Cargando…
Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer
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 pa...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8610973/ https://www.ncbi.nlm.nih.gov/pubmed/34815464 http://dx.doi.org/10.1038/s41598-021-02154-w |
_version_ | 1784603205797150720 |
---|---|
author | Schmidt, Ryder M. Delgadillo, Rodrigo Ford, John C. Padgett, Kyle R. Studenski, Matthew Abramowitz, Matthew C. Spieler, Benjamin Xu, Yihang Yang, Fei Dogan, Nesrin |
author_facet | Schmidt, Ryder M. Delgadillo, Rodrigo Ford, John C. Padgett, Kyle R. Studenski, Matthew Abramowitz, Matthew C. Spieler, Benjamin Xu, Yihang Yang, Fei Dogan, Nesrin |
author_sort | Schmidt, Ryder M. |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8610973 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-86109732021-11-24 Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer Schmidt, Ryder M. Delgadillo, Rodrigo Ford, John C. Padgett, Kyle R. Studenski, Matthew Abramowitz, Matthew C. Spieler, Benjamin Xu, Yihang Yang, Fei Dogan, Nesrin Sci Rep Article 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. Nature Publishing Group UK 2021-11-23 /pmc/articles/PMC8610973/ /pubmed/34815464 http://dx.doi.org/10.1038/s41598-021-02154-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Schmidt, Ryder M. Delgadillo, Rodrigo Ford, John C. Padgett, Kyle R. Studenski, Matthew Abramowitz, Matthew C. Spieler, Benjamin Xu, Yihang Yang, Fei Dogan, Nesrin Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8610973/ https://www.ncbi.nlm.nih.gov/pubmed/34815464 http://dx.doi.org/10.1038/s41598-021-02154-w |
work_keys_str_mv | AT schmidtryderm assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT delgadillorodrigo assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT fordjohnc assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT padgettkyler assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT studenskimatthew assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT abramowitzmatthewc assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT spielerbenjamin assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT xuyihang assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT yangfei assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer AT dogannesrin assessmentofcttocbctcontourmappingforradiomicfeatureanalysisinprostatecancer |