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 as...

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Autores principales:	Stephen S. F. Yip, Ying Liu, Chintan Parmar, Qian Li, Shichang Liu, Fangyuan Qu, Zhaoxiang Ye, Robert J. Gillies, Hugo J. W. L. Aerts
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d55a79bca4be44c7ac340a26b1dcd18b
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spelling	oai:doaj.org-article:d55a79bca4be44c7ac340a26b1dcd18b2021-12-02T15:05:38ZAssociations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer10.1038/s41598-017-02425-52045-2322https://doaj.org/article/d55a79bca4be44c7ac340a26b1dcd18b2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02425-5https://doaj.org/toc/2045-2322Abstract 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.Stephen S. F. YipYing LiuChintan ParmarQian LiShichang LiuFangyuan QuZhaoxiang YeRobert J. GilliesHugo J. W. L. AertsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Stephen S. F. Yip Ying Liu Chintan Parmar Qian Li Shichang Liu Fangyuan Qu Zhaoxiang Ye Robert J. Gillies Hugo J. W. L. Aerts Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer
description	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.
format	article
author	Stephen S. F. Yip Ying Liu Chintan Parmar Qian Li Shichang Liu Fangyuan Qu Zhaoxiang Ye Robert J. Gillies Hugo J. W. L. Aerts
author_facet	Stephen S. F. Yip Ying Liu Chintan Parmar Qian Li Shichang Liu Fangyuan Qu Zhaoxiang Ye Robert J. Gillies Hugo J. W. L. Aerts
author_sort	Stephen S. F. Yip
title	Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer
title_short	Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer
title_full	Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer
title_fullStr	Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer
title_full_unstemmed	Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer
title_sort	associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/d55a79bca4be44c7ac340a26b1dcd18b
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Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer

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