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?ConclusionsThe 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.

SUBMITTER: Fang X 

PROVIDER: S-EPMC7332742 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature