Non-contrast CT radiomics and machine learning for outcomes prediction of patients with acute ischemic stroke receiving conventional treatment

作者全名："Zhang, Limin; Wu, Jing; Yu, Ruize; Xu, Ruoyu; Yang, Jiawen; Fan, Qianrui; Wang, Dawei; Zhang, Wei"

作者地址："[Zhang, Limin; Zhang, Wei] Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, Chongqing, Peoples R China; [Wu, Jing] Army Med Univ, Southwest Hosp, Hosp 958, Dept Radiol, Chongqing, Peoples R China; [Yu, Ruize; Fan, Qianrui; Wang, Dawei] Infervis Med Technol Co Ltd, Yuanyang Int Ctr, Inst Res, 25F Bldg E, Beijing 100025, Peoples R China; [Xu, Ruoyu] Chongqing Med Univ, Affiliated Hosp 2, Dept Neurol, Chongqing, Peoples R China; [Yang, Jiawen] Wenzhou Med Univ, Taizhou Hosp Zhejiang Prov, Dept Radiol, Taizhou 317000, Zhejiang, Peoples R China"

通信作者："Zhang, W (通讯作者)，Chongqing Med Univ, Affiliated Hosp 2, Dept Radiol, Chongqing, Peoples R China."

来源：EUROPEAN JOURNAL OF RADIOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001045029600001

JCR分区：Q1

影响因子：3.2

年份：2023

卷号：165

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Acute ischemic stroke; Non-contrast computed tomography; Prognosis; Radiomics; Support vector machine

摘要："Purpose: Accurate prediction of outcomes for patients with acute ischemic stroke (AIS) is crucial for clinical decision-making. In this study, we developed prediction models based on non-contrast computed tomography (NCCT) radiomics and clinical features to predict the modified Rankin Scale (mRS) six months after hospital discharge.Method: A two-center retrospective cohort of 240 AIS patients receiving conventional treatment was included. Radiomics features of the infarct area were extracted from baseline NCCT scans. We applied Kruskal-Wallis (KW) test and recursive feature elimination (RFE) to select features for developing clinical, radiomics, and fusion models (with clinical data and radiomics features), using support vector machine (SVM) algorithm. The pre-diction performance of the models was assessed by accuracy, sensitivity, specificity, F1 score, and receiver operating characteristic (ROC) curve. Shapley Additive exPlanations (SHAP) was applied to analyze the inter-pretability and predictor importance of the model.Results: A total of 1454 texture features were extracted from the NCCT images. In the test cohort, the ROC analysis showed that the radiomics model and the fusion model showed AUCs of 0.705 and 0.857, which out-performed the clinical model (0.643), with the fusion model exhibiting the best performance. Additionally, the accuracy and sensitivity of the fusion model were also the best among the models (84.8% and 93.8%, respectively).Conclusions: The model based on NCCT radiomics and machine learning has high predictive efficiency for the prognosis of AIS patients receiving conventional treatment, which can be used to assist early personalized clinical therapy."

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