Gu Hongxian, Yan Yuting, He Xiaodong, Xu Yuyun, Wei Yuguo, Shao Yuan
Department of Radiology, The People's Hospital of Jianyang City, Jianyang, Sichuan Province, China.
Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Hangzhou, Zhejiang, China.
Front Neuroinform. 2024 Aug 22;18:1400702. doi: 10.3389/fninf.2024.1400702. eCollection 2024.
This study aimed to develop a radiomic model based on non-contrast computed tomography (NCCT) after interventional treatment to predict the clinical prognosis of acute ischemic stroke (AIS) with large vessel occlusion.
We retrospectively collected 141 cases of AIS from 2016 to 2020 and analyzed the patients' clinical data as well as NCCT data after interventional treatment. Then, the total dataset was divided into training and testing sets according to the subject serial number. The cerebral hemispheres on the infarct side were segmented for radiomics signature extraction. After radiomics signatures were standardized and dimensionality reduced, the training set was used to construct a radiomics model using machine learning. The testing set was then used to validate the prediction model, which was evaluated based on discrimination, calibration, and clinical utility. Finally, a joint model was constructed by incorporating the radiomics signatures and clinical data.
The AUCs of the joint model, radiomics signature, NIHSS score, and hypertension were 0.900, 0.863, 0.727, and 0.591, respectively, in the training set. In the testing set, the AUCs of the joint model, radiomics signature, NIHSS score, and hypertension were 0.885, 0.840, 0.721, and 0.590, respectively.
Our results provided evidence that using post-interventional NCCT for a radiomic model could be a valuable tool in predicting the clinical prognosis of AIS with large vessel occlusion.
本研究旨在基于介入治疗后的非增强计算机断层扫描（NCCT）开发一种放射组学模型，以预测大血管闭塞性急性缺血性卒中（AIS）的临床预后。
我们回顾性收集了2016年至2020年的141例AIS病例，并分析了患者的临床数据以及介入治疗后的NCCT数据。然后，根据受试者序列号将总数据集分为训练集和测试集。对梗死侧的大脑半球进行分割以提取放射组学特征。在对放射组学特征进行标准化和降维后，使用训练集通过机器学习构建放射组学模型。然后使用测试集验证预测模型，并基于区分度、校准度和临床实用性对其进行评估。最后，通过整合放射组学特征和临床数据构建联合模型。
在训练集中，联合模型、放射组学特征、美国国立卫生研究院卒中量表（NIHSS）评分和高血压的曲线下面积（AUC）分别为0.900、0.863、0.727和0.591。在测试集中，联合模型、放射组学特征、NIHSS评分和高血压的AUC分别为0.885、0.840、0.721和0.590。
我们的结果表明，将介入治疗后的NCCT用于放射组学模型可能是预测大血管闭塞性AIS临床预后的一种有价值的工具。
