Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19

Xiao-Hui Wang; Xiaopan Xu; Zhi Ao; Jun Duan; Xiaoli Han; Xing Tang; Yu-Fei Fu; Xu-Sha Wu; Xue Wang; Linxiao Zhu; Wenbing Zeng; Shuliang Guo

doi:10.3389/fmed.2021.730441

Elaboration of a Radiomics Strategy for the Prediction of the Re-positive Cases in the Discharged Patients With COVID-19

Front Med (Lausanne). 2021 Sep 16:8:730441. doi: 10.3389/fmed.2021.730441. eCollection 2021.

Authors

Xiao-Hui Wang¹, Xiaopan Xu², Zhi Ao¹, Jun Duan¹, Xiaoli Han¹, Xing Tang³, Yu-Fei Fu³, Xu-Sha Wu³, Xue Wang¹, Linxiao Zhu¹, Wenbing Zeng⁴, Shuliang Guo¹

Affiliations

¹ Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
² School of Biomedical Engineering, Air Force Medical University, Xi'an, China.
³ Department of Radiology, Xijing Hospital, Air Force Medical University, Xi'an, China.
⁴ Department of Radiology, Chongqing University Three Gorges Hospital, Chongqing, China.

Abstract

Objective: A considerable part of COVID-19 patients were found to be re-positive in the SARS-CoV-2 RT-PCR test after discharge. Early prediction of re-positive COVID-19 cases is of critical importance in determining the isolation period and developing clinical protocols. Materials and Methods: Ninety-one patients discharged from Wanzhou Three Gorges Central Hospital, Chongqing, China, from February 10, 2020 to March 3, 2020 were administered nasopharyngeal swab SARS-CoV-2 tests within 12-14 days, and 50 eligible patients (32 male and 18 female) with completed data were enrolled. Average age was 48 ± 11.5 years. All patients underwent non-enhanced chest CT on admission. A total of 568 radiomics features were extracted from the CT images, and 17 clinical factors were collected based on the medical record. Student's t-test and support vector machine-based recursive feature elimination (SVM-RFE) method were used to determine an optimal subset of features for the discriminative model development. Results: After Student's t-test, 62 radiomics features showed significant inter-group differences (p < 0.05) between the re-positive and negative cases, and none of the clinical features showed significant differences. These significant features were further selected by SVM-RFE algorithm, and a more compact feature subset containing only two radiomics features was finally determined, achieving the best predictive performance with the accuracy and area under the curve of 72.6% and 0.773 for the identification of the re-positive case. Conclusion: The proposed radiomics method has preliminarily shown potential in identifying the re-positive cases among the recovered COVID-19 patients after discharge. More strategies are to be integrated into the current pipeline to improve its precision, and a larger database with multi-clinical enrollment is required to extensively verify its performance.

Keywords: COVID-19; computed tomography; prediction; radiomics; re-positive cases.