Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features.

ABSTRACT:

Purpose

To investigate the efficacy of radiomics in diagnosing patients with coronavirus disease (COVID-19) and other types of viral pneumonia with clinical symptoms and CT signs similar to those of COVID-19.

Methods

Between 18 January 2020 and 20 May 2020, 110 SARS-CoV-2 positive and 108 SARS-CoV-2 negative patients were retrospectively recruited from three hospitals based on the inclusion criteria. Manual segmentation of pneumonia lesions on CT scans was performed by four radiologists. The latest version of Pyradiomics was used for feature extraction. Four classifiers (linear classifier, k-nearest neighbour, least absolute shrinkage and selection operator [LASSO], and random forest) were used to differentiate SARS-CoV-2 positive and SARS-CoV-2 negative patients. Comparison

SUBMITTER: Wang L 

PROVIDER: S-EPMC7810032 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature