Li Guirui, Zhang Yueling, Tang Jian, Chen Shijian, Liu Qianqian, Zhang Jian, Shi Shengliang
Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning.
Department of Neurology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China.
Neurologist. 2025 Mar 1;30(2):93-101. doi: 10.1097/NRL.0000000000000599.
The evaluation of the prognosis of patients with acute ischemic stroke (AIS) is of great significance in clinical practice. We aim to evaluate the feasibility and effectiveness of diffusion-weighted imaging (DWI) image-based radiomics features and machine learning methods in predicting 90-day prognosis among patients with AIS.
We enrolled a total of 171 patients with AIS in this study, including 134 patients with a good prognosis and 37 patients with a poor prognosis, and collected the patients' clinical and DWI image data. Radiomics features from manually sketched ischemic lesions were extracted using the Pyradiomics package of Python, and the best radiomics features were selected by a t test and the least absolute shrinkage and selection operator. The radiomics model and clinical model were constructed using support vector machine and logistic regression, respectively, and the predictive performance of each model was evaluated.
We selected 9 features from a total of 851 radiomics features to build the final radiomics model. For predicting the poor prognosis of patients with AIS, the area under the curves, accuracy, sensitivity and specificity of the clinical model, radiomics model in the training set and radiomics model in the testing set were 0.865, 0.930 and 0.906, 81.3%, 92.0% and 90.0%, 81.1%, 76.0% and 75.0%, and 81.3%, 97.0% and 95.0%, respectively.
DWI image-based radiomics features and machine learning methods can accurately predict the 90-day prognosis of patients with AIS, and the radiomics model is superior to the clinical model in predicting prognosis.
急性缺血性卒中（AIS）患者预后评估在临床实践中具有重要意义。我们旨在评估基于扩散加权成像（DWI）图像的放射组学特征和机器学习方法预测AIS患者90天预后的可行性和有效性。
本研究共纳入171例AIS患者，其中预后良好者134例，预后不良者37例，收集患者临床及DWI图像数据。使用Python的Pyradiomics包提取手动勾勒的缺血性病变的放射组学特征，并通过t检验和最小绝对收缩和选择算子选择最佳放射组学特征。分别使用支持向量机和逻辑回归构建放射组学模型和临床模型，并评估各模型的预测性能。
我们从总共851个放射组学特征中选择了9个特征来构建最终的放射组学模型。对于预测AIS患者的不良预后，临床模型、训练集中的放射组学模型和测试集中的放射组学模型的曲线下面积、准确率、敏感性和特异性分别为0.865、0.930和0.906，81.3%、92.0%和90.0%，81.1%、76.0%和75.0%，以及81.3%、97.0%和95.0%。
基于DWI图像的放射组学特征和机器学习方法可以准确预测AIS患者的90天预后，且放射组学模型在预测预后方面优于临床模型。
