Reproducibility and Repeatability of CBCT-Derived Radiomics Features

Purpose: This study was conducted in order to determine the reproducibility and repeatability of cone-beam computed tomography (CBCT) radiomics features. Methods: The first-, second-, and fifth-day CBCT images from 10 head and neck (H&N) cancer patients and 10 pelvic cancer patients were retrosp...

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Veröffentlicht in:Frontiers in oncology 2021-11, Vol.11, p.773512, Article 773512
Hauptverfasser: Wang, Hao, Zhou, Yongkang, Wang, Xiao, Zhang, Yin, Ma, Chi, Liu, Bo, Kong, Qing, Yue, Ning, Xu, Zhiyong, Nie, Ke
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container_issue
container_start_page 773512
container_title Frontiers in oncology
container_volume 11
creator Wang, Hao
Zhou, Yongkang
Wang, Xiao
Zhang, Yin
Ma, Chi
Liu, Bo
Kong, Qing
Yue, Ning
Xu, Zhiyong
Nie, Ke
description Purpose: This study was conducted in order to determine the reproducibility and repeatability of cone-beam computed tomography (CBCT) radiomics features. Methods: The first-, second-, and fifth-day CBCT images from 10 head and neck (H&N) cancer patients and 10 pelvic cancer patients were retrospectively collected for this study. Eighteen common radiomics features were extracted from the longitudinal CBCT images using two radiomics packages. The reproducibility of CBCT-derived radiomics features was assessed using the first-day image as input and compared across the two software packages. The site-specific intraclass correlation coefficient (ICC) was used to quantitatively assess the agreement between packages. The repeatability of CBCT-based radiomics features was evaluated by comparing the following days of CBCT to the first-day image and quantified using site-specific concordance correlation coefficient (CCC). Furthermore, the correlation with volume for all the features was assessed with linear regression and R-2 as correlation parameters. Results: The first-order histogram-based features such as skewness and entropy showed good agreement computed in either software package (ICCs >= 0.80), while the kurtosis measurements were consistent in H&N patients between the two software tools but not in pelvic cases. The ICCs for GLCM-based features showed good agreement (ICCs >= 0.80) between packages in both H&N and pelvic groups except for the GLCM-correction. The GLRLM-based texture features were overall less consistent as calculated by the two different software packages compared with the GLCM-based features. The CCC values of all first-order and second-order GLCM features (except GLCM-energy) were all above 0.80 from the 2-day part test-retest set, while the CCC values all dropped below the cutoff after 5-day treatment scans. All first-order histogram-based and GLCM-texture-based features were not highly correlated with volume, while two GLRLM features, in both H&N and pelvic cohorts, showed R-2 >= 0.8, meaning a high correlation with volume. Conclusion: The reproducibility and repeatability of CBCT-based radiomics features were assessed and compared for the first time on both H&N and pelvic sites. There were overlaps of stable features in both disease sites, yet the overall stability of radiomics features may be disease-/protocol-specific and a function of time between scans.
