Chest computed tomography radiomics to predict the outcome for patients with COVID-19 at an early stage

Shan Wu; Ranying Zhang; Xinjian Wan; Ting Yao; Qingwei Zhang; Xiaohua Chen; Xiaohong Fan

doi:10.5152/dir.2022.21576

Chest computed tomography radiomics to predict the outcome for patients with COVID-19 at an early stage

Diagn Interv Radiol. 2023 Jan 31;29(1):91-102. doi: 10.5152/dir.2022.21576. Epub 2023 Jan 18.

Authors

Shan Wu¹, Ranying Zhang², Xinjian Wan¹, Ting Yao³, Qingwei Zhang⁴, Xiaohua Chen³, Xiaohong Fan⁵

Affiliations

¹ Department of Endoscopy, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
² Department of Radiology, Zhongshan Hospital, Fudan University, and Shanghai Institute of Medical Imaging, Shanghai, China.
³ Department of Infectious Disease, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁴ Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China.
⁵ Department of Respiratory Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine; Department of Respiratory and Critical Care, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.

Abstract

Purpose: Early monitoring and intervention for patients with novel coronavirus disease-2019 (COVID-19) will benefit both patients and the medical system. Chest computed tomography (CT) radiomics provide more information regarding the prognosis of COVID-19.

Methods: A total of 833 quantitative features of 157 COVID-19 patients in the hospital were extracted. By filtering unstable features using the least absolute shrinkage and selection operator algorithm, a radiomic signature was built to predict the prognosis of COVID-19 pneumonia. The main outcomes were the area under the curve (AUC) of the prediction models for death, clinical stage, and complications. Internal validation was performed using the bootstrapping validation technique.

Results: The AUC of each model demonstrated good predictive accuracy [death, 0.846; stage, 0.918; complication, 0.919; acute respiratory distress syndrome (ARDS), 0.852]. After finding the optimal cut-off for each outcome, the respective accuracy, sensitivity, and specificity were 0.854, 0.700, and 0.864 for the prediction of the death of COVID-19 patients; 0.814, 0.949, and 0.732 for the prediction of a higher stage of COVID-19; 0.846, 0.920, and 0.832 for the prediction of complications of COVID-19 patients; and 0.814, 0.818, and 0.814 for ARDS of COVID-19 patients. The AUCs after bootstrapping were 0.846 [95% confidence interval (CI): 0.844-0.848] for the death prediction model, 0.919 (95% CI: 0.917-0.922) for the stage prediction model, 0.919 (95% CI: 0.916-0.921) for the complication prediction model, and 0.853 (95% CI: 0.852-0.0.855) for the ARDS prediction model in the internal validation. Based on the decision curve analysis, the radiomics nomogram was clinically significant and useful.

Conclusion: The radiomic signature from the chest CT was significantly associated with the prognosis of COVID-19. A radiomic signature model achieved maximum accuracy in the prognosis prediction. Although our results provide vital insights into the prognosis of COVID-19, they need to be verified by large samples in multiple centers.

Keywords: COVID-19; Radiomic signature; prediction; prognosis.

MeSH terms

Algorithms
COVID-19* / diagnostic imaging
Humans
Nomograms
Respiratory Distress Syndrome* / diagnostic imaging
Retrospective Studies
Tomography, X-Ray Computed