Radiomics analysis for distinctive identification of COVID-19 pulmonary nodules from other benign and malignant counterparts.

ABSTRACT: This observational study investigated the potential of radiomics as a non-invasive adjunct to CT in distinguishing COVID-19 lung nodules from other benign and malignant lung nodules. Lesion segmentation, feature extraction, and machine learning algorithms, including decision tree, support vector machine, random forest, feed-forward neural network, and discriminant analysis, were employed in the radiomics workflow. Key features such as Idmn, skewness, and long-run low grey level emphasis were identified as crucial in differentiation. The model demonstrated an accuracy of 83% in distinguishing COVID-19 from other benign nodules and 88% from malignant nodules. This study concludes that radiomics, through machine learning, serves as a valuable tool for non-invasive discrimination between COVID

SUBMITTER: Selvam M 

PROVIDER: S-EPMC10963772 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature