Zeng Ying, Long Chen, Zhao Wei, Liu Jun
Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China.
Department of Radiology, Xiangtan Central Hospital, Xiangtan 411199, China.
J Clin Med. 2022 Jul 11;11(14):4008. doi: 10.3390/jcm11144008.
Purpose: To develop a preliminary deep learning model that uses diffusion-weighted imaging (DWI) images to classify the severity of neurological impairment caused by ischemic stroke. Materials and Methods: This retrospective study included 851 ischemic stroke patients (711 patients in the training set and 140 patients in the test set). The patients’ NIHSS scores, which reflect the severity of neurological impairment, were reviewed upon admission and on Day 7 of hospitalization and were classified into two stages (stage 1 for NIHSS < 5 and stage 2 for NIHSS ≥ 5). A 3D-CNN was trained to predict the stage of NIHSS based on different preprocessed DWI images. The performance in predicting the severity of anterior and posterior circulation stroke was also investigated. The AUC, specificity, and sensitivity were calculated to evaluate the performance of the model. Results: Our proposed model obtained better performance in predicting the NIHSS stage on Day 7 of hospitalization than that at admission (best AUC 0.895 vs. 0.846). Model D trained with DWI images (normalized with z-score and resized to 256 × 256 × 64 voxels) achieved the best AUC of 0.846 in predicting the NIHSS stage at admission. Model E rained with DWI images (normalized with maximum−minimum and resized to 128 × 128 × 32 voxels) achieved the best AUC of 0.895 in predicting the NIHSS stage on Day 7 of hospitalization. Our model also showed promising performance in predicting the NIHSS stage on Day 7 of hospitalization for anterior and posterior circulation stroke, with the best AUCs of 0.905 and 0.903, respectively. Conclusions: Our proposed 3D-CNN model can effectively predict the neurological severity of IS using DWI images and performs better in predicting the NIHSS stage on Day 7 of hospitalization. The model also obtained promising performance in subgroup analysis, which can potentially help clinical decision making.
开发一种初步的深度学习模型，该模型利用扩散加权成像（DWI）图像对缺血性中风所致神经功能缺损的严重程度进行分类。材料与方法：这项回顾性研究纳入了851例缺血性中风患者（训练集711例，测试集140例）。患者入院时及住院第7天时的反映神经功能缺损严重程度的美国国立卫生研究院卒中量表（NIHSS）评分被复查，并被分为两个阶段（NIHSS＜5为第1阶段，NIHSS≥5为第2阶段）。基于不同预处理的DWI图像训练一个三维卷积神经网络（3D-CNN）来预测NIHSS阶段。还研究了该模型在预测前循环和后循环中风严重程度方面的表现。计算曲线下面积（AUC）、特异性和敏感性以评估模型的性能。结果：我们提出的模型在预测住院第7天时的NIHSS阶段方面比入院时表现更好（最佳AUC为0.895对0.846）。用DWI图像（经z分数标准化并调整大小为256×256×64体素）训练的模型D在预测入院时的NIHSS阶段方面达到了最佳AUC为0.846。用DWI图像（经最大-最小标准化并调整大小为128×128×32体素）训练的模型E在预测住院第7天时的NIHSS阶段方面达到了最佳AUC为0.895。我们的模型在预测住院第7天时前循环和后循环中风的NIHSS阶段方面也显示出良好的表现，最佳AUC分别为0.905和0.903。结论：我们提出的3D-CNN模型能够利用DWI图像有效预测缺血性中风的神经严重程度，且在预测住院第7天时的NIHSS阶段表现更佳。该模型在亚组分析中也取得了良好的表现，这可能有助于临床决策。
