Abstract DP364: Association of deep learning segmented ischemic core hypodensity on non-contrast CT with endovascular treatment benefit

Background: Endovascular treatment (EVT) is effective for patients with large ischemic cores caused by a large vessel occlusion. The size and severity of hypodense tissue on non-contrast CT (NCCT) in the ischemic core predict poor outcomes despite successful EVT. Automated ASPECTS tools can estimate the size and severity of hypodensity on NCCT, but these estimates do not accurately represent the ischemic core. Deep learning (DL) based segmentation of hypodensity on NCCT may offer improved precision. We compared the associations between (1) ischemic core volume on CTP, (2) manually segmented, or (3) DL segmented hypodensity volumes on NCCT and outcomes after EVT or medical management (MM) in patients with a large ischemic core. Methods: We performed a post-hoc analysis of SELECT2 trial patients. The volume of total hypodense (all segmented voxels) and severely hypodense (segmented voxels with ≤26 HU) tissue was measured on baseline NCCT using an externally validated DL model or manual segmentations (Figure 1). The ischemic core volume was determined using CTP (rCBF