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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Stephen S. F. Yip, Ying Liu, Chintan Parmar, Qian Li, Shichang Liu, Fangyuan Qu, Zhaoxiang Ye, Robert J. Gillies, Hugo J. W. L. Aerts

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 calcification) and 6 were rated on a categorical scale (texture, border definition, contour, lobulation, spiculation, and concavity). 32-41 radiomic features were associated with the binary semantic features (AUC = 0.56-0.76). The relationship between all radiomic features and the categorical semantic features ranged from weak to moderate (|Spearmen's correlation| = 0.002-0.65). There are associations between semantic and radiomic features, however the associations were not strong despite being significant. Our results indicate that radiomic features may capture distinct tumor phenotypes that fail to be perceived by naked eye that semantic features do not describe and vice versa. More... »

PAGES

3519

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-017-02425-5

DOI

http://dx.doi.org/10.1038/s41598-017-02425-5

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085943250

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28615677


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