Abstract
Background and Purpose
Paramagnetic 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.
Methods
We 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.
Results
Among 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]).
Conclusion
Our 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.
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This post is Copyright: 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,
the NAIMS Cooperative | October 16, 2024
Wiley: Journal of Neuroimaging: Table of Contents