ArticleBMC cancer2021
Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for the prediction of posthepatectomy recurrence in hepatocellular carcinoma: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- AI-Driven Predictive Models of Early Recurrence of HCC After Surgical Resection: A Systematic Review.Cancers · 2026Review
- Predictive value of a constructed artificial neural network model for microvascular invasion in hepatocellular carcinoma: A retrospective study.World journal of gastrointestinal oncology · 2025Article
- A CART-based prognostic model for risk stratification of postoperative early recurrence in hepatocellular carcinoma with microvascular invasion.Frontiers in oncology · 2025Article
- COPB2 promotes hepatocellular carcinoma progression through regulation of YAP1 nuclear translocation.Oncology research · 2025Article
- Article
- Endoplasmic reticulum stress promotes hepatocellular carcinoma by modulating immunity: a study based on artificial neural networks and single-cell sequencing.Journal of translational medicine · 2024Article
- The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.Frontiers in medicine · 2024Article
- Application of artificial neural networks to evaluate femur development in the human fetus.PloS one · 2024Article
- The Prediction of Peritoneal Carcinomatosis in Patients with Colorectal Cancer Using Machine Learning.Healthcare (Basel, Switzerland) · 2022Article
- Intraoperative low-dose dopamine is associated with worse survival in patients with hepatocellular carcinoma: A propensity score matching analysis.Frontiers in oncology · 2022Article
- 5-year recurrence prediction after hepatocellular carcinoma resection: deep learning vs. Cox regression models.American journal of cancer research · 2022Article
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11 authors.
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Abstract
backgroundThe accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. This study aimed to develop and validate an artificial neural network (ANN) model to predict PHER in HCC patients without macroscopic vascular invasion.
methodsNine hundred and three patients who underwent curative liver resection for HCC participated in this study. They were randomly divided into derivation (n = 679) and validation (n = 224) cohorts. The ANN model was developed in the derivation cohort and subsequently verified in the validation cohort.
resultsPHER morbidity in the derivation and validation cohorts was 34.8 and 39.2%, respectively. A multivariable analysis revealed that hepatitis B virus deoxyribonucleic acid load, γ-glutamyl transpeptidase level, α-fetoprotein level, tumor size, tumor differentiation, microvascular invasion, satellite nodules, and blood loss were significantly associated with PHER. These factors were incorporated into an ANN model, which displayed greater discriminatory abilities than a Cox's proportional hazards model, preexisting recurrence models, and commonly used staging systems for predicting PHER. The recurrence-free survival curves were significantly different between patients that had been stratified into two risk groups.
conclusionWhen compared to other models and staging systems, the ANN model has a significant advantage in predicting PHER for HCC patients without macroscopic vascular invasion.
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