Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2.
Eur Radiol
; 30(12): 6888-6901, 2020 Dec.
Article
in English
| MEDLINE | ID: covidwho-631855
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
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Tomography, X-Ray Computed
/
Coronavirus Infections
/
Nomograms
/
Betacoronavirus
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
/
Young adult
Country/Region as subject:
Asia
Language:
English
Journal:
Eur Radiol
Journal subject:
Radiology
Year:
2020
Document Type:
Article
Affiliation country:
S00330-020-07032-z
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