Predicting future multiple sclerosis disease progression from MR scans

Lara Dular; Gregor Brecl-Jakob; Lina Savšek; Jožef Magdič; Žiga Špiclin

doi:10.1117/12.2654416

7 April 2023 Predicting future multiple sclerosis disease progression from MR scans

Lara Dular, Gregor Brecl-Jakob, Lina Savšek, Jožef Magdič, Žiga Špiclin

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124652U (2023) https://doi.org/10.1117/12.2654416
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

The identification and management of Multiple Sclerosis (MS) patients who are at risk of disease progression and/or conversion to Secondary Progressive MS (SPMS) is a significant unmet clinical need. This study aimed to develop machine and deep learning (ML/DL) models to predict disability progression and/or conversion to SPMS at least 6 months before progression, determined by Expanded Disability Status Scale (EDSS) score. Three traditional ML algorithms were trained on brain parcellation volume measurements derived from T1 weighted magnetic resonance images (MRI) and convolutional neural networks (CNNs) were trained directly on MRI images. The results showed that the three ML models performed slightly better than a random classifier, with the Support Vector Classifier achieving the best results in terms of area under the receiver operating characteristic curve (AUROC) with a score of 0.62. The CNN approach yielded the most promising results, with an AUROC score of 0.75. This suggests that the ability to identify MS patients at high risk of future disability progression directly from MRI scans is a promising research direction. Further development of these algorithms could enable timely interventions, potentially preventing or delaying the onset of disability progression and/or SPMS.

Citation Download Citation

Lara Dular, Gregor Brecl-Jakob, Lina Savšek, Jožef Magdič, and Žiga Špiclin "Predicting future multiple sclerosis disease progression from MR scans", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124652U (7 April 2023); https://doi.org/10.1117/12.2654416

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available