Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2
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IMAGING INFORMATICS AND ARTIFICIAL INTELLIGENCE
Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2 Xu Fang 1 & Xiao Li 2,3 & Yun Bian 1
&
Xiang Ji 4 & Jianping Lu 1
Received: 24 March 2020 / Revised: 30 May 2020 / Accepted: 12 June 2020 # European Society of Radiology 2020
Abstract Objectives To develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia. Methods For this retrospective study, a radiomics model was developed on the basis of a training set consisting of 136 patients with COVID-19 pneumonia and 103 patients with other types of viral pneumonia. Radiomics features were extracted from the lung parenchyma window. A radiomics signature was built on the basis of reproducible features, using the least absolute shrinkage and selection operator method (LASSO). Multivariable logistic regression model was adopted to establish a radiomics nomogram. Nomogram performance was determined by its discrimination, calibration, and clinical usefulness. The model was validated in 90 consecutive patients, of which 56 patients had COVID-19 pneumonia and 34 patients had other types of viral pneumonia. Results The radiomics signature, consisting of 3 selected features, was significantly associated with COVID-19 pneumonia (p < 0.05) in both training and validation sets. The multivariable logistic regression model included the radiomics signature and distribution; maximum lesion, hilar, and mediastinal lymph node enlargement; and pleural effusion. The individualized prediction nomogram showed good discrimination in the training sample (area under the receiver operating characteristic curve [AUC], 0.959; 95% confidence interval [CI], 0.933–0.985) and in the validation sample (AUC, 0.955; 95% CI, 0.899–0.995) and good calibration. The mixed model achieved better predictive efficacy than the clinical model. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Conclusions The radiomics model derived has good performance for predicting COVID-19 pneumonia and may help in clinical decision-making. Key Points • A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range > 10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia. Keywords Coronavirus infections . Tomography, x-ray computed . Pneumonia, viral . Thorax . Radiomics, nomograms Xu Fang and Xiao Li contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07032-z) contains supplementary material, which is available to authorized users. * Yun Bian [email protected] 1
Department of Radi
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