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

Abstract

Background Convenient 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. Methods A 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. Results In 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. Conclusions The developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia.

A meta-analysis of the diagnostic test accuracy of CT-based radiomics for the prediction of COVID-19 severity

“…Four studies were excluded from the analysis as follows: one observational study [ 24 ], which used a repetitive patient population, one observational study [ 15 ], which used pulmonary opacities on chest images to predict disease severity, and two observational studies [ 25 , 26 ], which used other severity assessment protocols to predict disease outcome. Finally, eight articles were used for qualitative analysis [ 27 – 34 ]. Only seven reports were included in the meta-analysis as a study by Li et al [ 34 ] was excluded because only patients with severe COVID-19 were included in the report.…”

Prediction of potential severe coronavirus disease 2019 patients based on CT radiomics: A retrospective study

“…Among these works, many researches [22][23][24][25] focused on the lesions, not used the patients as subjects. Their socalled thousands of cases were actually thousands of lesions; many researches [26][27][28][29][30][31] built models to predict the COVID-19 patients' current severity status, not the potential severe risk. There were also some works [32][33][34] similar to our work in research ideas and methods.…”

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma

“…To reduce the variation caused by different CT scanners acquisition parameters, all images were resampled to the same voxel size (1mm*1mm*1mm) with the linear interpolation method ( 20 ). The volumes of interest (VOIs) of lesion areas were segmented by a radiologist (a 2-year work experience) semi-automatically with ITK-SNAP software (version 3.8.itksnap.org).…”