Support vector machine-based preoperative identification of IDH-Mutant low-grade gliomas in adult gliomas using clinical features.

ABSTRACT:

Background

The preoperative identification of (isocitrate dehydrogenase) IDH-mutant low-grade gliomas (LGGs) is critical for personalized treatment planning. We aimed to develop a streamlined machine-learning model using key clinical features for rapid and accurate preoperative prediction.

Methods

A retrospective cohort of 418 adult glioma patients was partitioned into training (70%) and internal validation (30%) sets. (Support Vector Machine) SVM was selected as the optimal model after comparing 9 machine learning models. Six clinically significant features, ranked by predictive importance, were incorporated into the final SVM model. The model's generalizability was further validated using an independent external cohort (n = 206).

Results

The SVM model demonstrated h

SUBMITTER: Chen W 

PROVIDER: S-EPMC12502357 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature