ArticleFrontiers in oncology2022
Machine learning prediction model for post- hepatectomy liver failure in hepatocellular carcinoma: A multicenter study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 28 citations in OpenAlex.
- Article
- A Machine Learning Model for Predicting Posthepatectomy Liver Failure After Hepatectomy With Extrahepatic Bile Duct Resection for Perihilar Cholangiocarcinoma: With and Without Indocyanine Green.Annals of gastroenterological surgery · 2026Article
- Article
- Machine Learning-Driven Risk Prediction Models for Posthepatectomy Liver Failure: A Narrative Review.Medicina (Kaunas, Lithuania) · 2026Review
- Artificial Intelligence and Digital Tools Across the Hepato-Pancreato-Biliary Surgical Pathway: A Systematic Review.Journal of clinical medicine · 2025Review
- Radiomics Beyond Radiology: Literature Review on Prediction of Future Liver Remnant Volume and Function Before Hepatic Surgery.Journal of clinical medicine · 2025Review
- Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma.Scientific reports · 2025Article
- Multivariable prognostic models for post-hepatectomy liver failure: An updated systematic review.World journal of hepatology · 2025Article
- 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
- Integrated Multiomics Unravels Hedgehog (HH) Signaling Characteristics in Pancreatic Cancer (PC) and DCBLD2 Regulates HH Signaling to Drive PC Progression.Human mutation · 2025Article
- Utility of a novel scoring system for difficulty of pure laparoscopic hepatectomy for intrahepatic cholangiocarcinoma.Scientific reports · 2024Article
- Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study.Surgical endoscopy · 2024Article
- Artificial Intelligence in Perioperative Planning and Management of Liver Resection.Indian journal of surgical oncology · 2024Review
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence.Journal of clinical medicine · 2024Article
- A Real-World Study on the Short-Term Efficacy of Amlodipine in Treating Hypertension Among Inpatients.Pragmatic and observational research · 2024Article
- Utility of Machine Learning in the Prediction of Post-Hepatectomy Liver Failure in Liver Cancer.Journal of hepatocellular carcinoma · 2024Article
- Comparison of multiple machine learning models for predicting prognosis of pancreatic ductal adenocarcinoma based on contrast-enhanced CT radiomics and clinical features.Frontiers in oncology · 2024Article
- A machine learning model based on clinical features and ultrasound radiomics features for pancreatic tumor classification.Frontiers in endocrinology · 2024Article
- Multi-omics analysis and experimental validation of the value of monocyte-associated features in prostate cancer prognosis and immunotherapy.Frontiers in immunology · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
22 authors at 9 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Post-hepatectomy liver failure (PHLF) is one of the most serious complications and causes of death in patients with hepatocellular carcinoma (HCC) after hepatectomy. This study aimed to develop a novel machine learning (ML) model based on the light gradient boosting machines (LightGBM) algorithm for predicting PHLF. Methods: A total of 875 patients with HCC who underwent hepatectomy were randomized into a training cohort (n=612), a validation cohort (n=88), and a testing cohort (n=175). Shapley additive explanation (SHAP) was performed to determine the importance of individual variables. By combining these independent risk factors, an ML model for predicting PHLF was established. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, and decision curve analyses (DCA) were used to evaluate the accuracy of the ML model and compare it to that of other noninvasive models. Results: The AUCs of the ML model for predicting PHLF in the training cohort, validation cohort, and testing cohort were 0.944, 0.870, and 0.822, respectively. The ML model had a higher AUC for predicting PHLF than did other non-invasive models. The ML model for predicting PHLF was found to be more valuable than other noninvasive models. Conclusion: A novel ML model for the prediction of PHLF using common clinical parameters was constructed and validated. The novel ML model performed better than did existing noninvasive models for the prediction of PHLF.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.