Click here to


Are you sure ?

Yes, do it No, cancel

A Random Forest Machine-Enabled Diagnostic Algorithm Combing Quantitative CT Radiomics and Clinical Factors for the Identification of Patients with Corona Virus Disease-19 (COVID-19): A Discovery and Validation Study

X Li1,2*, J Li3, X Zhao4, Z Ding1, B Yang1, Q Deng1, S Ma2, Y Kuang5, (1) Hangzhou Cancer Hospital, Hangzhou First People's Hospital Group, Hangzhou, China, (2) Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China, (3) Zhejiang University of Traditional Chinese Medicine, Hangzhou, China, (4) Hangzhou Municipal Health Commission, Hangzhou, China, (5) University of Nevada, Las Vegas, NV


(Sunday, 7/12/2020) 1:00 PM - 3:00 PM [Eastern Time (GMT-4)]

Room: Track 2

Purpose: Chest CT imaging has been added as an auxiliary method to detect the novel coronavirus disease 2019 (COVID-19) with high sensitivity to identify suspected patients prior to the occurrence of positive nuclear acid detection results. However, because of the significant overlap in CT manifestations between COVID-19 and other coronavirus and influenza viral pneumonias, the specificity of using CT to detect the COVID-19 is extremely low. In this study, we developed a machine learning-enabled algorithm combining lung CT radiomics and clinical factors to significantly improve the early detection of patients with COVID-19.

Methods: A total of 109 patients with COVID-19 were included in this study. The patients with influenza viral pneumonia were used as control. The 1766 radiomics features were extracted from disease lesion regions on CT images. The Mann-Whitney U test was applied to assess the differences of CT radiomics features between COVID-19 and non-COVID-19 groups. The LASSO regression were employed to select optimal features. A predictive model was constructed to combine clinical factors and optimal radiomics features using a 5-fold cross validation method and the random forest machine. The utility of the model constructed was evaluated by the ROC analysis in the validation cohort.

Results: A total of 17 radiomics features show significant differences between COVID-19 and non-COVID-19 groups. The predictive model using the optimal radiomics features alone shows an excellent performances in early identification of COVID-19 with an AUC value of 0.867. Furthermore, the model combining radiomics and clinical factors showed an improved detection rate with an AUC value of 0.937 in the validation cohort.

Conclusion: The random forest machine-enabled diagnostic algorithm depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have important diagnostic and therapeutic implications from a precision medicine perspective.


Quantitative Imaging, Computed Radiography, Helical CT


IM- CT: Radiomics

Contact Email