Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging.
Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging.
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DOI：
10.1161/strokeaha.121.034444
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发表时间：
2022-03
期刊：
影响因子：
8.3
通讯作者：
Lemmens R
中科院分区：
文献类型：
--
作者：
Wouters A;Robben D;Christensen S;Marquering HA;Roos YBWEM;van Oostenbrugge RJ;van Zwam WH;Dippel DWJ;Majoie CBLM;Schonewille WJ;van der Lugt A;Lansberg M;Albers GW;Suetens P;Lemmens R
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 using a deep learning approach. We trained a deep neural network to predict the final infarct volume in acute stroke patients presenting with large vessel occlusions based on the native CTP images, time to reperfusion and reperfusion status in a derivation cohort (MR CLEAN trial). 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. We included 127 patients from the MR CLEAN (derivation) and 101 patients of the CRISP study (validation). The deep learning model improved final infarct volume 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. 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 enable the introduction of tissue clocks during the management of acute stroke.
