• Luyun Chen, Zheng Ren, Kelly A Clark, Carolyn Lou, Fang Liu, Quy Cao, Abigail R Manning, Melissa L Martin, Elaina Luskin, Carly M O'Donnell, Christina J Azevedo, Peter A Calabresi, Leorah Freeman, Roland G Henry, Erin E Longbrake, Jiwon Oh, Nico Papinutto, Michel Bilello, Jae W Song, Marwa Kaisey, Nancy L Sicotte, Daniel S Reich, Andrew J Solomon, Daniel Ontaneda, Pascal Sati, Martina Absinta, Matthew K Schindler, Russell T Shinohara, and NAIMS Cooperative.
• Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
• J Neuroimaging. 2024 Nov 1; 34 (6): 750757750-757.

Background And PurposeParamagnetic rim lesions (PRLs) are an MRI biomarker of chronic inflammation in people with multiple sclerosis (MS). PRLs may aid in the diagnosis and prognosis of MS. However, manual identification of PRLs is time-consuming and prone to poor interrater reliability. To address these challenges, the Automated Paramagnetic Rim Lesion (APRL) algorithm was developed to automate PRL detection. The primary objective of this study is to evaluate the accuracy of APRL for detecting PRLs in a multicenter setting.MethodsWe applied APRL to a multicenter dataset, which included 3-Tesla MRI acquired in 92 participants (43 with MS, 14 with clinically isolated syndrome [CIS]/radiologically isolated syndrome [RIS], 35 without RIS/CIS/MS). Subsequently, we assessed APRL's performance by comparing its results with manual PRL assessments carried out by a team of trained raters.ResultsAmong the 92 participants, expert raters identified 5637 white matter lesions and 148 PRLs. The automated segmentation method successfully captured 115 (78%) of the manually identified PRLs. Within these 115 identified lesions, APRL differentiated between manually identified PRLs and non-PRLs with an area under the curve (AUC) of .73 (95% confidence interval [CI]: [.68, .78]). At the subject level, the count of APRL-identified PRLs predicted MS diagnosis with an AUC of .69 (95% CI: [.57, .81]).ConclusionOur study demonstrated APRL's capability to differentiate between PRLs and lesions without paramagnetic rims in a multicenter study. Automated identification of PRLs offers greater efficiency over manual identification and could facilitate large-scale assessments of PRLs in clinical trials.© 2024 American Society of Neuroimaging.

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