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Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy

BACKGROUND AND PURPOSE: A wide range of quantitative measures are available to facilitate clinical implementation of auto-contouring software, on-going Quality Assurance (QA) and interobserver contouring variation studies. This study aimed to assess the variation in output when applying different im...

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Autores principales: Gooding, Mark J., Boukerroui, Djamal, Vasquez Osorio, Eliana, Monshouwer, René, Brunenberg, Ellen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679364/
https://www.ncbi.nlm.nih.gov/pubmed/36424980
http://dx.doi.org/10.1016/j.phro.2022.11.009
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author Gooding, Mark J.
Boukerroui, Djamal
Vasquez Osorio, Eliana
Monshouwer, René
Brunenberg, Ellen
author_facet Gooding, Mark J.
Boukerroui, Djamal
Vasquez Osorio, Eliana
Monshouwer, René
Brunenberg, Ellen
author_sort Gooding, Mark J.
collection PubMed
description BACKGROUND AND PURPOSE: A wide range of quantitative measures are available to facilitate clinical implementation of auto-contouring software, on-going Quality Assurance (QA) and interobserver contouring variation studies. This study aimed to assess the variation in output when applying different implementations of the measures to the same data in order to investigate how consistently such measures are defined and implemented in radiation oncology. MATERIALS AND METHODS: A survey was conducted to assess if there were any differences in definitions of contouring measures or their implementations that would lead to variation in reported results between institutions. This took two forms: a set of computed tomography (CT) image data with “Test” and “Reference” contours was distributed for participants to process using their preferred tools and report results, and a questionnaire regarding the definition of measures and their implementation was completed by the participants. RESULTS: Thirteen participants completed the survey and submitted results, with one commercial and twelve in-house solutions represented. Excluding outliers, variations of up to 50% in Dice Similarity Coefficient (DSC), 50% in 3D Hausdorff Distance (HD), and 200% in Average Distance (AD) were observed between the participant submitted results. Collaborative investigation with participants revealed a large number of bugs in implementation, confounding the understanding of intentional implementation choices. CONCLUSION: Care must be taken when comparing quantitative results between different studies. There is a need for a dataset with clearly defined measures and ground truth for validation of such tools prior to their use.
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spelling pubmed-96793642022-11-23 Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy Gooding, Mark J. Boukerroui, Djamal Vasquez Osorio, Eliana Monshouwer, René Brunenberg, Ellen Phys Imaging Radiat Oncol Original Research Article BACKGROUND AND PURPOSE: A wide range of quantitative measures are available to facilitate clinical implementation of auto-contouring software, on-going Quality Assurance (QA) and interobserver contouring variation studies. This study aimed to assess the variation in output when applying different implementations of the measures to the same data in order to investigate how consistently such measures are defined and implemented in radiation oncology. MATERIALS AND METHODS: A survey was conducted to assess if there were any differences in definitions of contouring measures or their implementations that would lead to variation in reported results between institutions. This took two forms: a set of computed tomography (CT) image data with “Test” and “Reference” contours was distributed for participants to process using their preferred tools and report results, and a questionnaire regarding the definition of measures and their implementation was completed by the participants. RESULTS: Thirteen participants completed the survey and submitted results, with one commercial and twelve in-house solutions represented. Excluding outliers, variations of up to 50% in Dice Similarity Coefficient (DSC), 50% in 3D Hausdorff Distance (HD), and 200% in Average Distance (AD) were observed between the participant submitted results. Collaborative investigation with participants revealed a large number of bugs in implementation, confounding the understanding of intentional implementation choices. CONCLUSION: Care must be taken when comparing quantitative results between different studies. There is a need for a dataset with clearly defined measures and ground truth for validation of such tools prior to their use. Elsevier 2022-11-15 /pmc/articles/PMC9679364/ /pubmed/36424980 http://dx.doi.org/10.1016/j.phro.2022.11.009 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Research Article
Gooding, Mark J.
Boukerroui, Djamal
Vasquez Osorio, Eliana
Monshouwer, René
Brunenberg, Ellen
Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy
title Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy
title_full Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy
title_fullStr Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy
title_full_unstemmed Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy
title_short Multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy
title_sort multicenter comparison of measures for quantitative evaluation of contouring in radiotherapy
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679364/
https://www.ncbi.nlm.nih.gov/pubmed/36424980
http://dx.doi.org/10.1016/j.phro.2022.11.009
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