{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:46:27Z","timestamp":1757619987745,"version":"3.44.0"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Multiple system atrophy (MSA) is a rapidly progressive neuro-degenerative disorder characterized by autonomic dysfunction, levodopa- unresponsive parkinsonism, cerebellar ataxia, and corticospinal tract involvement. Early diagnosis remains challenging due to overlapping clinical manifestations and the absence of reliable biomarkers. This study aimed to develop a radiomics-based diagnostic model using multimodal MRI to improve MSA detection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A retrospective cohort of 62 clinically probable MSA patients (per the 2022 Movement Disorder Society criteria), and 73 matched healthy controls underwent 3.0-T MRI (T1WI, T2WI, FLAIR, DWI). Seven brain regions (bilateral cerebellar hemispheres, middle cerebellar peduncles, putamen, and pons) were manually segmented. A total of 1,502 radiomics features were extracted per region, using PyRadiomics (IBSI-compliant). Features with an intraclass correlation coefficient (ICC) \u2265 0.75 were retained, and the least absolute shrinkage and selection operator (LASSO) regression identified the top discriminative features to construct region-specific radiomics scores (Rad-scores). A logistic regression (LR) model integrated Rad-scores from all regions. Model performance was evaluated via precision, recall, and F1-score in training, testing, and validation cohorts (split ratio 6:2:2), and compared with visual assessments by two radiologists.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The LR model achieved high performance: accuracy was 0.98 in the training cohort, 0.97 in the testing cohort, and 0.95 in the validation cohort. Notably, classification precision for MSA reached 1.0 (indicating no false positives) across all cohorts. SHapley Additive exPlanations (SHAP) analysis revealed that the left putamen Rad-score as the most influential predictor. The model significantly outperformed radiologists' visual assessments (radiologist AUCs: 0.559 and 0.535; <jats:italic>P<\/jats:italic> &amp;lt; 0.001). Asymmetry was observed, with left-hemisphere structures (putamen\/cerebellar) exhibiting greater diagnostic contributions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Multimodal MRI radiomics accurately differentiates MSA from healthy controls, even in the absence of conventional MRI markers. The Rad-score model demonstrates high sensitivity (89% recall in the validation cohort) and perfect specificity (100% precision), providing a clinically actionable tool for early MSA diagnosis.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fneur.2025.1650350","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T04:11:40Z","timestamp":1757304700000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a radiomics-based model for diagnosis of multiple system atrophy using multimodal 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