Machine learning-based CT radiomics model distinguishes COVID-19 from non-COVID-19 pneumonia.

ABSTRACT:

Background

To develop a machine learning-based CT radiomics model is critical for the accurate diagnosis of the rapid spreading coronavirus disease 2019 (COVID-19).

Methods

In this retrospective study, a total of 326 chest CT exams from 134 patients (63 confirmed COVID-19 patients and 71 non-COVID-19 patients) were collected from January 20 to February 8, 2020. A semi-automatic segmentation procedure was used to delineate the volume of interest (VOI), and radiomic features were extracted. The Support Vector Machine (SVM) model was built on the combination of 4 groups of features, including radiomic features, traditional radiological features, quantifying features, and clinical features. By repeating cross-validation procedure, the performance on the time-independent testing

SUBMITTER: Chen HJ 

PROVIDER: S-EPMC8424152 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature