Machine Learning Radiomics Model for External and Internal Respiratory Motion Correlation Prediction in Lung Tumor.

ABSTRACT: Objectives: The complexity and specificity of lung tumor motion render it necessary to determine the external and internal correlation individually before applying indirect tumor tracking. However, the correlation cannot be determined from patient respiratory and tumor clinical characteristics before treatment. The purpose of this study is to present a machine learning model for an external/internal correlation prediction that is based on computed tomography (CT) radiomic features. Methods: 4-dimensional computed tomography (4DCT) images of 67 patients were collected retrospectively, and the external/internal correlation of lung tumor was calculated based on Spearman's rank correlation coefficient. Radiomic features were extracted from average intensity projection and the lig

SUBMITTER: Zhang X 

PROVIDER: S-EPMC9742719 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature