Quantitative CT imaging features for COVID-19 evaluation: The ability to differentiate COVID-19 from non- COVID-19 (highly suspected) pneumonia patients during the epidemic period.

Shengkun Peng,Yang Guo,Xiaobo Huang,Siyun Liu,Jianxin Huang,Jie Zeng,Lingai Pan,Bo Gong,Hong Pu,Gayle E Woloschak

doi:10.1371/journal.pone.0256194

Abstract

COVID-19 and Non-Covid-19 (NC) Pneumonia encountered high CT imaging overlaps during pandemic. The study aims to evaluate the effectiveness of image-based quantitative CT features in discriminating COVID-19 from NC Pneumonia. 145 patients with highly suspected COVID-19 were retrospectively enrolled from four centers in Sichuan Province during January 23 to March 23, 2020. 88 cases were confirmed as COVID-19, and 57 patients were NC. The dataset was randomly divided by 3:2 into training and testing sets. The quantitative CT radiomics features were extracted and screened sequentially by correlation analysis, Mann-Whitney U test, the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and backward stepwise LR with minimum AIC methods. The selected features were used to construct the LR model for differentiating COVID-19 from NC. Meanwhile, the differentiation performance of traditional quantitative CT features such as lesion volume ratio, ground glass opacity (GGO) or consolidation volume ratio were also considered and compared with Radiomics-based method. The receiver operating characteristic curve (ROC) analysis were conducted to evaluate the predicting performance. Compared with traditional CT quantitative features, radiomics features performed best with the highest Area Under Curve (AUC), sensitivity, specificity and accuracy in the training (0.994, 0.942, 1.0 and 0.965) and testing sets (0.977, 0.944, 0.870, 0.915) (Delong test, P < 0.001). Among CT volume-ratio based models using lesion or GGO component ratio, the model combining CT lesion score and component ratio performed better than others, with the AUC, sensitivity, specificity and accuracy of 0.84, 0.692, 0.853, 0.756 in the training set and 0.779, 0.667, 0.826, 0.729 in the testing set. The significant difference of the most selected wavelet transformed radiomics features between COVID-19 and NC might well reflect the CT signs. The differentiation between COVID-19 and NC could be well improved by using radiomics features, compared with traditional CT quantitative values.

Highlights

Corona Virus Disease 2019 (COVID-19) has threaten the public health
The differentiation between COVID-19 and NC could be well improved by using radiomics features, compared with traditional Computed tomography (CT) quantitative values
Previous report have shown that the pathological features of COVID-19 greatly resemble those seen in SARS and Middle Eastern respiratory syndrome (MERS) coronavirus infection, which makes the identification of community-acquired pneumonia (CAP) and COVID-19 difficult [3]

Summary

Introduction

Corona Virus Disease 2019 (COVID-19) has threaten the public health. Computed tomography (CT) is one of the most intuitive assessment tools for lung disease. Most of Non-COVID-19 (NC) patients are diagnosed as community-acquired pneumonia (CAP) during pandemic [1]. The most common CT manifestations of COVID-19 pneumonia are: GGO, consolidation, mixed density and subpleural distribution. These signs often appear in patients with bacterial infections or other viral infections. RT-PCR testing is the current gold standard for the diagnosis of COVID-19. The low sensitivity of RT-PCR implies that many COVID-19 patients may not be identified and may not receive appropriate treatment in time. Previous research supports the use of chest CT for screening for COVD-19 for patients with clinical and epidemiologic features compatible with COVID-19 infection when RT-PCR testing is negative [4,5,6]

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