An interpretable machine learning model based on MRI radiomics and GAME score for predicting early recurrence after thermal ablation in colorectal liver metastases.

ABSTRACT:

Objective

To develop and validate machine learning models based on preoperative magnetic resonance imaging(MRI) and baseline clinical characteristics for predicting early recurrence(ER) in patients with colorectal liver metastases(CRLM) treated with thermal ablation(TA).

Materials and methods

Patients with CRLM who underwent TA between January 2016 and December 2021 at two hospitals in China were allocated. Clinical and MRI data were used to develop and validate the clinical model, radiomics machine learning (R-ML) model, and combined clinical-radiomics model to predict ER after TA. The prognostic performance of the genetic and morphological evaluation (GAME) score and the Fong score was also compared (Supplementary Material). The best-performing algorithm among eight machin

SUBMITTER: Kong Y 

PROVIDER: S-EPMC12789207 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature