Journal of the neurological sciences · 2026
Predictors of short-term, relapse-independent progression in multiple sclerosis: A machine learning approach based on clinical data and conventional MRI-derived features.
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Ianniello Antonio, Barbuti Elena, Capobianco Maria Francesca, Tranfa Mario, Miele Clementina, Ruggieri Serena, Pontillo Giuseppe, Cocozza Sirio, Pantano Patrizia, Pozzilli Carlo, Cuocolo Renato, Petracca Maria
Аннотация
BACKGROUND: Progression independent of relapse activity (PIRA) contributes to long-term disability in multiple sclerosis (MS), even in early stages. However, predicting short-term PIRA in routine clinical settings remains a challenge.
OBJECTIVES: To develop and evaluate machine learning (ML) models to predict PIRA in relapsing MS using routinely available clinical and conventional MRI-derived features.
METHODS: We developed two ML models to predict PIRA at 24 and 36 months in relapsing MS using baseline and longitudinal clinical and conventional MRI-derived data including brain and spine lesion burden, atrophy, and change in structural connectivity (ChaCo) scores. A Naïve Bayes classifier was trained after feature selection and class balancing with Synthetic Minority Over-sampling Technique (SMOTE).
RESULTS: Among 186 patients, 12.4% experienced PIRA at 24 months. In a longitudinal subset (n = 81), 19.7% developed PIRA at 36 months. The 24-month model, achieved moderate discriminative performance (AUC = 0.73), mainly driven by baseline features. The 36-month model, including baseline disability, brain volume and volume change over time, new cervical cord lesions and baseline ChaCo features, showed improved accuracy (AUC = 0.83).
CONCLUSIONS: ML models using clinical and conventional MRI features can predict short-term PIRA with moderate-to-high accuracy. Incorporating imaging changes over time enhances prediction and may support earlier individualized treatment strategies.
OBJECTIVES: To develop and evaluate machine learning (ML) models to predict PIRA in relapsing MS using routinely available clinical and conventional MRI-derived features.
METHODS: We developed two ML models to predict PIRA at 24 and 36 months in relapsing MS using baseline and longitudinal clinical and conventional MRI-derived data including brain and spine lesion burden, atrophy, and change in structural connectivity (ChaCo) scores. A Naïve Bayes classifier was trained after feature selection and class balancing with Synthetic Minority Over-sampling Technique (SMOTE).
RESULTS: Among 186 patients, 12.4% experienced PIRA at 24 months. In a longitudinal subset (n = 81), 19.7% developed PIRA at 36 months. The 24-month model, achieved moderate discriminative performance (AUC = 0.73), mainly driven by baseline features. The 36-month model, including baseline disability, brain volume and volume change over time, new cervical cord lesions and baseline ChaCo features, showed improved accuracy (AUC = 0.83).
CONCLUSIONS: ML models using clinical and conventional MRI features can predict short-term PIRA with moderate-to-high accuracy. Incorporating imaging changes over time enhances prediction and may support earlier individualized treatment strategies.