Deep-learning-derived neuroimaging biomarkers of sarcopenia as predictors of outcome in endovascular thrombectomy in large vessel occlusion acute ischemic stroke.

Background and PurposeSarcopenia is an age-related syndrome that is associated with poor outcomes in many disease states. In this study, we aimed to evaluate the utility of muscle biomarkers of sarcopenia in predicting clinical outcomes for patients with large vessel occlusion (LVO) acute ischemic stroke (AIS).MethodsThis was a single-center observational cohort study of consecutive patients that underwent endovascular thrombectomy (EVT) for LVO AIS. A deep-learning model was employed to segment and measure the volume, surface area, and maximum thickness of temporalis and sternocleidomastoid (SCM) muscles. The primary outcome was functional independence (FI), defined by a modified Rankin Scale of 0-2 at 3 months post-stroke. Univariable and multivariable logistic regression models were performed to evaluate associations between muscle biomarkers and outcome measures after adjusting for clinical variables of age, sex, and National Institute of Health Stroke Scale (NIHSS), and successful recanalization status which was defined as a thrombolysis in cerebral infarction scale of 2B, 2C, or 3.ResultsIn total, 122 (41.1%) of 297 included patients achieved FI. For each 10 cm