ArticleCancers2026
Development and Internal Multicenter Validation of a Deep Learning Model for Predicting Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma: A Multicenter Study.
Article in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Artificial Intelligence for Perioperative Risk Prediction in Anesthesiology: A Bibliometric and Knowledge-Mapping Analysis.Healthcare (Basel, Switzerland) · 2026Review
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Authors and funding
14 authors.
Funding
Abstract
BACKGROUND/
objectivesPost-hepatectomy liver failure is one of the most serious complications after liver resection for hepatocellular carcinoma and is associated with high morbidity and mortality. Traditional clinical scoring systems and statistical models provide limited predictive accuracy. This study aimed to develop and internally validate a deep learning model for predicting post-hepatectomy liver failure using multicenter clinical data.
methodsA retrospective cohort of 498 patients from six centers undergoing curative-intent liver resection for hepatocellular carcinoma was analyzed. Preoperative biochemical parameters, intraoperative surgical variables, and tumor-related characteristics were incorporated into a deep neural network, with logistic regression as a baseline comparator. Data splitting was performed before preprocessing, and imputation/scaling parameters were fitted on the training set only to prevent information leakage. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) and precision-recall (PR) curves; calibration was assessed using calibration plots and Brier score; and clinical utility was assessed using decision curve analysis (DCA). A sensitivity analysis using a preoperative-only feature set (excluding intraoperative variables) was also conducted. SHapley Additive exPlanations were used to determine variable importance.
resultsThe deep learning model achieved AUCs of 0.914, 0.892, and 0.906 in the training, validation, and test sets, outperforming logistic regression (0.782, 0.757, and 0.773). Key predictors included ALBI and MELD scores, prothrombin time, intraoperative blood loss, and resection extent. Calibration and decision curve analysis further supported the robustness and clinical utility of the model.
conclusionsThe deep learning model provides improved predictive performance for post-hepatectomy liver failure compared with logistic regression in an internally validated multicenter cohort and may support perioperative risk stratification and surgical planning.
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Registered trials
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