ArticleHepatology international2022
Deep learning for evaluation of microvascular invasion in hepatocellular carcinoma from tumor areas of histology images.
Article in Hepatology international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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18 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- 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
- From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.Journal of the Egyptian National Cancer Institute · 2026Review
- Artificial intelligence in digital pathology diagnosis and analysis: technologies, challenges, and future prospects.Military Medical Research · 2026Review
- [Diagnosis and treatment of liver diseases in the era of artificial intelligence].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Artificial intelligence predicts outcome-related molecular profiles and vascular invasion in hepatocellular carcinoma.JHEP reports : innovation in hepatology · 2025Article
- Artificial intelligence-driven pathomics in hepatocellular carcinoma: current developments, challenges and perspectives.Discover oncology · 2025Review
- Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification.World journal of clinical oncology · 2025Review
- Global trends in artificial intelligence applications in liver disease over seventeen years.World journal of hepatology · 2025Article
- Denoised recurrence label-based deep learning for prediction of postoperative recurrence risk and sorafenib response in HCC.BMC medicine · 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
- The role of artificial intelligence in the management of liver diseases.The Kaohsiung journal of medical sciences · 2024Review
- Article
- Review
- Deep learning-based accurate diagnosis and quantitative evaluation of microvascular invasion in hepatocellular carcinoma on whole-slide histopathology images.Cancer medicine · 2024Article
- Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Reflections on prediction of microvascular invasion in hepatocellular carcinoma by pathology images.Hepatology international · 2023Article
- Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial Intelligence.Technology in cancer research & treatmentReview
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14 authors at 4 institutions in 1 country.
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Abstract
backgroundMicrovascular invasion (MVI) is essential for the management of hepatocellular carcinoma (HCC). However, MVI is hard to evaluate in patients without sufficient peri-tumoral tissue samples, which account for over a half of HCC patients.
methodsWe established an MVI deep-learning (MVI-DL) model with a weakly supervised multiple-instance learning framework, to evaluate MVI status using only tumor tissues from the histological whole slide images (WSIs). A total of 350 HCC patients (2917 WSIs) from the First Affiliated Hospital of Sun Yat-sen University (FAHSYSU cohort) were divided into a training and test set. One hundred and twenty patients (504 WSIs) from Dongguan People's Hospital and Shunde Hospital of Southern Medical University (DG-SD cohort) formed an external test set. Unsupervised clustering and class activation mapping were applied to visualize the key histological features.
resultsIn the FAHSYSU and DG-SD test set, the MVI-DL model achieved an AUC of 0.904 (95% CI 0.888-0.920) and 0.871 (95% CI 0.837-0.905), respectively. Visualization results showed that macrotrabecular architecture with rich blood sinus, rich tumor stroma and high intratumor heterogeneity were identified as the key features associated with MVI ( +), whereas severe immune infiltration and highly differentiated tumor cells were associated with MVI (-). In the simulation of patients with only one WSI or biopsies only, the AUC of the MVI-DL model reached 0.875 (95% CI 0.855-0.895) and 0.879 (95% CI 0.853-0.906), respectively.
conclusionThe effective, interpretable MVI-DL model has potential as an important tool with practical clinical applicability in evaluating MVI status from the tumor areas on the histological slides.
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