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Methods: The first-, second-, and fifth-day CBCT images from 10 head and neck (H&amp;N) cancer patients and 10 pelvic cancer patients were retrospectively collected for this study. Eighteen common radiomics features were extracted from the longitudinal CBCT images using two radiomics packages. The reproducibility of CBCT-derived radiomics features was assessed using the first-day image as input and compared across the two software packages. The site-specific intraclass correlation coefficient (ICC) was used to quantitatively assess the agreement between packages. The repeatability of CBCT-based radiomics features was evaluated by comparing the following days of CBCT to the first-day image and quantified using site-specific concordance correlation coefficient (CCC). Furthermore, the correlation with volume for all the features was assessed with linear regression and R-2 as correlation parameters. Results: The first-order histogram-based features such as skewness and entropy showed good agreement computed in either software package (ICCs &gt;= 0.80), while the kurtosis measurements were consistent in H&amp;N patients between the two software tools but not in pelvic cases. The ICCs for GLCM-based features showed good agreement (ICCs &gt;= 0.80) between packages in both H&amp;N and pelvic groups except for the GLCM-correction. The GLRLM-based texture features were overall less consistent as calculated by the two different software packages compared with the GLCM-based features. The CCC values of all first-order and second-order GLCM features (except GLCM-energy) were all above 0.80 from the 2-day part test-retest set, while the CCC values all dropped below the cutoff after 5-day treatment scans. All first-order histogram-based and GLCM-texture-based features were not highly correlated with volume, while two GLRLM features, in both H&amp;N and pelvic cohorts, showed R-2 &gt;= 0.8, meaning a high correlation with volume. Conclusion: The reproducibility and repeatability of CBCT-based radiomics features were assessed and compared for the first time on both H&amp;N and pelvic sites. There were overlaps of stable features in both disease sites, yet the overall stability of radiomics features may be disease-/protocol-specific and a function of time between scans.</description><identifier>ISSN: 2234-943X</identifier><identifier>EISSN: 2234-943X</identifier><identifier>DOI: 10.3389/fonc.2021.773512</identifier><identifier>PMID: 34869015</identifier><language>eng</language><publisher>LAUSANNE: Frontiers Media Sa</publisher><subject>imaging protocol ; in-treatment image ; Life Sciences &amp; Biomedicine ; longitudinal CBCT radiomics ; Oncology ; repeatability ; reproducibility ; Science &amp; Technology</subject><ispartof>Frontiers in oncology, 2021-11, Vol.11, p.773512, Article 773512</ispartof><rights>Copyright © 2021 Wang, Zhou, Wang, Zhang, Ma, Liu, Kong, Yue, Xu and Nie.</rights><rights>Copyright © 2021 Wang, Zhou, Wang, Zhang, Ma, Liu, Kong, Yue, Xu and Nie 2021 Wang, Zhou, Wang, Zhang, Ma, Liu, Kong, Yue, Xu and Nie</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>10</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000885946300001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c462t-9226f63a2d10dafb9a0d2b6b504b58bde3bc4faf79d1f19814923a2f9a4539be3</citedby><cites>FETCH-LOGICAL-c462t-9226f63a2d10dafb9a0d2b6b504b58bde3bc4faf79d1f19814923a2f9a4539be3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637922/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637922/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,866,887,2104,2116,27931,27932,39265,53798,53800</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34869015$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Hao</creatorcontrib><creatorcontrib>Zhou, Yongkang</creatorcontrib><creatorcontrib>Wang, Xiao</creatorcontrib><creatorcontrib>Zhang, Yin</creatorcontrib><creatorcontrib>Ma, Chi</creatorcontrib><creatorcontrib>Liu, Bo</creatorcontrib><creatorcontrib>Kong, Qing</creatorcontrib><creatorcontrib>Yue, Ning</creatorcontrib><creatorcontrib>Xu, Zhiyong</creatorcontrib><creatorcontrib>Nie, Ke</creatorcontrib><title>Reproducibility and Repeatability of CBCT-Derived Radiomics Features</title><title>Frontiers in oncology</title><addtitle>FRONT ONCOL</addtitle><addtitle>Front Oncol</addtitle><description>Purpose: This study was conducted in order to determine the reproducibility and repeatability of cone-beam computed tomography (CBCT) radiomics features. Methods: The first-, second-, and fifth-day CBCT images from 10 head and neck (H&amp;N) cancer patients and 10 pelvic cancer patients were retrospectively collected for this study. Eighteen common radiomics features were extracted from the longitudinal CBCT images using two radiomics packages. The reproducibility of CBCT-derived radiomics features was assessed using the first-day image as input and compared across the two software packages. The site-specific intraclass correlation coefficient (ICC) was used to quantitatively assess the agreement between packages. The repeatability of CBCT-based radiomics features was evaluated by comparing the following days of CBCT to the first-day image and quantified using site-specific concordance correlation coefficient (CCC). Furthermore, the correlation with volume for all the features was assessed with linear regression and R-2 as correlation parameters. Results: The first-order histogram-based features such as skewness and entropy showed good agreement computed in either software package (ICCs &gt;= 0.80), while the kurtosis measurements were consistent in H&amp;N patients between the two software tools but not in pelvic cases. The ICCs for GLCM-based features showed good agreement (ICCs &gt;= 0.80) between packages in both H&amp;N and pelvic groups except for the GLCM-correction. The GLRLM-based texture features were overall less consistent as calculated by the two different software packages compared with the GLCM-based features. The CCC values of all first-order and second-order GLCM features (except GLCM-energy) were all above 0.80 from the 2-day part test-retest set, while the CCC values all dropped below the cutoff after 5-day treatment scans. All first-order histogram-based and GLCM-texture-based features were not highly correlated with volume, while two GLRLM features, in both H&amp;N and pelvic cohorts, showed R-2 &gt;= 0.8, meaning a high correlation with volume. Conclusion: The reproducibility and repeatability of CBCT-based radiomics features were assessed and compared for the first time on both H&amp;N and pelvic sites. There were overlaps of stable features in both disease sites, yet the overall stability of radiomics features may be disease-/protocol-specific and a function of time between scans.</description><subject>imaging protocol</subject><subject>in-treatment image</subject><subject>Life Sciences &amp; Biomedicine</subject><subject>longitudinal CBCT radiomics</subject><subject>Oncology</subject><subject>repeatability</subject><subject>reproducibility</subject><subject>Science &amp; Technology</subject><issn>2234-943X</issn><issn>2234-943X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>DOA</sourceid><recordid>eNqNkctrFTEUxgdRbKndu5K7FGSueU-yEXRqH1AQpIK7kMdJTZk7uSYzLf3vzXWul3ZnNgnn_L7v5PA1zVuM1pRK9TGk0a0JInjddZRj8qI5JoSyVjH68-WT91FzWsodqkdwhBF93RxRJoVCmB83Z99hm5OfXbRxiNPjyox-VWtgJrOvpLDqv_Q37RnkeA-1a3xMm-jK6rxSc4bypnkVzFDgdH-fND_Ov970l-31t4ur_vN165ggU6sIEUFQQzxG3gSrDPLECssRs1xaD9Q6FkzolMcBK4mZIpUOyjBOlQV60lwtvj6ZO73NcWPyo04m6r-FlG-1yVN0A2hmQWLJqScGmKFeBuK44NiAq6tLWr0-LV7b2W7AOxinbIZnps87Y_ylb9O9loJ2dZNq8H5vkNPvGcqkN7E4GAYzQpqLJgJ1FAmieEXRgrqcSskQDmMw0rss9S5LvctSL1lWybun3zsI_iVXAbkAD2BTKC7C6OCA1bCl5IoJussd93EyU0xjn-ZxqtIP_y-lfwA747xD</recordid><startdate>20211117</startdate><enddate>20211117</enddate><creator>Wang, Hao</creator><creator>Zhou, Yongkang</creator><creator>Wang, Xiao</creator><creator>Zhang, Yin</creator><creator>Ma, Chi</creator><creator>Liu, Bo</creator><creator>Kong, Qing</creator><creator>Yue, Ning</creator><creator>Xu, Zhiyong</creator><creator>Nie, Ke</creator><general>Frontiers Media Sa</general><general>Frontiers Media S.A</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20211117</creationdate><title>Reproducibility and Repeatability of CBCT-Derived Radiomics Features</title><author>Wang, Hao ; Zhou, Yongkang ; Wang, Xiao ; Zhang, Yin ; Ma, Chi ; Liu, Bo ; Kong, Qing ; Yue, Ning ; Xu, Zhiyong ; Nie, Ke</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c462t-9226f63a2d10dafb9a0d2b6b504b58bde3bc4faf79d1f19814923a2f9a4539be3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>imaging protocol</topic><topic>in-treatment image</topic><topic>Life Sciences &amp; Biomedicine</topic><topic>longitudinal CBCT radiomics</topic><topic>Oncology</topic><topic>repeatability</topic><topic>reproducibility</topic><topic>Science &amp; Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Hao</creatorcontrib><creatorcontrib>Zhou, Yongkang</creatorcontrib><creatorcontrib>Wang, Xiao</creatorcontrib><creatorcontrib>Zhang, Yin</creatorcontrib><creatorcontrib>Ma, Chi</creatorcontrib><creatorcontrib>Liu, Bo</creatorcontrib><creatorcontrib>Kong, Qing</creatorcontrib><creatorcontrib>Yue, Ning</creatorcontrib><creatorcontrib>Xu, Zhiyong</creatorcontrib><creatorcontrib>Nie, Ke</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Frontiers in oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Hao</au><au>Zhou, Yongkang</au><au>Wang, Xiao</au><au>Zhang, Yin</au><au>Ma, Chi</au><au>Liu, Bo</au><au>Kong, Qing</au><au>Yue, Ning</au><au>Xu, Zhiyong</au><au>Nie, Ke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reproducibility and Repeatability of CBCT-Derived Radiomics Features</atitle><jtitle>Frontiers in oncology</jtitle><stitle>FRONT ONCOL</stitle><addtitle>Front Oncol</addtitle><date>2021-11-17</date><risdate>2021</risdate><volume>11</volume><spage>773512</spage><pages>773512-</pages><artnum>773512</artnum><issn>2234-943X</issn><eissn>2234-943X</eissn><abstract>Purpose: This study was conducted in order to determine the reproducibility and repeatability of cone-beam computed tomography (CBCT) radiomics features. Methods: The first-, second-, and fifth-day CBCT images from 10 head and neck (H&amp;N) cancer patients and 10 pelvic cancer patients were retrospectively collected for this study. Eighteen common radiomics features were extracted from the longitudinal CBCT images using two radiomics packages. The reproducibility of CBCT-derived radiomics features was assessed using the first-day image as input and compared across the two software packages. The site-specific intraclass correlation coefficient (ICC) was used to quantitatively assess the agreement between packages. The repeatability of CBCT-based radiomics features was evaluated by comparing the following days of CBCT to the first-day image and quantified using site-specific concordance correlation coefficient (CCC). Furthermore, the correlation with volume for all the features was assessed with linear regression and R-2 as correlation parameters. Results: The first-order histogram-based features such as skewness and entropy showed good agreement computed in either software package (ICCs &gt;= 0.80), while the kurtosis measurements were consistent in H&amp;N patients between the two software tools but not in pelvic cases. The ICCs for GLCM-based features showed good agreement (ICCs &gt;= 0.80) between packages in both H&amp;N and pelvic groups except for the GLCM-correction. The GLRLM-based texture features were overall less consistent as calculated by the two different software packages compared with the GLCM-based features. The CCC values of all first-order and second-order GLCM features (except GLCM-energy) were all above 0.80 from the 2-day part test-retest set, while the CCC values all dropped below the cutoff after 5-day treatment scans. All first-order histogram-based and GLCM-texture-based features were not highly correlated with volume, while two GLRLM features, in both H&amp;N and pelvic cohorts, showed R-2 &gt;= 0.8, meaning a high correlation with volume. Conclusion: The reproducibility and repeatability of CBCT-based radiomics features were assessed and compared for the first time on both H&amp;N and pelvic sites. There were overlaps of stable features in both disease sites, yet the overall stability of radiomics features may be disease-/protocol-specific and a function of time between scans.</abstract><cop>LAUSANNE</cop><pub>Frontiers Media Sa</pub><pmid>34869015</pmid><doi>10.3389/fonc.2021.773512</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record>
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subjects imaging protocol
in-treatment image
Life Sciences & Biomedicine
longitudinal CBCT radiomics
Oncology
repeatability
reproducibility
Science & Technology
title Reproducibility and Repeatability of CBCT-Derived Radiomics Features
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