Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Feasibility of a Clinical-Radiomics Model to Predict the Outcomes of Acute Ischemic Stroke

  • Yiran Zhou (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Di Wu (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Su Yan (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Yan Xie (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Shun Zhang (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Wenzhi Lv (Department of Artificial Intelligence, Julei Technology) ;
  • Yuanyuan Qin (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Yufei Liu (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Chengxia Liu (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Jun Lu (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Jia Li (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Hongquan Zhu (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Weiyin Vivian Liu (MR Research, GE Healthcare) ;
  • Huan Liu (Advanced Application Team, GE Healthcare) ;
  • Guiling Zhang (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology) ;
  • Wenzhen Zhu (Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology)
  • Received : 2022.03.14
  • Accepted : 2022.04.26
  • Published : 2022.08.01
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Abstract

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

Keywords

Acknowledgement

This research was funded by the National Natural Science Foundation of China (grant no: 81730049, 81801666, and 82102024).

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