A Radiomic "Warning Sign" of Progression on Brain MRI in Individuals with MS.

Autor:	Kelly BS; From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland brendanskelly@me.com.; Insight Centre for Data Analytics (B.S.K., P.M., A.L.), University College Dublin, Dublin, Ireland.; Wellcome Trust and Health Research Board (B.S.K.), Irish Clinical Academic Training, Dublin, Ireland.; School of Medicine (B.S.K.), University College Dublin, Dublin, Ireland., Mathur P; Insight Centre for Data Analytics (B.S.K., P.M., A.L.), University College Dublin, Dublin, Ireland., McGuinness G; From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland., Dillon H; From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland., Lee EH; Lucille Packard Children's Hospital at Stanford (E.H.L., K.W.Y.), Stanford, California., Yeom KW; Lucille Packard Children's Hospital at Stanford (E.H.L., K.W.Y.), Stanford, California., Lawlor A; Insight Centre for Data Analytics (B.S.K., P.M., A.L.), University College Dublin, Dublin, Ireland., Killeen RP; From the Department of Radiology (B.S.K., G.M., H.D., R.P.K.), St. Vincent's University Hospital, Dublin, Ireland.
Jazyk:	angličtina
Zdroj:	AJNR. American journal of neuroradiology [AJNR Am J Neuroradiol] 2024 Feb 07; Vol. 45 (2), pp. 236-243. Date of Electronic Publication: 2024 Feb 07.
DOI:	10.3174/ajnr.A8104
Abstrakt:	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. (© 2024 by American Journal of Neuroradiology.)
Databáze:	MEDLINE
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