基于深度学习的脑卒中后出血性转化预测模型。
A deep learning-based model for prediction of hemorrhagic transformation after stroke.
机构信息
Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
出版信息
Brain Pathol. 2023 Mar;33(2):e13023. doi: 10.1111/bpa.13023. Epub 2021 Oct 4.
Hemorrhagic transformation (HT) is one of the most serious complications after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) patients. The purpose of this study is to develop and validate deep-learning (DL) models based on multiparametric magnetic resonance imaging (MRI) to automatically predict HT in AIS patients. Multiparametric MRI and clinical data of AIS patients with EVT from two centers (data set 1 for training and testing: n = 338; data set 2 for validating: n = 54) were used in the DL models. The acute infarction area of diffusion-weighted imaging (DWI) and hypoperfusion of perfusion-weighted imaging (PWI) was labeled manually. Two forms of data sets (volume of interest [VOI] data sets and slice data sets) were analyzed, respectively. The models based on single parameter and multiparameter models were developed and validated to predict HT in AIS patients after EVT. Performance was evaluated by area under the receiver-operating characteristic curve (AUC), accuracy (ACC), sensitivity, specificity, negative predictive value, and positive predictive value. The results showed that the performance of single parameter model based on MTT (VOI data set: AUC = 0.933, ACC = 0.843; slice data set: AUC = 0.945, ACC = 0.833) and TTP (VOI data set: AUC = 0.916, ACC = 0.873; slice data set: AUC = 0.889, ACC = 0.818) were better than the other single parameter model. The multiparameter model based on DWI & MTT & TTP & Clinical (DMTC) had the best performance for predicting HT (VOI data set: AUC = 0.948, ACC = 0.892; slice data set: AUC = 0.932, ACC = 0.873). The DMTC model in the external validation set achieved similar performance with the testing set (VOI data set: AUC = 0.939, ACC = 0.884; slice data set: AUC = 0.927, ACC = 0.871) (p > 0.05). The proposed clinical, DWI, and PWI multiparameter DL model has great potential for assisting the periprocedural management in the early prediction HT of the AIS patients with EVT.
出血转化（HT）是急性缺血性脑卒中（AIS）患者血管内取栓后最严重的并发症之一。本研究旨在开发和验证基于多参数磁共振成像（MRI）的深度学习（DL）模型，以自动预测 AIS 患者的 HT。来自两个中心的 AIS 患者接受血管内取栓治疗后的多参数 MRI 和临床数据（数据集 1 用于训练和测试：n=338；数据集 2 用于验证：n=54）用于 DL 模型。弥散加权成像（DWI）的急性梗死区和灌注加权成像（PWI）的低灌注区由手动标记。分别分析了两种形式的数据集（感兴趣区[VOI]数据集和切片数据集）。基于单参数和多参数模型开发和验证了预测 EVT 后 AIS 患者 HT 的模型。通过受试者工作特征曲线下的面积（AUC）、准确性（ACC）、敏感度、特异度、阴性预测值和阳性预测值评估性能。结果表明，基于 MTTDWI 的单参数模型（VOI 数据集：AUC=0.933，ACC=0.843；切片数据集：AUC=0.945，ACC=0.833）和 TTPEVI 的性能优于其他单参数模型（VOI 数据集：AUC=0.916，ACC=0.873；切片数据集：AUC=0.889，ACC=0.818）。基于 DWI、MTT、TTP 和临床数据（DMTC）的多参数模型在预测 HT 方面表现最佳（VOI 数据集：AUC=0.948，ACC=0.892；切片数据集：AUC=0.932，ACC=0.873）。外部验证集中的 DMTC 模型与测试集具有相似的性能（VOI 数据集：AUC=0.939，ACC=0.884；切片数据集：AUC=0.927，ACC=0.871）（p>0.05）。提出的临床、DWI 和 PWI 多参数 DL 模型在预测 EVT 后 AIS 患者 HT 的早期辅助围手术期管理方面具有很大的潜力。
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