MRI Markers of EDSS ≥ 3 in Relapsing–Remitting Multiple Sclerosis: An Assessment of Lesion Burden, Brain Volumetry, and IVIM-DWI Metrics

Background/Objectives: Disability in relapsing–remitting multiple sclerosis (RRMS) reflects multiple pathological processes that may not be fully captured by individual MRI markers. Because an EDSS score ≥ 3 represents a clinically meaningful threshold of moderate-to-higher disability, this study evaluated the cross-sectional ability of brain volumetric measures and IVIM-DWI parameters to identify patients with RRMS who had EDSS ≥ 3. Methods: This retrospective cross-sectional study included 189 patients with RRMS who had complete EDSS, lesion count, IVIM-DWI, and brain volumetric MRI data. Patients were stratified into EDSS < 3 and EDSS ≥ 3 groups. Single-marker ROC analyses were performed for lesion count, IVIM-DWI parameters, absolute volumetric measures, and fractional volumetric measures. Combined ROC models were constructed to assess the discriminatory performance of integrating lesion burden, volumetric MRI, and IVIM-DWI metrics. Internal model stability was evaluated using 5-fold cross-validation. Results: Patients with EDSS ≥ 3 had higher lesion count, higher ADC and D values, lower gray matter, white matter, and brain parenchymal volumes, higher CSF volume, higher CSF fraction, and lower brain parenchymal fraction (BPF). In single-marker ROC analysis, BPF and CSF fraction showed the strongest discriminatory performance (AUC = 0.705), followed by lesion count (AUC = 0.696), whereas absolute BV showed minimal discriminatory value (AUC = 0.503). The core lesion count + BPF model achieved an AUC of 0.757 and a cross-validated AUC of 0.737. Adding perfusion fraction (f) produced the numerically highest cross-validated performance, with an AUC of 0.769 and a cross-validated AUC of 0.746, although the incremental improvement over lesion count + BPF was modest and not statistically significant. Conclusions: Fractional volumetric measures showed stronger discriminatory performance than absolute BV for EDSS ≥ 3 stratification in RRMS. The lesion count + BPF + f model achieved the highest cross-validated performance among the evaluated MRI models; however, overall discrimination was moderate, and the incremental improvement associated with f was not statistically significant. These exploratory findings require evaluation in larger longitudinal cohorts with external validation.