Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging

Background and Purpose: Computed tomography perfusion imaging 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. Methods: We trained a deep neural network to predict the final infarct volume in patients with acute stroke presenting with large vessel occlusions based on the native computed tomography perfusion images, time to reperfusion and reperfusion status in a derivation cohort (MR CLEAN trial [Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands]). The model was internally validated in a 5-fold cross-validation and externally in an independent dataset (CRISP study [CT Perfusion to Predict Response to Recanalization in Ischemic Stroke Project]). We calculated the mean absolute difference between the predictions of the deep learning model and the final infarct volume versus the mean absolute difference between computed tomography perfusion imaging processing by RAPID software (iSchemaView, Menlo Park, CA) 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 volume prediction compared with the RAPID software in both the derivation, mean absolute difference 34.5 versus 52.4 mL, and validation cohort, 41.2 versus 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. Conclusions: We validated a deep learning-based method which improved final infarct volume estimations compared with classic computed tomography perfusion imaging 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.

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

Stroke ◽

10.1161/str.52.suppl_1.p333 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Anke Wouters ◽

David Robben ◽

Soren Christensen ◽

Henk Marquering ◽

Yvo Roos ◽

...

Keyword(s):

Deep Learning ◽

Perfusion Imaging ◽

Growth Rates ◽

Ct Perfusion ◽

Infarct Volume ◽

Learning Model ◽

Absolute Difference ◽

Derivation Cohort ◽

Mr Clean ◽

Deep Learning Model

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.

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