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

PURPOSE: Coronavirus disease 2019 (COVID-19) is a recently declared worldwide pandemic. Triaging of patients into severe and non-severe could further help in targeted management. "Potential severe patients" is a category of patients who did not have severe symptoms at their initial diagnosis, but eventually progressed to be severe patients and are easily overlooked in the early stage. This work aimed to develop and evaluate a CT-based radiomics signature for the prediction of these potential severe COVID-19 patients. METHODS: One hundred fifty COVID-19 patients were enrolled and randomly divided into cross-validation and independent test sets. First, their clinical characteristics were screened using the univariate and multivariate logistic regression step by step. Then, radiomics features were extracted from the lesions on their chest CT images. Subsequently, the inter- and intra-class correlation coefficients (ICC) analysis, minimum-redundancy maximum-relevance (mRMR) selection, and the least absolute shrinkage and selection operator (LASSO) algorithm were used step by step for feature selection and construction of a radiomics signature. Finally, the screened clinical risk factors and constructed radiomics signature were combined for the combined model and Radiomics+Clinics nomogram construction. The predictive performance of the Radiomics and Combined models were evaluated and compared using receiver operating characteristic curve (ROC) analysis, Hosmer-Lemeshow test and Delong test. RESULTS: Clinical characteristics analysis resulted in the screening of five clinical risk factors. The combination of ICC, mRMR, and LASSO methods resulted in the selection of ten radiomics features, which made up of the radiomics signature. The differences in the radiomics signature between the potential severe and non-severe groups in cross-validation set and test sets were both p < 0.001. All Radiomics and Combined models showed a very good predictive performance with the accuracy and AUC of nearly or above 0.9. Additionally, we found no significant difference in the predictive performance between these two models. CONCLUSIONS: A CT-based radiomics signature for the prediction of potential severe COVID-19 patients was constructed and evaluated. Constructed Radiomics and Combined model showed good feasibility and accuracy. The Radiomics+Clinical nomogram, acted as a useful tool, may assist clinicians to better identify potential severe cases to target their management in the COVID-19 pandemic prevention and control.

Radiomics analysis of chest CT to predict the overall survival for the severe patients of COVID-19 pneumonia.

Personalized assessment and treatment of severe patients with COVID-19 pneumonia have greatly affected the prognosis and survival of these patients. This study aimed to develop the radiomics models as the potential biomarkers to estimate the overall survival (OS) for the COVID-19 severe patients. A total of 74 COVID-19 severe patients were enrolled in this study, and 30 of them died during the follow-up period. First, the clinical risk factors of the patients were analyzed. Then, two radiomics signatures were constructed based on two segmented volumes of interest of whole lung area and lesion area. Two combination models were built depend on whether the clinic risk factors were used and/or whether two radiomics signatures were combined. Kaplan-Meier analysis were performed for validating two radiomics signatures and C-index was used to evaluated the predictive performance of all radiomics signatures and combination models. Finally, a radiomics nomogram combining radiomics signatures with clinical risk factors was developed for predicting personalized OS, and then assessed with respect to the calibration curve. Three clinical risk factors were found, included age, malignancy and highest temperature that influence OS. Both two radiomics signatures could effectively stratify the risk of OS in COVID-19 severe patients. The predictive performance of the combination model with two radiomics signatures was better than that only one radiomics signature was used, and became better when three clinical risk factors were interpolated. Calibration curves showed good agreement in both 15 d survival and 30 d survival between the estimation with the constructed nomogram and actual observation. Both two constructed radiomics signatures can act as the potential biomarkers for risk stratification of OS in COVID-19 severe patients. The radiomics+clinical nomogram generated might serve as a potential tool to guide personalized treatment and care for these patients.