Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer

Abstract This study provides a quantitative assessment of the accuracy of a commercially available deformable image registration (DIR) algorithm to automatically generate prostate contours and additionally investigates the robustness of radiomic features to differing contours. Twenty-eight prostate...

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Autores principales:	Ryder M. Schmidt, Rodrigo Delgadillo, John C. Ford, Kyle R. Padgett, Matthew Studenski, Matthew C. Abramowitz, Benjamin Spieler, Yihang Xu, Fei Yang, Nesrin Dogan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d1799194bbfe4db99c190917f5da1c0a
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spelling	oai:doaj.org-article:d1799194bbfe4db99c190917f5da1c0a2021-11-28T12:19:57ZAssessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer10.1038/s41598-021-02154-w2045-2322https://doaj.org/article/d1799194bbfe4db99c190917f5da1c0a2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02154-whttps://doaj.org/toc/2045-2322Abstract This study provides a quantitative assessment of the accuracy of a commercially available deformable image registration (DIR) algorithm to automatically generate prostate contours and additionally investigates the robustness of radiomic features to differing contours. Twenty-eight prostate cancer patients enrolled on an institutional review board (IRB) approved protocol were selected. Planning CTs (pCTs) were deformably registered to daily cone-beam CTs (CBCTs) to generate prostate contours (auto contours). The prostate contours were also manually drawn by a physician. Quantitative assessment of deformed versus manually drawn prostate contours on daily CBCT images was performed using Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), difference in center-of-mass position (ΔCM) and difference in volume (ΔVol). Radiomic features from 6 classes were extracted from each contour. Lin’s concordance correlation coefficient (CCC) and mean absolute percent difference in radiomic feature-derived data (mean \|%Δ\|RF) between auto and manual contours were calculated. The mean (± SD) DSC, MDA, ΔCM and ΔVol between the auto and manual prostate contours were 0.90 ± 0.04, 1.81 ± 0.47 mm, 2.17 ± 1.26 mm and 5.1 ± 4.1% respectively. Of the 1,010 fractions under consideration, 94.8% of DIRs were within TG-132 recommended tolerance. 30 radiomic features had a CCC > 0.90 and 21 had a mean \|%∆\|RF < 5%. Auto-propagation of prostate contours resulted in nearly 95% of DIRs within tolerance recommendations of TG-132, leading to the majority of features being regarded as acceptably robust. The use of auto contours for radiomic feature analysis is promising but must be done with caution.Ryder M. SchmidtRodrigo DelgadilloJohn C. FordKyle R. PadgettMatthew StudenskiMatthew C. AbramowitzBenjamin SpielerYihang XuFei YangNesrin DoganNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ryder M. Schmidt Rodrigo Delgadillo John C. Ford Kyle R. Padgett Matthew Studenski Matthew C. Abramowitz Benjamin Spieler Yihang Xu Fei Yang Nesrin Dogan Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer
description	Abstract This study provides a quantitative assessment of the accuracy of a commercially available deformable image registration (DIR) algorithm to automatically generate prostate contours and additionally investigates the robustness of radiomic features to differing contours. Twenty-eight prostate cancer patients enrolled on an institutional review board (IRB) approved protocol were selected. Planning CTs (pCTs) were deformably registered to daily cone-beam CTs (CBCTs) to generate prostate contours (auto contours). The prostate contours were also manually drawn by a physician. Quantitative assessment of deformed versus manually drawn prostate contours on daily CBCT images was performed using Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), difference in center-of-mass position (ΔCM) and difference in volume (ΔVol). Radiomic features from 6 classes were extracted from each contour. Lin’s concordance correlation coefficient (CCC) and mean absolute percent difference in radiomic feature-derived data (mean \|%Δ\|RF) between auto and manual contours were calculated. The mean (± SD) DSC, MDA, ΔCM and ΔVol between the auto and manual prostate contours were 0.90 ± 0.04, 1.81 ± 0.47 mm, 2.17 ± 1.26 mm and 5.1 ± 4.1% respectively. Of the 1,010 fractions under consideration, 94.8% of DIRs were within TG-132 recommended tolerance. 30 radiomic features had a CCC > 0.90 and 21 had a mean \|%∆\|RF < 5%. Auto-propagation of prostate contours resulted in nearly 95% of DIRs within tolerance recommendations of TG-132, leading to the majority of features being regarded as acceptably robust. The use of auto contours for radiomic feature analysis is promising but must be done with caution.
format	article
author	Ryder M. Schmidt Rodrigo Delgadillo John C. Ford Kyle R. Padgett Matthew Studenski Matthew C. Abramowitz Benjamin Spieler Yihang Xu Fei Yang Nesrin Dogan
author_facet	Ryder M. Schmidt Rodrigo Delgadillo John C. Ford Kyle R. Padgett Matthew Studenski Matthew C. Abramowitz Benjamin Spieler Yihang Xu Fei Yang Nesrin Dogan
author_sort	Ryder M. Schmidt
title	Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer
title_short	Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer
title_full	Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer
title_fullStr	Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer
title_full_unstemmed	Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer
title_sort	assessment of ct to cbct contour mapping for radiomic feature analysis in prostate cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d1799194bbfe4db99c190917f5da1c0a
work_keys_str_mv	AT rydermschmidt assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT rodrigodelgadillo assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT johncford assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT kylerpadgett assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT matthewstudenski assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT matthewcabramowitz assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT benjaminspieler assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT yihangxu assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT feiyang assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT nesrindogan assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer
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Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer

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