Deep Learning-Based Automated Detection and Burden Assessment of Paramagnetic Rim Lesions on Quantitative Susceptibility Mapping in Patients With Multiple Sclerosis.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
- University of Ulsan College of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. [email protected].
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea. [email protected].
Abstract
This 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. Brain 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. In 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], <i>P</i> = 0.004) and precision (78.2% [147/188] vs. 65.6% [126/192], <i>P</i> = 0.009). In the temporal test set, the lesion-level sensitivity was 45.3% (29/64) for the QSM-only model and 54.7% (35/64) for the QSM + FLAIR model (<i>P</i> = 0.181), whereas both models achieved 100% (8/8) patient-level sensitivity. No significant differences were observed in patient-level classification in either test set. A higher automated PRL burden was associated with poorer cognition (<i>P</i> = 0.002). QSM-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.