Cross-Sectional Validation of an Automated Lesion Segmentation Software in Multiple Sclerosis: Comparison with Radiologist Assessments

BACKGROUND AND PURPOSE: Magnetic Resonance Imaging is widely used to assess disease burden in multiple sclerosis (MS). This study aimed to evaluate the effectiveness of a commercially available k-nearest neighbors (k-NN) software in quantifying white matter lesion (WML) burden in MS. We compared the software’s WML quantification to expert radiologists’ assessments.

MATERIALS AND METHODS: We retrospectively reviewed brain MRI examinations of adult MS patients and of adult patients without MS and with a normal brain MRI referred from the neurology clinic. MRI images were processed using an AI-powered, cloud-based k-NN software, which generated a DICOM lesion distribution map and a report of WML count and volume in four brain regions (periventricular, deep, juxtacortical, and infratentorial white matter). Two blinded radiologists performed semi-quantitative assessments of WM lesion load and lesion segmentation accuracy. Additionally, four blinded neuroradiologists independently reviewed the data to determine if MRI findings supported an MS diagnosis. Results were considered significant when p < 0.05.

RESULTS: The study included 32 MS patients (35.4 years ± 9.1) and 19 patients without MS (33.5 years ± 12.1). The k-NN software demonstrated 94.1% and 84.3% accuracy in differentiating MS from non-MS subjects based respectively on WML count and WML volume, compared to radiologists’ accuracy of 90.2% to 94.1%. Lesion segmentation was more accurate for the deep WM and infratentorial regions than for the juxtacortical region (both p <0.001).

CONCLUSIONS: k-NN-derived WML volume and WML count provide valuable quantitative metrics of disease burden in MS. AI-powered post-processing software may enhance the interpretation of brain MRIs in MS patients

ABBREVIATIONS: MS = multiple sclerosis; k-NN＝k-Nearest Neighbors; WML＝white matter lesion; MPRAGE = Magnetization-Prepared Rapid Acquisition Gradient Echo; SPACE = Sampling Perfection with Application-optimized Contrasts using a Different Flip Angle Evolution; EDSS = Expanded Disability Status Scale.