Machine learning-based classification of multiple sclerosis lesion activity using multi-sequence MRI radiomics: a complete analysis of T1, T2, FLAIR, DWI, and SWI features.

ABSTRACT:

Purpose

Differentiating active from non-active multiple sclerosis (MS) lesions is critical for disease management but often relies on gadolinium-enhanced magnetic resonance imaging (MRI), raising concerns about retention risks and costs. This study introduces a contrast-free, multi-sequence MRI approach using radiomics and machine learning to classify MS lesion activity.

Material and methods

A total of 187 lesions from 31 MS patients (mean age 42.5 ± 11.3 years; 64.5% female) at Amin Hospital (November 2024 - February 2025) were retrospectively analysed using a 1.5 T MRI scanner. Five sequences - T1-weighted (T1W), T2-weighted (T2W), fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), and susceptibility-weighted imaging (SWI) - were processed to ex

SUBMITTER: Elhaie M 

PROVIDER: S-EPMC12550689 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature