Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer

Abstract Radiomics is a method to mine large numbers of quantitative imaging features and develop predictive models. It has shown exciting promise for improved cancer decision support from early detection to personalized precision treatment, and therefore offers a desirable new direction for pancrea...

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Autores principales:	Jeffrey Wong, Michael Baine, Sarah Wisnoskie, Nathan Bennion, Dechun Zheng, Lei Yu, Vipin Dalal, Michael A. Hollingsworth, Chi Lin, Dandan Zheng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/bcdb7caa7f194e2184b552cfd4028e59
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spelling	oai:doaj.org-article:bcdb7caa7f194e2184b552cfd4028e592021-12-02T18:50:51ZEffects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer10.1038/s41598-021-95152-x2045-2322https://doaj.org/article/bcdb7caa7f194e2184b552cfd4028e592021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95152-xhttps://doaj.org/toc/2045-2322Abstract Radiomics is a method to mine large numbers of quantitative imaging features and develop predictive models. It has shown exciting promise for improved cancer decision support from early detection to personalized precision treatment, and therefore offers a desirable new direction for pancreatic cancer where the mortality remains high despite the current care and intense research. For radiomics, interobserver segmentation variability and its effect on radiomic feature stability is a crucial consideration. While investigations have been reported for high-contrast cancer sites such as lung cancer, no studies to date have investigated it on CT-based radiomics for pancreatic cancer. With three radiation oncology observers and three radiology observers independently contouring on the contrast CT of 21 pancreatic cancer patients, we conducted the first interobserver segmentation variability study on CT-based radiomics for pancreatic cancer. Moreover, our novel investigation assessed whether there exists an interdisciplinary difference between the two disciplines. For each patient, a consensus tumor volume was generated using the simultaneous truth and performance level expectation algorithm, using the dice similarity coefficient (DSC) to assess each observer’s delineation against the consensus volume. Radiation oncology observers showed a higher average DSC of 0.81 ± 0.06 than the radiology observers at 0.69 ± 0.16 (p = 0.002). On a panel of 1277 radiomic features, the intraclass correlation coefficients (ICC) was calculated for all observers and those of each discipline. Large variations of ICCs were observed for different radiomic features, but ICCs were generally higher for the radiation oncology group than for the radiology group. Applying a threshold of ICC > 0.75 for considering a feature as stable, 448 features (35%) were found stable for the radiation oncology group and 214 features (16%) were stable from the radiology group. Among them, 205 features were found stable for both groups. Our results provide information for interobserver segmentation variability and its effect on CT-based radiomics for pancreatic cancer. An interesting interdisciplinary variability found in this study also introduces new considerations for the deployment of radiomics models.Jeffrey WongMichael BaineSarah WisnoskieNathan BennionDechun ZhengLei YuVipin DalalMichael A. HollingsworthChi LinDandan ZhengNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jeffrey Wong Michael Baine Sarah Wisnoskie Nathan Bennion Dechun Zheng Lei Yu Vipin Dalal Michael A. Hollingsworth Chi Lin Dandan Zheng Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer
description	Abstract Radiomics is a method to mine large numbers of quantitative imaging features and develop predictive models. It has shown exciting promise for improved cancer decision support from early detection to personalized precision treatment, and therefore offers a desirable new direction for pancreatic cancer where the mortality remains high despite the current care and intense research. For radiomics, interobserver segmentation variability and its effect on radiomic feature stability is a crucial consideration. While investigations have been reported for high-contrast cancer sites such as lung cancer, no studies to date have investigated it on CT-based radiomics for pancreatic cancer. With three radiation oncology observers and three radiology observers independently contouring on the contrast CT of 21 pancreatic cancer patients, we conducted the first interobserver segmentation variability study on CT-based radiomics for pancreatic cancer. Moreover, our novel investigation assessed whether there exists an interdisciplinary difference between the two disciplines. For each patient, a consensus tumor volume was generated using the simultaneous truth and performance level expectation algorithm, using the dice similarity coefficient (DSC) to assess each observer’s delineation against the consensus volume. Radiation oncology observers showed a higher average DSC of 0.81 ± 0.06 than the radiology observers at 0.69 ± 0.16 (p = 0.002). On a panel of 1277 radiomic features, the intraclass correlation coefficients (ICC) was calculated for all observers and those of each discipline. Large variations of ICCs were observed for different radiomic features, but ICCs were generally higher for the radiation oncology group than for the radiology group. Applying a threshold of ICC > 0.75 for considering a feature as stable, 448 features (35%) were found stable for the radiation oncology group and 214 features (16%) were stable from the radiology group. Among them, 205 features were found stable for both groups. Our results provide information for interobserver segmentation variability and its effect on CT-based radiomics for pancreatic cancer. An interesting interdisciplinary variability found in this study also introduces new considerations for the deployment of radiomics models.
format	article
author	Jeffrey Wong Michael Baine Sarah Wisnoskie Nathan Bennion Dechun Zheng Lei Yu Vipin Dalal Michael A. Hollingsworth Chi Lin Dandan Zheng
author_facet	Jeffrey Wong Michael Baine Sarah Wisnoskie Nathan Bennion Dechun Zheng Lei Yu Vipin Dalal Michael A. Hollingsworth Chi Lin Dandan Zheng
author_sort	Jeffrey Wong
title	Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer
title_short	Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer
title_full	Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer
title_fullStr	Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer
title_full_unstemmed	Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer
title_sort	effects of interobserver and interdisciplinary segmentation variabilities on ct-based radiomics for pancreatic cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/bcdb7caa7f194e2184b552cfd4028e59
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_version_	1718377528794546176

Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer

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