Glioma Grade Classification Using Machine Learning and MRI Radiomics: A Single‐Center Prospective Study Comparing Original and Wavelet‐Transformed Features From Anatomical, Diffusion‐Weighted, and Post‐Contrast Imaging

Abstract

Accurate glioma grade classification is critical for prognostic assessment and clinical decision‐making. This study aimed to evaluate the impact of wavelet‐based radiomics feature analysis on the performance of machine learning (ML) models for glioma grade classification using diffusion‐weighted imaging (DWI), structural MRI sequences, and contrast‐enhanced T1‐weighted (T1Gd) images.