Xie Gang, Gao Jin, Liu Jian, Zhou Xuwei, Zhao Zhengkai, Tang Wuli, Zhang Yue, Zhang Lingfeng, Li Kang
Department of Radiology, The Third People's Hospital of Chengdu, Chengdu, China.
Department of Radiology, Chongqing Liangjiang New Area People's Hospital, Chongqing, China.
BMC Med Imaging. 2025 May 30;25(1):199. doi: 10.1186/s12880-025-01745-7.
This study aims to develop an imaging-based machine learning model for evaluating the severity of ischemic stroke in the middle cerebral artery (MCA) territory.
This retrospective study included 173 patients diagnosed with acute ischemic stroke (AIS) in the MCA territory from two centers, with 114 in the training set and 59 in the test set. In the training set, spearman correlation coefficient and multiple linear regression were utilized to analyze the correlation between the CT imaging features of patients prior to treatment and the national institutes of health stroke scale (NIHSS) score. Subsequently, an optimal machine learning algorithm was determined by comparing seven different algorithms. This algorithm was then used to construct a imaging-based prediction model for stroke severity (severe and non-severe). Finally, the model was validated in the test set.
After conducting correlation analysis, CT imaging features such as infarction side, basal ganglia area involvement, dense MCA sign, and infarction volume were found to be independently associated with NIHSS score (P < 0.05). The Logistic Regression algorithm was determined to be the optimal method for constructing the prediction model for stroke severity. The area under the receiver operating characteristic curve of the model in both the training set and test set were 0.815 (95% CI: 0.736-0.893) and 0.780 (95% CI: 0.646-0.914), respectively, with accuracies of 0.772 and 0.814.
Imaging-based machine learning model can effectively evaluate the severity (severe or non-severe) of ischemic stroke in the MCA territory.
Not applicable.
本研究旨在开发一种基于影像学的机器学习模型，用于评估大脑中动脉（MCA）区域缺血性卒中的严重程度。
这项回顾性研究纳入了来自两个中心的173例诊断为MCA区域急性缺血性卒中（AIS）的患者，其中114例在训练集，59例在测试集。在训练集中，利用斯皮尔曼相关系数和多元线性回归分析治疗前患者的CT影像特征与美国国立卫生研究院卒中量表（NIHSS）评分之间的相关性。随后，通过比较七种不同算法确定最佳机器学习算法。然后使用该算法构建基于影像的卒中严重程度（重度和非重度）预测模型。最后，在测试集中对该模型进行验证。
进行相关性分析后，发现梗死侧、基底节区受累、大脑中动脉高密度征和梗死体积等CT影像特征与NIHSS评分独立相关（P<0.05）。确定逻辑回归算法是构建卒中严重程度预测模型的最佳方法。该模型在训练集和测试集的受试者操作特征曲线下面积分别为0.815（95%CI：0.736-0.893）和0.780（95%CI：0.646-0.914），准确率分别为0.772和0.814。
基于影像学的机器学习模型可以有效评估MCA区域缺血性卒中的严重程度（重度或非重度）。
不适用。
