Radiomics-based machine learning for glioma grade classification: a multicenter study with SHapley Additive exPlanations interpretability analysis.

ABSTRACT:

Background

The accurate preoperative grading of gliomas is critical for guiding therapeutic strategies and prognostic assessment. This study aimed to develop and validate a robust, interpretable machine learning (ML) model based on multicenter magnetic resonance imaging (MRI) radiomics data for predicting the World Health Organization (WHO) grade in glioma patients.

Methods

We collected MRI data from 905 glioma patients diagnosed between 2005-2024 across three independent cohorts. Data from 329 of The Cancer Genome Atlas (TCGA) patients served as training (n=230) and internal testing (n=99) sets, while data from 482 University of California San Francisco (UCSF) patients and 94 Nantong University Affiliated Hospital (NTUA) patients served as external validation sets. Radiomic

SUBMITTER: Shi Y 

PROVIDER: S-EPMC12605345 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature