A multi-modal deep learning network for the classification of paramagnetic rim and remyelinated lesions in multiple sclerosis.

OBJECTIVES

Robust automated classification of paramagnetic rim lesions (PRLs) and remyelinated lesions based on iron and myelin content in people with multiple sclerosis (pwMS).

MATERIALS AND

METHODS

In this prospective study (2018-2022), three-dimensional (3D) brain magnetic resonance imaging (MRI) from 180 pwMS (mean age: 47 ± 14 years, 108 females) were acquired at 3T including fluid-attenuated inversion recovery, magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE), and T2*-weighted segmented echo-planar imaging. Post-processed quantitative susceptibility mapping (QSM) and filtered phase unwrapped (PU) images were generated. Ground truth was established through expert manual rating. Performance was assessed using nested cross-validation (CV) including outer test sets. Three neural network configurations were evaluated: (1) PRL classification with QSM and MP2RAGE, (2) PRL classification with PU and MP2RAGE, and (3) multiple lesion phenotype (MLP) classification with QSM and MP2RAGE.

RESULTS

For PRL classification, our network achieves a top mean validation F1 score of 0.737 ± 0.027 in the MP2RAGE-QSM configuration trained on PRLs, and a best test performance of 0.709 ± 0.040 when trained on MLP. The MP2RAGE-PU configuration yielded validation and test F1 scores of 0.733 ± 0.021 and 0.662 ± 0.037, respectively. The QSM-based MLP classification achieved a macro F1 test score of 0.728 ± 0.012.

CONCLUSION

Deep learning can automate the classification of PRLs and QSM lesion phenotypes with high accuracy, potentially facilitating the detection of PRLs (included in the new diagnostic criteria) and remyelinated lesions, guiding personalized treatment decisions in pwMS.