Beyond Recanalization: Machine Learning-Based Insights into Post-Thrombectomy Vascular Morphology in Stroke Patients

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.