Beyond Recanalization: Machine Learning-Based Insights into Post-Thrombectomy Vascular Morphology in Stroke Patients.
Authors
Affiliations (1)
Affiliations (1)
- From the Department of Mechanical Engineering, University of California, Riverside, CA, USA (A.D., K.L.), Department of Vascular Neurology and Neurocritical Care, Inova Neuroscience and Spine Institute, Inova Fairfax Medical Campus, Falls Church, VA, USA (P.T.F.), Stroke Branch, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA (M.L, L.L.L).
Abstract
Many stroke patients have poor outcomes despite successful endovascular therapy (EVT). We hypothesized that machine learning (ML)-based analysis of vascular changes post-EVT could identify macrovascular perfusion deficits such as residual hypoperfusion and distal emboli. Patients with anterior circulation large vessel occlusion (LVO) stroke, pre-and post-EVT MRI, and successful recanalization (mTICI 2b/3) were included. An ML algorithm extracted vascular features from pre-and 24-hour post-EVT MRA. A ≥100% increase in ipsilateral arterial branch length was considered significant. Perfusion deficits were defined using PWI, MTT, or distal clot presence; early neurological improvement (ENI) by a 24-hour NIHSS decrease ≥4 or NIHSS 0-1. Among 44 patients (median age 63), 71% had complete reperfusion. Those with distal clot had smaller arterial length increases (51% vs. 134%, p=0.05). ENI patients showed greater arterial length increases (161% vs. 67%, p=0.023). ML-based vascular analysis post-EVT correlates with perfusion deficits and may guide adjunctive therapy.ABBREVIATIONS: EVT = Endovascular Thrombectomy, LVO = Large Vessel Occlusion, ENI = Early Neurological Improvement, AIS = Acute Ischemic Stroke, mTICI = Modified Thrombolysis in Cerebral Infarction.