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

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.

Keywords: COVID-19; CT radiomics; potential severe patient; prediction.

FIGURE 1

Data flowchart of this study. (a) The clinical characteristics analysis process; (b) the image features analysis and modeling process

FIGURE 2

Dataset division scheme in this study

FIGURE 3

An examples of ground‐glass opacities (GGOs) and their segmentation results on CT images for one COVID‐19 severe patient. (a,b) The CT images before and after the semi‐automatical segmentation. The volumes of interest (VOIs) of lesions were represented red regions in (b)

FIGURE 4

Feature selection and modeling for the radiomics signature using the least absolute shrinkage and selection operator (LASSO) algorithm. (a) The optimal tuning parameter (Lambda) in the LASSO model was selected using 10‐fold cross‐validation and the 1 standard error rule. Two lambdas (the optimal value and the value that the simplest model obtained within one standard error of the optimal value) were obtained and drawn as two vertical dashed line. The optimal Lambda value of 0.023 with log (Lambda) = −3.781was selected for modeling and 10 nonzero coefficients were chosen; (b) LASSO coefficient profiles of the features compared to the lambda values. According to the 10‐fold cross‐validation in (a), the vertical line of the optimal lambda was drawn. The eight features with non‐zero coefficients were selected for radiomics signature construction

FIGURE 5

The results of radiomics signature construction and validation. (a) Histogram showing contribution of each feature to the constructed radiomics signature. (b,c) Radiomics signature distribution in training (p < 0.001) (b) and test set (p < 0.001) (c), respectively

FIGURE 6

The comparison of the receiver operating characteristic curves (ROCs) for the Radiomics and Combined models. (a) For the training set, while (b) for the test set

FIGURE 7

Radiomics+Clinics nomogram and its calibration curves

FIGURE 8

Boxplot of 100‐times repeated dataset randomly split evaluation results for the Combined model in independent test set. The details statistical results of receiver operating characteristic curve (ROC) metrics showed as follows: AUC = 0.9099±0.0856; ACC = 0.7974±0.1188; sensitivity = 0.7733±0.1910; specificity = 0.8213±0.1462; PPV = 0.8173±0.1362; NPV = 0.8106±0.1445. AUC, area under curve; ACC, accuracy; NPV, negative predicted value; PPV, positive predicted value