Advancing noninvasive glioma classification with diffusion radiomics: Exploring the impact of signal intensity normalization.

ABSTRACT:

Background

This study investigates the influence of diffusion-weighted Magnetic Resonance Imaging (DWI-MRI) on radiomic-based prediction of glioma types according to molecular status and assesses the impact of DWI intensity normalization on model generalizability.

Methods

Radiomic features, compliant with image biomarker standardization initiative standards, were extracted from preoperative MRI of 549 patients with diffuse glioma, known IDH, and 1p19q-status. Anatomical sequences (T1, T1c, T2, FLAIR) underwent N4-Bias Field Correction (N4) and WhiteStripe normalization (N4/WS). Apparent diffusion coefficient (ADC) maps were normalized using N4 or N4/z-score. Nine machine-learning algorithms were trained for multiclass prediction of glioma types (IDH-mutant 1p/19q codeleted,

SUBMITTER: Foltyn-Dumitru M 

PROVIDER: S-EPMC11003539 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature