Liver regeneration-associated machine learning architecture integrating time-phased predictions for post-hepatectomy liver failure.

ABSTRACT:

Background

Post-hepatectomy liver failure (PHLF) is the leading cause of morbidity and mortality following major hepatectomy. Existing prediction models inadequately capture the dynamic liver regeneration and perioperative changes, limiting their predictive accuracy. We aimed to develop a machine learning (ML) modelling system (PILOT architecture) integrating liver regeneration-associated biomarkers with time-phased perioperative data for PHLF prediction.

Methods

This retrospective multicentre study included 1071 patients undergoing major hepatectomy at three centres (2019-2024), divided into training (n = 623) and two external validation cohorts (n = 206 and 242). Fifty-five perioperative variables, including novel liver regeneration-associated biomarkers (GATA3, RAMP2, VEG

SUBMITTER: Shen H 

PROVIDER: S-EPMC12702303 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature