Body composition radiomics combined with machine learning for early recurrence prediction in intrahepatic cholangiocarcinoma following curative surgery: A Multi-Center study.

ABSTRACT:

Purpose

Early recurrence (ER) of intrahepatic cholangiocarcinoma (ICC) after curative hepatectomy correlates with dismal prognosis. We hypothesized that body composition radiomics reflecting systemic metabolic-immunologic status could enhance ER prediction. This multi-center study aimed to develop and validate integrated radiomics-clinical machine learning (RCML) models for postoperative ER risk stratification.

Methods

In this retrospective study, 258 ICC patients (2011-2022) from three institutions who underwent curative resection were enrolled. Body composition features were extracted from preoperative contrast-enhanced CT (L3 level). After minimum redundancy maximum relevance(mRMR) feature selection, radiomics-based ML(RML) models were constructed. Integrated RCML models

SUBMITTER: Gan Y 

PROVIDER: S-EPMC12920340 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature