基于基线灌注成像预测脑梗死的生长速率。
Prediction of Stroke Infarct Growth Rates by Baseline Perfusion Imaging.
机构信息
Department of Neurology, University Hospitals Leuven, Belgium (A.W., R.L.).
Department of Neurosciences, Experimental Neurology, KU Leuven - University of Leuven, Belgium (A.W., R.L.).
出版信息
Stroke. 2022 Feb;53(2):569-577. doi: 10.1161/STROKEAHA.121.034444. Epub 2021 Sep 30.
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 (<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.
背景与目的
计算机断层灌注成像可用于评估急性缺血性脑卒中患者的组织状态。我们旨在通过深度学习方法提高对最终梗死体积和个体梗死增长率的预测。
方法
我们在一个推导队列（MR CLEAN 试验[荷兰多中心急性缺血性卒中血管内治疗的随机临床试验]）中，基于患者的原始计算机断层灌注图像、再灌注时间和再灌注状态，训练了一个深度神经网络来预测急性大血管闭塞性脑卒中患者的最终梗死体积。该模型在 5 折交叉验证中进行了内部验证，并在一个独立数据集（CRISP 研究[CT 灌注预测缺血性卒中再灌注反应项目]）中进行了外部验证。我们计算了深度学习模型预测的最终梗死体积与 RAPID 软件（iSchemaView，加利福尼亚州门洛帕克）处理的计算机断层灌注成像与最终梗死体积之间的平均绝对差，以及深度学习模型预测的最终梗死体积与平均绝对差。接下来，我们确定了每个患者的梗死增长率。
结果
我们纳入了来自 MR CLEAN（推导）的 127 名患者和 CRISP 研究（验证）的 101 名患者。与 RAPID 软件相比，深度学习模型在推导组和验证组中都提高了最终梗死体积的预测，平均绝对差分别为 34.5 毫升和 52.4 毫升，41.2 毫升和 52.4 毫升（<0.01）。我们获得了个体梗死增长率，能够根据再灌注时间和程度来估计最终梗死体积。
结论
我们验证了一种基于深度学习的方法，该方法与经典的计算机断层灌注成像处理相比，提高了最终梗死体积的估计。此外，深度学习模型预测了个体梗死增长率，这可能会在急性脑卒中的治疗中引入组织时钟。
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