Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2

Fang, Xu; Li, Xiao; Bian, Yun; Ji, Xiang; Lu, Jianping

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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00330-020-07032-z) contains supplementary material, which is available to authorized users.