Abstract 39: Ability Of Radiomics Versus Humans In Predicting First-pass Effect After Endovascular Treatment In The Escape-na1 Trial

Introduction: First-pass effect (FPE), i.e., achieving reperfusion with a single thrombectomy device pass, is associated with better clinical outcomes in patients with acute stroke. FPE is therefore increasingly being used as a marker of device and procedural efficacy. We evaluated the ability of thrombus-based radiomics models to predict FPE in patients undergoing endovascular thrombectomy (EVT) and compare performance to experts and non-radiomics thrombus characteristics. Methods: Patients with thin-slice non-contrast CT and CT angiography from The Efficacy and Safety of Nerinetide for the Treatment of Acute Ischemic Stroke (ESCAPE-NA1) trial were included. Thrombi were manually segmented on all images. Data was randomly split into a derivation set that included a training and a validation subset and an independent test set. Radiomics features were extracted from the derivation data set. Three expert stroke physicians reviewed baseline imaging and clinical data for the test set. The machine learning (ML) models were compared to the three experts in predicting the primary outcome (FPE) in the test set using area under the receiver operating characteristic curves (AUC-ROC). Results: A total of 554 patients with available thin-slice images comprised of a derivation set (training subset [n=388, 70%]), validation subset [n=55, 10%]), and a test set (n=111, 20%). FPE was seen in 31.8% in the derivation set and 31.5 % in the test set. AUC of the best radiomics model was 0.74 (95% CI: 0.64, 0.84), which was higher than the mean AUC of the three experts 0.60 (95% CI: 0.50, 0.71) ( P =0.009). Specificity of radiomics was better than the mean specificity of the three experts, 46 of 76 (60%) vs. 35 of 76 (46.4%), P =0.004, whereas sensitivity was not significantly different between radiomics (28 of 35 [79%]) and experts (27 of 35 [77%]). Moreover, radiomics features performed better than non-radiomics features such as thrombus volume and permeability measurements in predicting FPE ( P Conclusion: A radiomics-based ML model of thrombus characteristics on non-contrast CT and CT angiography performs better than experts and non-radiomics image characteristics in predicting FPE in patients with acute stroke treated with EVT.