical-Imaging-Radiomics nomogram for preoperatively predicting MVI in HCC patients was generated. Recurrence-free survival was analyzed using the log-rank test. RESULTS For tumor-extracted features, the performance of signatures in fat-suppressed T1-weighted images and hepatobiliary phase was superior to that of other sequences in a single-sequence model. The radiomics signatures demonstrated better discriminatory ability than that of the Clinical-Imaging model for MVI. The nomogram incorporating clinical, imaging and radiomics signature showed excellent predictive ability and achieved well-fitted calibration curves, outperforming both the Radiomics and Clinical-Radiomics models in the training and validation cohorts. CONCLUSIONS The Clinical-Imaging-Radiomics nomogram model of multiple regions and multiple sequences based on serum alpha-fetoprotein, three MRI characteristics, and 12 radiomics signatures achieved good performance for predicting MVI in HCC patients, which may help clinicians select optimal treatment strategies to improve subsequent clinical outcomes.
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Affiliation(s)
- Lili Wang
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Yanyan Zhang
- Department of Radiology, Beijing YouAn Hospital, Capital Medical University, Beijing, 100069, China
| | - Junfeng Li
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
- Department of Infectious Diseases, Institute of Infectious Diseases, First Hospital of Lanzhou University, Chengguan District, Donggang Road No. 1, Lanzhou, 730000, China
| | - Shunlin Guo
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Jialiang Ren
- GE Healthcare China, Daxing District, Tongji South Road No. 1, Beijing, 100176, China
| | - Zhihao Li
- GE Healthcare China, Yanta District, 12th Jinye Road, Xi'an, 710076, Shanxi, China
| | - Xin Zhuang
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Jingmei Xue
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China
| | - Junqiang Lei
- First Clinical Medical School of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China.
- Department of Radiology, First Hospital of Lanzhou University, Chengguan District, Donggangxi Road No. 1, Lanzhou, 730000, China.
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Feng L, Chen Q, Huang L, Long L. Radiomics features of computed tomography and magnetic resonance imaging for predicting response to transarterial chemoembolization in hepatocellular carcinoma: a meta-analysis. Front Oncol 2023; 13:1194200. [PMID: 37519801 PMCID: PMC10374837 DOI: 10.3389/fonc.2023.1194200] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 06/26/2023] [Indexed: 08/01/2023] Open
Abstract
Purpose To examine the methodological quality of radiomics-related studies and evaluate the ability of radiomics to predict treatment response to transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC). Methods A systematic review was performed on radiomics-related studies published until October 15, 2022, predicting the effectiveness of TACE for HCC. Methodological quality and risk of bias were assessed using the Radiomics Quality Score (RQS) and Quality Assessment of Diagnostic Accuracy Studies-2 tools, respectively. Pooled sensitivity, pooled specificity, and area under the curve (AUC) were determined to evaluate the utility of radiomics in predicting the response to TACE for HCC. Results In this systematic review, ten studies were eligible, and six of these studies were used in our meta-analysis. The RQS ranged from 7-21 (maximum possible score: 36). The pooled sensitivity and specificity were 0.89 (95% confidence interval (CI) = 0.79-0.95) and 0.82 (95% CI = 0.64-0.92), respectively. The overall AUC was 0.93 (95% CI = 0.90-0.95). Conclusion Radiomics-related studies evaluating the efficacy of TACE in patients with HCC revealed promising results. However, prospective and multicenter trials are warranted to make radiomics more feasible and acceptable.
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Affiliation(s)
- Lijuan Feng
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Qianjuan Chen
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Linjie Huang
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Liling Long
- Department of Radiology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Gaungxi Medical University, Ministry of Education, Nanning, Guangxi, China
- Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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