Deep Learning-Based Automated Detection and Burden Assessment of Paramagnetic Rim Lesions on Quantitative Susceptibility Mapping in Patients With Multiple Sclerosis.

objectiveThis study aimed to develop a deep learning (DL) framework for automated detection and burden assessment of paramagnetic rim lesions (PRLs) by using quantitative susceptibility mapping (QSM) in multiple sclerosis (MS), compare QSM-only and QSM + fluid-attenuated inversion recovery (FLAIR) configurations, and evaluate the clinical relevance of the automated PRL burden. MATERIALS AND

methodsBrain magnetic resonance imaging data, including QSM and three-dimensional (3D) FLAIR data, obtained between January and December 2025, were retrospectively collected from 106 patients suspected of having MS. The dataset consisted of an exploration set (n = 84)-divided into training (n = 54) and internal test (n = 30) sets-and a temporal test set (n = 22). The PRLs were manually segmented to establish the ground truth. A 3D nnU-Net framework was trained using the QSM-only and QSM + FLAIR configurations for patient-level classification, lesion-level detection, and PRL burden assessment. A separate clinical implication cohort (n = 117) was used to assess the associations between the automated PRL burden and clinical outcomes.

resultsIn the exploration set (median age: 40.0 years; 67.9% female), 69 (82.1%) patients were PRL-positive, with a total of 705 PRLs. In the internal test set, the QSM-only model showed higher lesion-level sensitivity (72.4% [147/203] vs. 62.1% [126/203],

conclusionQSM-based DL models enabled automated detection and burden assessment of PRLs in MS, with the QSM-only model performing comparably to QSM + FLAIR while offering a simplified single-sequence pipeline. The association between the automated PRL burden and cognitive impairment highlights its potential as a biomarker for MS assessment.