Radiomics Model for Prognosis of Brainstem Stroke Based on Lesion and Surrounding Features

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.