Evaluation of Lesion Burden in Pediatric Patients with Multiple Sclerosis by Computer Aided Algorithm and Comparison with Standard Detection Methods

Abstract

Background: The aim of this retrospective study was to assess the lesion burden in pediatric patients with multiple sclerosis (pMS) using a computer-assisted algorithm, specifically the VolBrain program. The study aimed to compare the performance of this automated tool with traditional detection methods performed by neuroimaging analysts, providing valuable insights into the potential of automated tools for lesion quantification in pMS. Materials and Methods: The study cohort consisted of 20 PMS patients, aged 10-18 years, registered at Atatürk University Research Hospital. Lesion assessment was performed using the VolBrain program (by an anatomist) and standard detection methods (by a neuroradiologist) using T2 SPACE dark matter sequences. Statistical analysis included Wilcoxon and Pearson correlation tests, and the study adhered to ethical considerations and standardised magnetic resonance imaging (MRI) protocols. Results: In this study, pMS patients aged 10-18 years, the cohort consisted of 60% females (n=12) and 40% males (n=8). The mean age for females was 15.67±1.969 and for males 14.50±2.20 years (p=0.24). Plaque count analysis showed a statistically significant difference between radiologist and VolBrain assessment in all pMS patients (p=0.021). Significant differences were also observed in female pMS patients (p=0.034) but not in males (p=0.362). Correlations between radiologist and VolBrain assessments showed significant associations in both female and male patients, with strong correlations observed for plaque number, lesion burden and Expanded Disability Status Scale (EDSS) scores (p<0.01). Conclusions: This study demonstrates the potential of the VolBrain programme in assessing lesion burden in pMS patients. The observed correlations with traditional methods and clinical parameters support the concurrent validity of VolBrain and emphasise its potential clinical relevance. Incorporating automated tools into routine clinical practice could improve the accuracy of lesion quantification and thus contribute to improved monitoring and management of pMS.