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 extract 8905 radiomic features, refined to 127 via correlation and recursive feature elimination. XGBoost classified lesions as active or non-active, validated on an internal test set (n = 28 lesions), with performance assessed by area under the receiver operating characteristic curve (AUC-ROC). Results: The XGBoost model achieved an AUC-ROC of 0.87 (95 CI: 0.82-0.92), sensitivity of 0.85, and specificity of 0.83, outperforming other classifiers (SVM AUC 0.84). FLAIR (35.4) and T2W (28.3) dominated feature contributions, with SWI (12.6) enhancing accuracy (AUC dropped to 0.84 without SWI). Noise simulation (Gaussian sigma = 0.1) confirmed robustness (AUC = 0.86). Conclusions: This integration of SWI with conventional sequences in a unified radiomic model offers a promising contrast-free alternative for MS lesion classification, achieving promising accuracy comparable to radiologist performance on an internal test set (n = 28 lesions), pending external validation. External validation is needed to confirm the generalisability, but this approach could reduce gadolinium reliance in clinical practice.