A novel CT-based radiomics in the distinction of severity of coronavirus disease 2019 (COVID-19) pneumonia

BMC Infectious Diseases

Tongtong Zhao¹  Yuqing Gao²  Cancan Zhao²  Xiaolei Wang²  He Xu²  Zongyu Xie²  Shuhua Li²  Chunhong Hu³  Weiqun Ao⁴  Jian Wang⁴  Shaofeng Duan⁵  Haitao Sun⁶ 

[1] Department of Radiology, Fuyang Second People’s Hospital, No. 450 Linquan Road, 236000, Fuyang, Anhui, China;Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, No. 287 Changhuai Road, 233004, Bengbu, Anhui, China;Department of Radiology, The First Affiliated Hospital of Soochow University, No. 188, Street of Shizi, 200000, Suzhou, China;Department of Radiology, Tongde Hospital of Zhejiang Province, No.234, Gucui Road, 310012, Hangzhou, Zhejiang Province, China;GE Healthcare China, Pudong new town, No1, Huatuo road, 210000, Shanghai, China;Shanghai Institute of Medical Imaging, and Department of Interventional Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, 200032, Shanghai, China;

关键词: COVID-19;    Radiomics;    Tomography;    X-ray computed;    Nomogram;   

DOI  :  10.1186/s12879-021-06331-0

来源: Springer

【 摘 要 】

BackgroundConvenient and precise assessment of the severity in coronavirus disease 2019 (COVID-19) contributes to the timely patient treatment and prognosis improvement. We aimed to evaluate the ability of CT-based radiomics nomogram in discriminating the severity of patients with COVID-19 Pneumonia.MethodsA total of 150 patients (training cohort n = 105; test cohort n = 45) with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) test were enrolled. Two feature selection methods, Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), were used to extract features from CT images and construct model. A total of 30 radiomic features were finally retained. Rad-score was calculated by summing the selected features weighted by their coefficients. The radiomics nomogram incorporating clinical-radiological features was eventually constructed by multivariate regression analysis. Nomogram, calibration, and decision-curve analysis were all assessed.ResultsIn both cohorts, 40 patients with COVID-19 pneumonia were severe and 110 patients were non-severe. By combining the 30 radiomic features extracted from CT images, the radiomics signature showed high discrimination between severe and non-severe patients in the training set [Area Under the Curve (AUC), 0.857; 95% confidence interval (CI), 0.775–0.918] and the test set (AUC, 0.867; 95% CI, 0.732–949). The final combined model that integrated age, comorbidity, CT scores, number of lesions, ground glass opacity (GGO) with consolidation, and radiomics signature, improved the AUC to 0.952 in the training cohort and 0.98 in the test cohort. The nomogram based on the combined model similarly exhibited excellent discrimination performance in both training and test cohorts.ConclusionsThe developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia.

【 授权许可】

CC BY