ArticleAnnals of surgical oncology2022
Application of a Convolutional Neural Network for Multitask Learning to Simultaneously Predict Microvascular Invasion and Vessels that Encapsulate Tumor Clusters in Hepatocellular Carcinoma.
Article in Annals of surgical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 5 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Accuracy of Medical Image-Based Deep Learning for Detecting Microvascular Invasion in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Effectiveness of Machine Learning in Detecting Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Diagnostic performance based on MRI for preoperative of vessels encapsulating tumor clusters in hepatocellular carcinoma: a systematic review and meta-analysis.La Radiologia medica · 2025Pooled it
- The prognostic value of vessels encapsulating tumor clusters (VETC) in patients with hepatocellular carcinoma: a systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Pooled it
- A Validated, explainable machine learning-based preoperative risk model for microvascular invasion in hepatocellular carcinoma.Langenbeck's archives of surgery · 2026Article
- CRAF-Net: A Fine-Grained Cross-Channel Attention Network for Preoperative Microvascular Invasion Grading in Hepatocellular Carcinoma via DCE-MRI.Journal of imaging informatics in medicine · 2026Article
- Impact of Annotation Level on Multisequence MRI Models for Preoperative Microvascular Invasion Prediction in Hepatocellular Carcinoma.Radiology. Imaging cancer · 2026Article
- Integrating multiphase MRI surrogates to improve microvascular invasion detection and recurrence risk stratification in HCC.JHEP reports : innovation in hepatology · 2026Article
- Vessels encapsulating tumor clusters in hepatocellular carcinoma: a distinct metastatic pathway with diagnostic and therapeutic significance.Journal of translational medicine · 2026Review
- An Interpretable MRI-Based Machine Learning Model for Preoperative Identification of the Vascular Dissemination Phenotype in Hepatocellular Carcinoma: A Dual-Center Study.Journal of hepatocellular carcinoma · 2026Article
- Pre-treatment CEMRI habitat radiomics and deep learning for noninvasive prediction of the VETC pattern in hepatocellular carcinoma: an exploratory radiogenomic analysis.Frontiers in oncology · 2026Article
- The diagnostic accuracy of imaging methods in preoperative prediction of vessels encapsulating tumor clusters in hepatocellular carcinoma in East Asian populations: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2026Article
- MRI-based habitat radiomics and deep learning for predicting vessels encapsulating tumor clusters and survival in hepatocellular carcinoma.Insights into imaging · 2025Article
- CT-based deep learning radiomics model for predicting proliferative hepatocellular carcinoma: application in transarterial chemoembolization and radiofrequency ablation.BMC medical imaging · 2025Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Chinese expert consensus on the diagnosis and treatment of hepatocellular carcinoma with microvascular invasion (2024 edition).Hepatobiliary surgery and nutrition · 2025Review
- MRI-Based Topology Deep Learning Model for Noninvasive Prediction of Microvascular Invasion and Assisting Prognostic Stratification in HCC.Liver international : official journal of the International Association for the Study of the Liver · 2025Article
- Deep Learning Radiopathomics Models Based on Contrast-enhanced MRI and Pathologic Imaging for Predicting Vessels Encapsulating Tumor Clusters and Prognosis in Hepatocellular Carcinoma.Radiology. Imaging cancer · 2025Article
- Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.European radiology · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundHepatocellular 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.
methodsA 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.
resultsThe 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.
conclusionsA 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.