ArticleHepatology international2023
Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation.
Article in Hepatology international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 55 citations in OpenAlex.
- Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer.Journal of cancer research and clinical oncology · 2024Trial
- Comparison of Long-Term Outcomes of Living-Donor Liver Transplantation in Hepatocellular Carcinoma Based on the Milan Criteria After Downstaging According to Degradable Starch Microspheres with Transcatheter Arterial Chemoembolization.Life (Basel, Switzerland) · 2026Article
- Artificial intelligence and oral microbiome: Reshaping the diagnostic and therapeutic paradigm of OSCC.Clinical and translational medicine · 2026Review
- Redefining Liver Transplantation Indications for Hepatic Malignancies in the Era of Precision Transplant Oncology: An Up-to-Date Narrative Review.Journal of clinical medicine · 2026Review
- Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance.Journal of clinical medicine · 2026Review
- Development and Validation of a Pathomics-Based Prognostic Model for Patients with Lung Adenocarcinoma Undergoing First-Line EGFR-TKI Therapy.Annals of surgical oncology · 2026Article
- AI-based pathomics model predicts regulatory T cell infiltration and radiotherapy response in IDH-wild-type glioblastoma.Frontiers in immunology · 2026Article
- A pathomics-integrated multimodal model to evaluate chemoimmunotherapy efficacy in unresectable intrahepatic cholangiocarcinoma.JHEP reports : innovation in hepatology · 2025Article
- Multi-model applications and cutting-edge advancements of artificial intelligence in hepatology in the era of precision medicine.World journal of gastroenterology · 2025Review
- Article
- Decision-Making Biomarkers Guiding Therapeutic Strategies in Hepatocellular Carcinoma: From Prediction to Personalized Care.Cancers · 2025Review
- Augmenting conventional criteria: a CT-based deep learning radiomics nomogram for early recurrence risk stratification in hepatocellular carcinoma after liver transplantation.Insights into imaging · 2025Article
- Prognostic Significance of Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score in Liver Transplantation for Hepatocellular Carcinoma.Current oncology (Toronto, Ont.) · 2025Article
- Predictive value of neutrophil-to-lymphocyte ratio in recurrent HCC after repeat hepatectomy or salvage liver transplantation.Hepatology international · 2025Article
- NETosis Genes and Pathomic Signature: A Novel Prognostic Marker for Ovarian Serous Cystadenocarcinoma.Journal of imaging informatics in medicine · 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
- Review
- Denoised recurrence label-based deep learning for prediction of postoperative recurrence risk and sorafenib response in HCC.BMC medicine · 2025Article
- Criteria and prognostic models for patients with hepatocellular carcinoma undergoing liver transplantation.Clinical and molecular hepatology · 2025Review
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Authors and funding
22 authors at 4 institutions in 2 countries.
Funding
Abstract
background and purposeTumor recurrence after liver transplantation (LT) impedes the curative chance for hepatocellular carcinoma (HCC) patients. This study aimed to develop a deep pathomics score (DPS) for predicting tumor recurrence after liver transplantation using deep learning. PATIENTS AND
methodsTwo datasets of 380 HCC patients who underwent LT were enrolled. Residual convolutional neural networks were used to identify six histological structures of HCC. The individual risk score of each structure and DPS were derived by a modified DeepSurv network. Cox regression analysis and Concordance index were used to evaluate the prognostic significance. The cellular exploration of prognostic immune biomarkers was performed by quantitative and spatial proximity analysis according to three panels of 7-color immunofluorescence.
resultsThe overall classification accuracy of HCC tissue was 97%. At the structural level, immune cells were the most significant tissue category for predicting post-LT recurrence (HR 1.907, 95% CI 1.490-2.440). The C-indices of DPS achieved 0.827 and 0.794 in the training and validation cohorts, respectively. Multivariate analysis for recurrence-free survival (RFS) showed that DPS (HR 4.795, 95% CI 3.017-7.619) was an independent risk factor. Patients in the high-risk subgroup had a shorter RFS, larger tumor diameter and a lower proportion of clear tumor borders. At the cellular level, a higher infiltration of intratumoral NK cells was negatively correlated with recurrence risk.
conclusionsThis study established an effective DPS. Immune cells were the most significant histological structure related to HCC recurrence. DPS performed well in post-LT recurrence prediction and the identification of clinicopathological features.
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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.