Ability of Radiomics Versus Humans in Predicting First‐Pass Effect After Endovascular Treatment in the ESCAPE‐NA1 Trial

Abstract

Background First‐pass effect (FPE), that is, achieving reperfusion with a single thrombectomy device pass, is associated with better clinical outcomes in patients with acute stroke. FPE is therefore increasingly used as a marker of device and procedural efficacy. We aimed to evaluate the ability of thrombus‐based radiomics models to predict FPE in patients undergoing endovascular thrombectomy and compare performance with experts and nonradiomics thrombus characteristics. Methods Patients with thin‐slice noncontrast computed tomography and computed tomography angiography from the ESCAPE‐NA1 (Efficacy and Safety of Nerinetide for the Treatment of Acute Ischemic Stroke) trial were included. Thrombi were manually segmented on all images. Data were 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 set. The machine learning models were compared with 3 expert stroke physicians in predicting FPE in the test set using area under the receiver operating characteristic curves. Results Thin‐slice images of 554 patients were divided into a derivation set (training [n=388] and validation [n=55]) and a test set (n=111). A radiomics model using the combination of noncontrast computed tomography, computed tomography angiography, and noncontrast computed tomography–computed tomography angiography difference achieved the highest performance (area under the curve, 0.74 [95% CI, 0.64–0.84]) for prediction of FPE. This was higher than the mean area under the curve of the 3 experts (0.62 [95% CI, 0.53–0.71], P=0.01 for difference in area under the curves). The radiomics model also performed better than nonradiomics‐based thrombus features such as volume and permeability measurements in predicting FPE (P<0.05). Addition of device type did not improve the performance of the chosen radiomics model in predicting FPE. Conclusion A radiomics‐based machine learning model of thrombus characteristics from noncontrast computed tomography and computed tomography angiography performs better than experts and traditional nonradiomics imaging features in predicting FPE in patients with acute stroke treated with endovascular thrombectomy.

Keywords

• deep learning
• endovascular therapy
• ischemia
• machine learning
• stroke
• thrombus