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; Lingai Pan; Yang Guo; Bo Gong; Xiaobo Huang; Siyun Liu; Jianxin Huang; Hong Pu; Jie Zeng

doi:10.1371/journal.pone.0256194

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

PLoS One. 2022 Jan 13;17(1):e0256194. doi: 10.1371/journal.pone.0256194. eCollection 2022.

Authors

Shengkun Peng¹, Lingai Pan², Yang Guo², Bo Gong³, Xiaobo Huang², Siyun Liu⁴, Jianxin Huang⁵, Hong Pu¹, Jie Zeng⁶

Affiliations

¹ Department of Radiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
² Department of Critical Care Medicine, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
³ The Key Laboratory for Human Disease Gene Study of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
⁴ GE Healthcare (China), Beijing, China.
⁵ Department of Anesthesiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
⁶ Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Abstract

Objectives: 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.

Materials and methods: 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.

Results: 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.

Conclusions: The differentiation between COVID-19 and NC could be well improved by using radiomics features, compared with traditional CT quantitative values.

Publication types

Research Support, Non-U.S. Gov't
Comment

MeSH terms

COVID-19*
Humans
Pandemics
Retrospective Studies
SARS-CoV-2
Tomography, X-Ray Computed

Grants and funding

This research was supported by a grant from Sichuan Province Science and Technology Support Program to BG (Grant number: 2020YFS0558). GE Healthcare provided support in the form of salaries for author SL. The specific roles of the authors are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.