PT  - JOURNAL ARTICLE
AU  - Kelly, Brendan S.
AU  - Mathur, Prateek
AU  - McGuinness, Gerard
AU  - Dillon, Henry
AU  - Lee, Edward H.
AU  - Yeom, Kristen W.
AU  - Lawlor, Aonghus
AU  - Killeen, Ronan P.
TI  - A Radiomic “Warning Sign” of Progression on Brain MRI in Individuals with MS
AID  - 10.3174/ajnr.A8104
DP  - 2024 Feb 01
TA  - American Journal of Neuroradiology
PG  - 236--243
VI  - 45
IP  - 2
4099  - http://www.ajnr.org/content/45/2/236.short
4100  - http://www.ajnr.org/content/45/2/236.full
SO  - Am. J. Neuroradiol.2024 Feb 01; 45
AB  - 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.AIartificial intelligenceMSSEG2MS SEGmentation Challenge 2NAWMnormal-appearing white matter