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

CONCLUSION: Non-invasive classification of NSCLC can be done accurately using random forest classification models based on well-known CT-derived descriptive features. However, radiomics-based classification models performed poorly in this scenario when tested on independent data and should be used with caution, due to their possible lack of generalizability to new data. ADVANCES IN KNOWLEDGE: 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. PMID: 31166787 [PubMed - as supplied by publisher]
Source: The British Journal of Radiology - Category: Radiology Authors: Tags: Br J Radiol Source Type: research