Abstract TP98: Deep Learning-based Automatic Ischemic Core Estimation Based On Non-contrastenhanced Computed Tomography

Abstract

Background/Purpose: With the spread of thrombus retrieval procedures, the demand for immediate diagnosis and severity assessment of ischemic stroke is increasing, but current standard method using ASPECTS is semi-quantitative, and has low consistency among physicians. We develop and validate the fully automatic machine learning-based ischemic core segmentation model based only on non-contrast-enhanced computed tomography (NCCT). Materials and Methods: This multicenter retrospective study included patients with anterior circulation acute ischemic stroke who received both CT and MRI before thrombolysis or recanalization treatment between 2013 to 2019. The ischemic core on CT was manually delineated referencing the DWI and ADC map. A deep learning-based ischemic core segmentation model was developed using the derivation cohort data from 3 institutions (n=313), and the model performance was validated on the external validation cohort data from 3 institutions (n=106). Results: On the validation cohort, the median time between CT and MRI was 18 min. The ischemic core volume calculated by the DL model was significantly correlated with the reference standard (Pearson r=0.91, p<.001). The correlation was significant both in early time window (<4.5hr from onset; Pearson r=0.91, p<.0.01) and the late time window (>4.5hr from onset; Pearson r=0.93, p<0.01). The median difference between model and reference standard was 4.7 mL (IQR, 0.8-12.4 mL). The DL model displayed high accuracy on discriminating large ischemic core (>70mL) with a sensitivity of 84.2%, specificity of 97.7%, and AUC of 0.91. Conclusions: A segmentation model of cerebral ischemic core volume of the anterior circulation based on NCCT using deep learning showed good correlation with the correct labels generated with reference to MRI.