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

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.

A Radiomics Signature to Quantitatively Analyze COVID-19-Infected Pulmonary Lesions

“…Wu et al [30] developed a CT-based signature to perform prognostic analysis in patients with COVID-19. Fang et al [31] developed a radiomics model to predict COVID-19 pneumonia. Fu et al [32] used a machine learning-based tool to develop radiomics signatures and perform prognosis analysis of COVID-19 patients.…”

Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features

“…Our results support the application of radiomics to assist with diagnosis of COVID-19 pneumonia. The recent development of radiomics has provided a new research paradigm in clinical studies [ 35 , 36 ], and there have already been radiomics studies published for COVID-19 survival prognosis and illness severity identification [ [12] , [13] , [14] , [15] ]. However, current radiomics studies on the differentiation of COVID-19 from other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19, and the evaluation of radiomic feature among different classifiers on COVID-19, are scarce.…”

“…Based on the region of interest (ROI) for pneumonia lesions delineated by radiologists, radiomics may provide additional knowledge for survival prognosis and classification of illness severity for COVID-19 pneumonia [ [12] , [13] , [14] , [15] ]. The latest analytical tool of radiomics, Pyradiomics, has paved the way for standardised radiomics analysis [ 16 ].…”

A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

“…Over the past few years, different deep learning (DL)-based artificial intelligence (AI) diagnostic systems were developed and deployed in clinical practice to assist radiologists, such as the DL-based pulmonary nodules diagnostic system 5 . Since the outbreak of COVID-19, multiple machine learning (ML) and DL models for detecting lesions, assessing disease severity, and predicting disease prognosis of COVID-19 have been developed 6 – 13 . Wang et al developed a DL model to provide clinical diagnosis before the pathogenic examinations by extracting radiographical features of COVID-19 8 .…”