https://doi.org/10.1016/j.nicl.2022.102979

QSMRim-Net: Imbalance-aware learning for identification of chronic active multiple sclerosis lesions on quantitative susceptibility maps

Abstract Background and Purpose Chronic active multiple sclerosis (MS) lesions are characterized by a paramagnetic rim at the edge of the lesion and are associated with increased disability in patients. Quantitative susceptibility mapping (QSM) is an MRI technique that is sensitive to chronic active lesions, termed rim+ lesions on the QSM. We present QSMRim-Net, a data imbalance-aware deep neural network that fuses lesion-level radiomic and convolutional image features for automated identification of rim+ lesions on QSM. Methods QSM and T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRI of the brain were collected at 3T for 172 MS patients. Rim+ lesions were manually annotated by two human experts, followed by consensus from a third expert, for a total of 177 rim+ and 3986 rim negative (rim-) lesions. Our automated rim+ detection algorithm, QSMRim-Net, consists of a two-branch feature extraction network and a synthetic minority oversampling network to classify rim+ lesions. The first network branch is for image feature extraction from the QSM and T2-FLAIR, and the second network branch is a fully connected network for QSM lesion-level radiomic feature extraction. The oversampling network is designed to increase classification performance with imbalanced data. Results On a lesion-level, in a five-fold cross validation framework, the proposed QSMRim-Net detected rim+ lesions with a partial area under the receiver operating characteristic curve (pROC AUC) of 0.760, where clinically relevant false positive rates of less than 0.1 were considered. The method attained an area under the precision recall curve (PR AUC) of 0.704. QSMRim-Net out-performed other state-of-the-art methods applied to the QSM on both pROC AUC and PR AUC. On a subject-level, comparing the predicted rim+ lesion count and the human expert annotated count, QSMRim-Net achieved the lowest mean square error of 0.98 and the highest correlation of 0.89 (95% CI: 0.86, 0.92). Conclusion This study develops a novel automated deep neural network for rim+ MS lesion identification using T2-FLAIR and QSM images. 

QSMRim-Net: Imbalance-Aware Learning for Identification of Chronic Active Multiple Sclerosis Lesions on Quantitative Susceptibility Maps

Objective: This study aimed to explore the association between chronic active rim+ lesions, identified as having a hyperintense rim on quantitative susceptibility mapping (QSM), on both clinical disability and imaging measures of neurodegeneration in patients with multiple sclerosis. 
Methods: The patient cohort was composed of 159 relapsing remitting multiple sclerosis patients aged 42.17 +/- 10.25 years and disease duration of 10.74 +/- 7.51 years. The Brief International Cognitive Assessment for Multiple Sclerosis and Expanded Disability Status Scale (EDSS) were used to assess clinical disability. Cortical thickness and thalamic volume were evaluated as imaging measures of neurodegeneration. 
Results: A total of 4,469 multiple sclerosis lesions were identified, of which 171 QSM rim+ (3.8%) lesions were identified among 57 patients (35.9%). In a multivariate regression model, as the overall total lesion burden increased, patients with at least one rim+ lesion on QSM performed worse on both physical disability and cognitive assessments, specifically the Symbol Digit Modalities Test (p=0.010), California Verbal Learning Test-II (p=0.030), and EDSS (p=0.001). In a separate univariate regression model, controlling for age (p<0.001), having at least one rim+ lesion was related to more cortical thinning (p= 0.03) in younger patients (< 45 years). Lower thalamic volume was associated with older patients (p=0.038) and larger total lesion burden (p<0.001) however, the association did not remain significant with rim+ lesions (p=0.10). 
Interpretation: Our findings demonstrate the significant impact of chronic active lesions, as identified on QSM, on both clinical disability and imaging measures of neurodegeneration in patients with multiple sclerosis.

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Disease correlates of quantitative susceptibility mapping rim lesions in multiple sclerosis

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Disease correlates of quantitative susceptibility mapping rim lesions in multiple sclerosis