Non-invasive classification of non-small cell lung cancer: a comparison between random forest models utilising radiomic and semantic features

Abstract

Our study describes novel CT-derived random forest models based on radiologist-interpretation of CT scans (semantic features) that can assist NSCLC classification when histopathology is equivocal or when histopathological sampling is not possible. It also shows that random forest models based on semantic features may be more useful than those built from computational radiomic features.