Deep learning-based non-contrast CT imaging markers enhance post-transfer DWI core volume prediction

Deep learning enables the extraction of ischemic lesion size and hypodensity imaging markers from noncontrast CT (DLNCCT) in patients with acute ischemic stroke, but it remains unclear whether those markers can predict post-transfer core volume.

We performed a post-hoc analysis of prospectively enrolled patients transferred from a primary to a comprehensive center (PSC/CSC) for endovascular treatment (EVT). Using a validated deep-learning NCCT segmentation method, we quantified total lesion volume (per 10mL), modified net water uptake (mNWU %), and severely hypodense volume (≤26 HU per 10mL) and compared these markers with core-lab-rated ASPECTS (per point decrease) and CTP-based evaluation for their association with (adjusted regression coefficient [95%CI]) and predictive performance in addition to baseline variables (R2±SE) for post-transfer CSC-admission DWI core volume.

We included 420 patients (239[57%] males) with a median age of 72 years (IQR:61;80). We observed 11.2mL (95%CI:8.3;14.1] larger post-transfer core volumes per point decrease in ASPECTS, 10.0mL (95%CI:6.8;13.3) and 20.0mL (95%CI:12.7;27.2) larger post-transfer core volumes per 10 mL increase in total and severely hypodense DLNCCT volume, respectively. mNWU was not associated with post-transfer core volume (p=0.63). In addition to clinical baseline and CTA variables, post-transfer core volume prediction with ASPECTS (R2:0.49±0.02) and DLNCCT (R2:0.50±0.02) did not differ significantly (p=0.58). Compared with using CTP imaging markers (R2:0.56±0.02), adding ASPECTS (R2:0.63±0.02, p<0.01) and DLNCCT (R2:0.65±0.01, p<0.01) improved performance for post-transfer core volume prediction.

Total and severely hypodense DLNCCT volumes are independent predictors for post-transfer core volume. These DLNCCT markers improved CTP-based post-transfer core volume prediction.