Ischemic vasculopathy, particularly small-vessel disease, may mimic multiple sclerosis (MS) located in the periventricular or subcortical region on magnetic resonance (MR) examinations and should be included in the differential diagnosis of MS-like lesions.
To evaluate the performance of a T2-weighted imaging (T2WI)-based radiomic signature to distinguish MS lesions from lesions corresponding to ischemic demyelination, which often mimics MS on MRI.
A retrospective study was conducted in 38 patients (627 lesions) with MS and 914 patients (2466 lesions) with lesions mimicking ischemic demyelination in the periventricular or subcortical region. All patients underwent 3 T MRI. A total of 472 radiomic features were extracted from the T2WI data of each patient. Intraclass correlation coefficients were used to select the features with excellent stability and repeatability. Then, we used the minimum-redundancy maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms for feature selection. After feature selection, various classifiers (including logistic regression, decision tree, AdaBoost, random forest (RF), or support vector machine (SVM)) were trained. The performance of each classifier was validated in the test set by determining the area under the curve (AUC).
Nine features were selected to distinguish MS lesions from the similar lesions of ischemic demyelination. The radiomic signature showed a significant difference between the MS and ischemic demyelination patients (p < 0.01). RF and SVM were overfitted. The LASSO logistic regression model was the best-performing radiomic model,with an AUC, accuracy, sensitivity, and specificity of 0.900 (95% CI: 0.883-0.918), 87.0%, 58.9% and 95.2%, respectively, in the training set and 0.828 (95% CI: 0.791-0.864), 87.7%, 53.6% and 94.4%, respectively, in the validation set.
The T2WI-based radiomic signature can effectively differentiate MS patients from patients with MS-like lesions due to ischemic demyelination.

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