The value of CT signs combined with radiomics in the differentiation of COVID-19 from other viral pneumonias
10.3760/cma.j.cn112149-20201220-01318
- VernacularTitle:CT影像组学联合征象鉴别新型冠状病毒肺炎与其他病毒性肺炎的价值
- Author:
Yilong HUANG
1
;
Zhenguang ZHANG
;
Xiang LI
;
Yunhui YANG
;
Zhipeng LI
;
Jialong ZHOU
;
Yuanming JIANG
;
Jiyao MA
;
Siyun LIU
;
Bo HE
Author Information
1. 昆明医科大学第一附属医院医学影像科，昆明 650000
- Keywords:
Tomography, X-ray computed;
Diagnosis, differential;
COVID-19;
Pneumonia, viral;
Radiomics
- From:
Chinese Journal of Radiology
2022;56(1):36-42
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the classification performance of combined model constructed from CT signs combined with radiomics for discriminating COVID-19 pneumonia and other viral pneumonia.Methods:The clinical and CT imaging data of 181 patients with viral pneumonia confirmed by reverse transcription-polymerase chain reaction in 15 hospitals of Yunnan Province from March 2015 to March 2020 were analyzed retrospectively. The 181 patients were divided into COVID-19 group (89 cases) and non-COVID-19 group (92 cases), which were further divided into training cohort (126 cases) and test cohort (55 cases) at a ratio of 7∶3 using random stratified sampling. The CT signs of pneumonia were determined and the radiomics features were extracted from the initial unenhanced chest CT images to build independent and combined models for predicting COVID-19 pneumonia. The diagnostic performance of the models were evaluated using receiver operating characteristic (ROC) analysis, continuous net reclassification index (NRI) calibration curve and decision curve analysis.Results:The combined models consisted of 3 significant CT signs and 14 selected radiomics features. For the radiomics model alone, the area under the ROC curve (AUC) were 0.904 (sensitivity was 85.5%, specificity was 84.4%, accuracy was 84.9%) in the training cohort and 0.866 (sensitivity was 77.8%, specificity was 78.6%, accuracy 78.2%) in the test cohort. After combining CT signs and radiomics features, AUC of the combined model for the training cohort was 0.956 (sensitivity was 91.9%, specificity was 85.9%, accuracy was 88.9%), while that for the test cohort was 0.943 (sensitivity was 88.9%, specificity was 85.7%, accuracy was 87.3%). The AUC values of the combined model and the radiomics model in the differentiation of COVID-19 group and the non-COVID-19 group were significantly different in the training cohort ( Z=-2.43, P=0.015), but difference had no statistical significance in the test cohort ( Z=-1.73, P=0.083), and further analysis using the NRI showed that the combined model in both the training cohort and the test cohort had a positive improvement ability compared with radiomics model alone (training cohort: continuous NRI 1.077, 95 %CI 0.783-1.370; test cohort: continuous NRI 1.421, 95 %CI 1.051-1.790). The calibration curve showed that the prediction probability of COVID-19 predicted by the combined model was in good agreement with the observed value in the training and test cohorts; the decision curve showed that a net benefit greater than 0.6 could be obtained when the threshold probability of the combined model was 0-0.75. Conclusion:The combination of CT signs and radiomics might be a potential method for distinguishing COVID-19 and other viral pneumonia with good performance.
