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

摘要

Abstract 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.