Radiomic deformation features to predict associations between affected brain regions and verbal fluency scores in acute stroke patients: preliminary finding.

Acute stroke is a systemic disease that leads to rapid cellular damage and functional impairments throughout brain regions, even those remote from the site of injury. It triggers widespread effects through various mechanisms, such as secondary degeneration of neurons, leading to diverse neurological deficits. Advances in machine learning approaches have shown promise towards predicting individualized outcomes at an early stage, through identifying imaging biomarkers of cognitive deficits. This ultimately allows for providing personalized treatment plans and early interventions to slow cognitive decline. In this study, we attempt to predict associations between the verbal fluency scores (VFS) and the brain regions with sub-clinical phenotypes of patients with acute stroke using a novel radiomic approach. Specifically, we extract radiomic features from T1-weighted (T1w) images that capture the structural alterations occurring in the brain regions due to stroke lesions. Our work explores the hypothesis that the degeneration occurring in brain regions, hence leading to cognitive impairment, can be quantified on routine imaging via radiomic features that capture the structural deformations resulting from stroke lesions. A retrospective analysis was conducted on the MRI scans (T1-weighted (T1w), FLAIR, Diffusion Weighted Imaging, and Apparent Diffusion Coefficient maps) of