RT Journal Article
SR Electronic
T1 Predicting Ten-Year Clinical Outcomes in Multiple Sclerosis with Radiomics-Based Machine Learning Models
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP ajnr.A8912
DO 10.3174/ajnr.A8912
A1 Tranfa, Mario
A1 Petracca, Maria
A1 Cuocolo, Renato
A1 Ugga, Lorenzo
A1 Morra, Vincenzo Brescia
A1 Carotenuto, Antonio
A1 Elefante, Andrea
A1 Falco, Fabrizia
A1 Lanzillo, Roberta
A1 Moccia, Marcello
A1 Scaravilli, Alessandra
A1 Brunetti, Arturo
A1 Cocozza, Sirio
A1 Quarantelli, Mario
A1 Pontillo, Giuseppe
YR 2025
UL http://www.ajnr.org/content/early/2025/07/03/ajnr.A8912.abstract
AB BACKGROUND AND PURPOSE: Identifying patients with multiple sclerosis (pwMS) at higher risk of clinical progression is essential to inform clinical management. We aimed to build prognostic models using machine learning (ML) algorithms predicting long-term clinical outcomes based on a systematic mapping of volumetric, radiomic, and macrostructural disconnection features from routine brain MRI scans of pwMS.MATERIALS AND METHODS: In this longitudinal monocentric study, 3T structural MRI scans of pwMS were retrospectively analyzed. Based on a ten-year clinical follow-up (average duration=9.4±1.1 years), patients were classified according to confirmed disability progression (CDP) and cognitive impairment (CI) as assessed through the Expanded Disability Status Scale (EDSS) and the Brief International Cognitive Assessment of Multiple Sclerosis (BICAMS) battery, respectively. 3D-T1w and FLAIR images were automatically segmented to obtain volumes, disconnection scores (estimated based on lesion masks and normative tractography data), and radiomic features from 116 gray matter regions defined according to the Automated Anatomical Labelling (AAL) atlas. Three ML algorithms (Extra Trees, Logistic Regression, and Support Vector Machine) were used to build models predicting long-term CDP and CI based on MRI-derived features. Feature selection was performed on the training set with a multi-step process, and models were validated with a holdout approach, randomly splitting the patients into training (75%) and test (25%) sets.RESULTS: We studied 177 pwMS (M/F = 51/126; mean±SD age: 35.2±8.7 years). Long-term CDP and CI were observed in 71 and 55 patients, respectively. Regarding the CDP class prediction analysis, the feature selection identified 13-, 12-, and 10-feature subsets obtaining an accuracy on the test set of 0.71, 0.69, and 0.67 for the Extra Trees, Logistic Regression, and Support Vector Machine classifiers, respectively. Similarly, for the CI prediction, subsets of 16, 17, and 19 features were selected, with 0.69, 0.64, and 0.62 accuracy values on the test set, respectively. There were no significant differences in accuracy between ML models for CDP (p=0.65) or CI (p=0.31).CONCLUSIONS: Building on quantitative features derived from conventional MRI scans, we obtained long-term prognostic models, potentially informing patients’ stratification and clinical decision-making.ABBREVIATIONS: MS, multiple sclerosis; pwMS, people with MS; HC, healthy controls; ML, machine learning; DD, disease duration; EDSS, Expanded Disability Status Scale; TLV, total lesion volume; CDP, confirmed disability progression; CI, cognitive impairment; BICAMS, Brief International Cognitive Assessment of Multiple Sclerosis.