Abstract
Introduction: Application of machine learning (ML) algorithms has shown promising results in estimating ischemic core volumes using routine non-contrast CT (NCCT). We aimed to assess the performance of the e-Stroke Suite software (Brainomix, Oxford, United Kingdom) in assessing ischemic core volumes on NCCT compared to CTP in patients with acute ischemic stroke. Methods: In this retrospective study, consecutive patients with anterior circulation large vessel occlusions who underwent pretreatment NCCT and CTP and posttreatment MRI were included. Ischemic core volumes were automatically calculated on NCCTs using e-Stroke Suite (Brainomix) which uses a combination of traditional 3D graphics and ML classification techniques to identify ischemic core voxels. Estimated ischemic core volumes were also automatically calculated from CTP using Olea Sphere (Olea Medical SAS, SP23) using a combination of rCBF<25% and differential Time-to-peak (dTTP)>5 sec. Estimated core volumes were compared against the final infarct volume on posttreatment MRI in patients who achieved successful reperfusion (mTICI ≥2b). Results: 83 patients [52 female; age (mean ± SD): 73.1 ±15.3] were included. The estimated ischemic core volumes (mean ± SD) were 18.9 ± 13.5 mL on NCCT and 17.5 ± 16.5 mL on CTP, not significantly different (p=0.54) and demonstrated significant correlation (r=0.51, p<0.001) ( Figure 1 ). Among patients with successful recanalization (n=49), there was no significant difference in estimated ischemic core volume between NCCT vs. CTP (p=0.80) and NCCT vs. MRI (p=0.38). There was significant correlation between estimated ischemic core volume on NCCT vs. CTP (r=0.75, p<0.001) and vs. final MRI infarct volume (r=0.75, p<0.001). Conclusions: Results show estimated ischemic core volumes obtained automatically by ML-based approach (Brainomix) on NCCT correlates well with ischemic core volumes on acute CTP and with post-treatment MR infarct volume.
Published Version
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