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

Overview

1. Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
2. Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
3. University of Ulsan College of Medicine, Seoul, Republic of Korea

Abstract

Objective: 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.

Materials and Methods: 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.

Results: 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], P = 0.004) and precision (78.2% [147/188] vs. 65.6% [126/192], P = 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 (P = 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 (P = 0.002).

Conclusion: 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.

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The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

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This paper

Jeong, E., Seo, D., Moon, H. H., Yun, H., Choi, Y., & Lee, E.-J. (2026). Deep Learning-Based Automated Detection and Burden Assessment of Paramagnetic Rim Lesions on Quantitative Susceptibility Mapping in Patients With Multiple Sclerosis. Korean journal of radiology, 27(8), 745-757. https://doi.org/10.3348/kjr.2026.0103

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