Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer.

ABSTRACT: Tumor phenotypes captured in computed tomography (CT) images can be described qualitatively and quantitatively using radiologist-defined "semantic" and computer-derived "radiomic" features, respectively. While both types of features have shown to be promising predictors of prognosis, the association between these groups of features remains unclear. We investigated the associations between semantic and radiomic features in CT images of 258 non-small cell lung adenocarcinomas. The tumor imaging phenotypes were described using 9 qualitative semantic features that were scored by radiologists, and 57 quantitative radiomic features that were automatically calculated using mathematical algorithms. Of the 9 semantic features, 3 were rated on a binary scale (cavitation, air bronchogram, and calcifi

SUBMITTER: Yip SSF 

PROVIDER: S-EPMC5471260 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature