Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma

Abstract The purpose of this study was to examine differences in texture features between olfactory neuroblastoma (ONB) and sinonasal squamous cell carcinoma (SCC) on contrast-enhanced CT (CECT) images, and to evaluate the predictive accuracy of texture analysis compared to radiologists’ interpretat...

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Autores principales:	Masaki Ogawa, Satoshi Osaga, Norio Shiraki, Daisuke Kawakita, Nobuhiro Hanai, Tsuneo Tamaki, Satoshi Tsukahara, Takatsune Kawaguchi, Misugi Urano, Yuta Shibamoto
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d8bfbaac3a424551898b4f7cd0fbf3d9
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spelling	oai:doaj.org-article:d8bfbaac3a424551898b4f7cd0fbf3d92021-12-02T13:19:29ZUtility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma10.1038/s41598-021-84048-52045-2322https://doaj.org/article/d8bfbaac3a424551898b4f7cd0fbf3d92021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84048-5https://doaj.org/toc/2045-2322Abstract The purpose of this study was to examine differences in texture features between olfactory neuroblastoma (ONB) and sinonasal squamous cell carcinoma (SCC) on contrast-enhanced CT (CECT) images, and to evaluate the predictive accuracy of texture analysis compared to radiologists’ interpretations. Forty-three patients with pathologically-diagnosed primary nasal and paranasal tumor (17 ONB and 26 SCC) were included. We extracted 42 texture features from tumor regions on CECT images obtained before treatment. In univariate analysis, each texture features were compared, with adjustment for multiple comparisons. In multivariate analysis, the elastic net was used to select useful texture features and to construct a texture-based prediction model with leave-one-out cross-validation. The prediction accuracy was compared with two radiologists’ visual interpretations. In univariate analysis, significant differences were observed for 28 of 42 texture features between ONB and SCC, with areas under the receiver operating characteristic curve between 0.68 and 0.91 (median: 0.80). In multivariate analysis, the elastic net model selected 18 texture features that contributed to differentiation. It tended to show slightly higher predictive accuracy than radiologists’ interpretations (86% and 74%, respectively; P = 0.096). In conclusion, several texture features contributed to differentiation of ONB from SCC, and the texture-based prediction model was considered useful.Masaki OgawaSatoshi OsagaNorio ShirakiDaisuke KawakitaNobuhiro HanaiTsuneo TamakiSatoshi TsukaharaTakatsune KawaguchiMisugi UranoYuta ShibamotoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Masaki Ogawa Satoshi Osaga Norio Shiraki Daisuke Kawakita Nobuhiro Hanai Tsuneo Tamaki Satoshi Tsukahara Takatsune Kawaguchi Misugi Urano Yuta Shibamoto Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma
description	Abstract The purpose of this study was to examine differences in texture features between olfactory neuroblastoma (ONB) and sinonasal squamous cell carcinoma (SCC) on contrast-enhanced CT (CECT) images, and to evaluate the predictive accuracy of texture analysis compared to radiologists’ interpretations. Forty-three patients with pathologically-diagnosed primary nasal and paranasal tumor (17 ONB and 26 SCC) were included. We extracted 42 texture features from tumor regions on CECT images obtained before treatment. In univariate analysis, each texture features were compared, with adjustment for multiple comparisons. In multivariate analysis, the elastic net was used to select useful texture features and to construct a texture-based prediction model with leave-one-out cross-validation. The prediction accuracy was compared with two radiologists’ visual interpretations. In univariate analysis, significant differences were observed for 28 of 42 texture features between ONB and SCC, with areas under the receiver operating characteristic curve between 0.68 and 0.91 (median: 0.80). In multivariate analysis, the elastic net model selected 18 texture features that contributed to differentiation. It tended to show slightly higher predictive accuracy than radiologists’ interpretations (86% and 74%, respectively; P = 0.096). In conclusion, several texture features contributed to differentiation of ONB from SCC, and the texture-based prediction model was considered useful.
format	article
author	Masaki Ogawa Satoshi Osaga Norio Shiraki Daisuke Kawakita Nobuhiro Hanai Tsuneo Tamaki Satoshi Tsukahara Takatsune Kawaguchi Misugi Urano Yuta Shibamoto
author_facet	Masaki Ogawa Satoshi Osaga Norio Shiraki Daisuke Kawakita Nobuhiro Hanai Tsuneo Tamaki Satoshi Tsukahara Takatsune Kawaguchi Misugi Urano Yuta Shibamoto
author_sort	Masaki Ogawa
title	Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma
title_short	Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma
title_full	Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma
title_fullStr	Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma
title_full_unstemmed	Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma
title_sort	utility of ct texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d8bfbaac3a424551898b4f7cd0fbf3d9
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Utility of CT texture analysis to differentiate olfactory neuroblastoma from sinonasal squamous cell carcinoma

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