Diffusion-weighted imaging-based DenseNet model for prediction of TOAST etiological typing in acute ischemic stroke
10.3760/cma.j.cn112149-20240508-00255
- VernacularTitle:基于扩散加权成像的密集卷积神经网络模型预测急性缺血性脑卒中TOAST病因分型的研究
- Author:
Pahati TUXUNJIANG
1
;
Wei ZHAO
;
Hanjiaerbieke KUKUN
;
Rui XU
;
Yifan CHANG
;
Ainikaerjiang AIHEMAITI
;
Zheng XU
;
Yunling WANG
Author Information
1. 新疆医科大学第一附属医院影像中心，乌鲁木齐 830054
- Keywords:
Stroke;
Deep learning;
Diffusion-weighted imaging;
Etiological classification
- From:
Chinese Journal of Radiology
2024;58(10):1015-1020
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of a deep learning model based on diffusion-weighted imaging (DWI) in quick identification of the TOAST etiology classification in patients with acute ischemic stroke (AIS).Methods:In this cross-sectional study, imaging and clinical data of 504 patients with AIS admitted to the First Affiliated Hospital of Xinjiang Medical University from March 2023 to February 2024 were retrospectively reviewed. Using the TOAST etiology classification, there were 252 large artery atherosclerosis type and 252 small-artery occlusion type. The 504 cases were divided into a training set ( n=302), a validation set ( n=101) and a test set ( n=101) using stratified randomization in the ratio of 6∶2∶2. All cases had DWI data. A DenseNet network framework was used to construct DenseNet models by optimizing the model configurations of different layers. Three DenseNet models with different layers (121, 169, 201) were constructed, named DenseNet169 model, DenseNet121 model, and DenseNet201 model. The data enhancement, Adam optimizer and cross-entropy loss function methods were used to improve the convergence speed and robustness of the model, and to balance the positive and negative sample imbalance problem. Independent sample t-test or χ2 was used to compare the clinical data of patients with large artery atherosclerosis type and small-artery occlusion type AIS. Receiver operating characteristic curves and area under the curve (AUC) were performed to evaluate the efficacy of each model in identification of patients with large artery atherosclerosis type and small-artery occlusion type AIS. Results:There were statistically significant differences in age, National Institutes of Health Stroke Scale score at admission, and stenosis or occlusion of large vessels between patients with large artery atherosclerosis type and small-artery occlusion (all P<0.05). In the test set, the AUC, sensitivity, accuracy, and F1 score values of the DenseNet201 model for discriminating patients with large artery atherosclerosis type AIS and small-artery occlusion type AIS (0.826, 0.902, 0.743, 0.780, respectively) were higher than those of DenseNet121 (0.801, 0.647, 0.723, 0.702, respectively) and DenseNet169 model (0.778, 0.882, 0.733, 0.769). Conclusions:The deep learning models based DWI constructed in this study can help with the TOAST etiology classification of AIS cases. DenseNet201 model shows the best and stable performance in the deep learning-based classification.
