Keywords: Radiomics, Radiomics, Stroke
Motivation: Stroke is a major global health issue, necessitating early outcome prediction for optimal treatment. Brainstem stroke, often overlooked, requires dedicated predictive models due to its unique challenges.
Goal(s): Develop radiomics models to predict brainstem stroke outcomes, considering infarct edge and surrounding regions, improving prognosis, and simplifying clinical evaluation.
Approach: 474 patients were studied, and radiomics features were extracted from diffusion-weighted images. Machine learning models were trained using SVM, RF, KNN and AdaBoost algorithms.
Results: The RF model, based on the circle2 region, exhibited the highest performance (AUC=0.84). Models in the circle region outperformed core.
Impact: Our specialized radiomics models offer a valuable tool for personalized brainstem stroke treatment planning, potentially enhancing patient outcomes.
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