Machine learning-based prediction of glioma grading.

ABSTRACT:

Objective

Gliomas are among the most common and heterogeneous primary tumours of the central nervous system. Accurate grading is essential for treatment planning and prognosis, yet conventional histopathological approaches are limited by subjectivity and poor reproducibility. This study aimed to develop a machine learning-based prediction model that integrates clinical and molecular characteristics to improve early glioma grading, thereby enhancing diagnostic accuracy and supporting individualized treatment strategies.

Methods

An efficient prediction model for low-grade gliomas (LGGs) and glioblastoma (grade IV, GBM) was developed by utilizing the clinical and molecular characteristics of gliomas from The Cancer Genome Atlas (TCGA) dataset. A novel integration of recursive f

SUBMITTER: Liu S 

PROVIDER: S-EPMC12742763 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature