Abstract P333: Prediction of Stroke Lesion Growth Rates by Baseline Perfusion Imaging

Abstract

Objective: Computed Tomography Perfusion imaging (CTP) allows estimation of tissue status in patients with acute ischemic stroke. We aimed to improve prediction of the final infarct and individual infarct growth rates based on a deep learning approach. Methods: We trained a deep neural network to predict the final infarct volume in patients presenting with large vessel occlusions based on the native CTP images, time to reperfusion and reperfusion status in a derivation cohort (MR CLEAN study). The model was internally validated in a five-fold cross-validation and externally in an independent dataset (CRISP study). We calculated the mean absolute difference (MAD) between the predictions of the deep learning model and the final infarct volume versus the MAD between CTP processing by RAPID software and the final infarct volume. Next, we determined infarct growth rates for every patient. Results: We included 127 patients from the MR CLEAN (derivation) and 101 patients of the CRISP study (validation). The deep learning model improved final infarct lesion prediction compared to the RAPID software in both the derivation, MAD 34.5 vs 52.4ml, and validation cohort, 41.2 vs 52.4 ml, (p < 0.01). We obtained individual infarct growth rates enabling the estimation of final infarct volume based on time and grade of reperfusion. Interpretation: We validated a deep learning-based method which improved final infarct volume estimations compared to classic CTP processing. In addition, the deep learning model predicted individual infarct growth rates which could potentially enable the introduction of tissue clocks during the management of acute stroke. Figure A. Patient with a mean infarct growth of 18.3 ml/h. The final infarct volume was 104 ml. Recanalization was performed 131 min after CT perfusion with a mTICI = 2b. B . Patient with a mean infarct growth of 2.3 ml/h. The final infarct volume was 10.8 ml. Recanalization was performed 101 min after CT perfusion with a mTICI = 3.