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Updated: Jul 5, 2025
Magnetic Resonance Imaging of Multiple Sclerosis at 7.0 Tesla
Published on: February 19, 2021
A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS
Brendan S Kelly1,2,3,4, Prateek Mathur2, Gerard McGuinness5
1From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland brendanskelly@me.com.
Background And Purpose:
MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS.
Materials And Methods:
This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model.
Results:
The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications.
Conclusions:
Our machine learning model based on radiomics features successfully differentiated prelesions from normal-appearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.
Insights
Machine learning models using radiomics features can predict multiple sclerosis (MS) progression by analyzing brain MR imaging. This approach identifies early biomarkers in normal-appearing white matter, aiding in disease management.
Area of Science:
- Neurology
- Medical Imaging
- Machine Learning
Background:
- Multiple Sclerosis (MS) is a chronic, progressive demyelinating disorder.
- Diagnosis and monitoring of MS heavily rely on Magnetic Resonance (MR) imaging.
- New MR imaging lesions are critical early indicators of disease progression.
Purpose of the Study:
- To assess a machine learning model utilizing radiomics features for predicting MS progression on brain MR imaging.
- To identify potential imaging biomarkers for early detection of MS progression.
Main Methods:
- A retrospective cohort study with external validation was conducted.
- Longitudinal MR imaging data from MS patients were processed for machine learning.
- Radiomics features were extracted from prelesion areas and normal-appearing white matter.
Main Results:
- The best-performing model (XGBoost) achieved high accuracy (0.91) on the test set and good performance (0.74) on external validation.
- Key radiomics features were related to heterogeneity and gray-level emphasis in segmented regions.
- Probability maps were generated for potential clinical applications.
Conclusions:
- Machine learning models based on radiomics features can effectively distinguish prelesions from normal-appearing white matter.
- Radiomics features from normal-appearing white matter show promise as imaging biomarkers for MS progression.
