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

ABSTRACT: Assessing pulmonary lesions using computed tomography (CT) images is of great significance to the severity diagnosis and treatment of coronavirus disease 2019 (COVID-19)-infected patients. Such assessment mainly depends on radiologists' subjective judgment, which is inefficient and presents difficulty for those with low levels of experience, especially in rural areas. This work focuses on developing a radiomics signature to quantitatively analyze whether COVID-19-infected pulmonary lesions are mild (Grade I) or moderate/severe (Grade II). We retrospectively analyzed 1160 COVID-19-infected pulmonary lesions from 16 hospitals. First, texture features were extracted from the pulmonary lesion regions of CT images. Then, feature preselection was performed and a radiomics signature was built usi

SUBMITTER: Qiu J 

PROVIDER: S-EPMC7788548 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature