ArticleJournal of hepatocellular carcinoma2024
Utility of Machine Learning in the Prediction of Post-Hepatectomy Liver Failure in Liver Cancer.
Article in Journal of hepatocellular carcinoma, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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
10 citing papers in PubMed.
- Article
- Machine Learning-Driven Risk Prediction Models for Posthepatectomy Liver Failure: A Narrative Review.Medicina (Kaunas, Lithuania) · 2026Review
- Enhancing future liver remnant hypertrophy: innovative preoperative strategies and emerging technologies.Translational gastroenterology and hepatology · 2026Review
- Artificial intelligence in the diagnosis and prognosis of intrahepatic cholangiocarcinoma: Applications and challenges.World journal of gastrointestinal oncology · 2025Review
- Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma.Scientific reports · 2025Article
- Learning-based early detection of post-hepatectomy liver failure using temporal perioperative data: a nationwide multicenter retrospective study in China.EClinicalMedicine · 2025Article
- Roles of PDGF/PDGFR signaling in various organs.The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2025Review
- An in-depth analysis of research on posthepatectomy liver failure (2006-2024): exploring trends and future directions through a bibliometric approach.Frontiers in medicine · 2025Article
- Predicting the gastrointestinal bleeding of HBV-related acute-on-chronic liver failure based on machine learning.Frontiers in medicine · 2025Article
- Utility of a novel scoring system for difficulty of pure laparoscopic hepatectomy for intrahepatic cholangiocarcinoma.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Posthepatectomy liver failure (PHLF) is a serious complication associated with high mortality rates. Machine learning (ML) has rapidly developed and may outperform traditional models in predicting PHLF in patients who have undergone hepatectomy. This study aimed to predict PHLF using ML and compare its performance with that of traditional scoring systems. Methods: The clinicopathological data of 334 patients who underwent liver resection were retrospectively collected. The Pycaret library, a simple, open-source machine learning library, was used to compare multiple classification models for PHLF prediction. The predictive performance of 15 ML algorithms was compared using the mean area under the receiver operating characteristic curve (AUROC) and accuracy, and the best-fit model was selected among 15 ML algorithms. Next, the predictive performance of the selected ML-PHLF model was compared with that of routine scoring systems, the albumin-bilirubin score (ALBI) and the fibrosis-4 (FIB-4) index, using AUROC. Results: The best model was extreme gradient boosting (accuracy:93.1%; AUROC:0.863) among the 15 ML algorithms. As compared with ALBI and FIB-4, the ML PHLF model had higher AUROC for predicting PHLF. Conclusion: The novel ML model for predicting PHLF outperformed routine scoring systems.
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