CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS.

ABSTRACT:

Background

Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model.

Methods

This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneumonia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models

SUBMITTER: Liu H 

PROVIDER: S-EPMC7790050 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature