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Automated position and size selection of round applicators for AccuBoost breast brachytherapy

PURPOSE: AccuBoost is a complex non-invasive brachytherapy procedure for breast treatment. This technique requires a radiation oncologist to manually select applicator grid position and size by overlaying transparencies over a mammographic image to encompass surgical clips and resected tumor bed. An...

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Autores principales: West, Foster L., Munbodh, Reshma, Patrick, John C., Rivard, Mark J., Roles, Sean A., Saleh, Ziad H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Termedia Publishing House 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787202/
https://www.ncbi.nlm.nih.gov/pubmed/33437307
http://dx.doi.org/10.5114/jcb.2020.101692
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author West, Foster L.
Munbodh, Reshma
Patrick, John C.
Rivard, Mark J.
Roles, Sean A.
Saleh, Ziad H.
author_facet West, Foster L.
Munbodh, Reshma
Patrick, John C.
Rivard, Mark J.
Roles, Sean A.
Saleh, Ziad H.
author_sort West, Foster L.
collection PubMed
description PURPOSE: AccuBoost is a complex non-invasive brachytherapy procedure for breast treatment. This technique requires a radiation oncologist to manually select applicator grid position and size by overlaying transparencies over a mammographic image to encompass surgical clips and resected tumor bed. An algorithm was developed in MATLAB™ to automate the selection of round applicators based on surgical clip position. MATERIAL AND METHODS: A total of 42 mammograms belonging to 10 patients were retrospectively analyzed. Images were pre-processed by masking imprinted localization grid and regions around the grid. A threshold was applied to isolate high-intensity pixels and generate a binary image. A set of morphological operations including region dilation, filling, clearing border structures, and erosion were performed to segment the different regions. A support vector machine classification model was trained to categorize segmented regions as either surgical clips or miscellaneous objects based on different region properties (area, perimeter, eccentricity, circularity, minor axis length, and intensity-derived quantities). Applicator center position was determined by calculating the centroid of detected clips. Size of the applicator was determined with the smallest circle that encompassed all clips with an isotropic 1.0 cm margin. RESULTS: The clip identification model classified 946 regions, with a sensitivity of 96.6% and a specificity of 98.2%. Applicator position was correctly predicted for 20 of 42 fractions and was within 0.5 cm of physician-selected position for 33 of 42 fractions. Applicator size was correctly predicted for 25 out of 42 fractions. CONCLUSIONS: The proposed algorithm provided a method to quantitatively determine applicator position and size for AccuBoost treatments, and may serve as a tool for independent verifications. The discrepancy between physician-selected and algorithm-predicted determinations of applicator position and size suggests that the methodology may be further improved by considering radiomic features of breast tissue in addition to clip position.
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spelling pubmed-77872022021-01-11 Automated position and size selection of round applicators for AccuBoost breast brachytherapy West, Foster L. Munbodh, Reshma Patrick, John C. Rivard, Mark J. Roles, Sean A. Saleh, Ziad H. J Contemp Brachytherapy Original Paper PURPOSE: AccuBoost is a complex non-invasive brachytherapy procedure for breast treatment. This technique requires a radiation oncologist to manually select applicator grid position and size by overlaying transparencies over a mammographic image to encompass surgical clips and resected tumor bed. An algorithm was developed in MATLAB™ to automate the selection of round applicators based on surgical clip position. MATERIAL AND METHODS: A total of 42 mammograms belonging to 10 patients were retrospectively analyzed. Images were pre-processed by masking imprinted localization grid and regions around the grid. A threshold was applied to isolate high-intensity pixels and generate a binary image. A set of morphological operations including region dilation, filling, clearing border structures, and erosion were performed to segment the different regions. A support vector machine classification model was trained to categorize segmented regions as either surgical clips or miscellaneous objects based on different region properties (area, perimeter, eccentricity, circularity, minor axis length, and intensity-derived quantities). Applicator center position was determined by calculating the centroid of detected clips. Size of the applicator was determined with the smallest circle that encompassed all clips with an isotropic 1.0 cm margin. RESULTS: The clip identification model classified 946 regions, with a sensitivity of 96.6% and a specificity of 98.2%. Applicator position was correctly predicted for 20 of 42 fractions and was within 0.5 cm of physician-selected position for 33 of 42 fractions. Applicator size was correctly predicted for 25 out of 42 fractions. CONCLUSIONS: The proposed algorithm provided a method to quantitatively determine applicator position and size for AccuBoost treatments, and may serve as a tool for independent verifications. The discrepancy between physician-selected and algorithm-predicted determinations of applicator position and size suggests that the methodology may be further improved by considering radiomic features of breast tissue in addition to clip position. Termedia Publishing House 2020-12-16 2020-12 /pmc/articles/PMC7787202/ /pubmed/33437307 http://dx.doi.org/10.5114/jcb.2020.101692 Text en Copyright © 2020 Termedia http://creativecommons.org/licenses/by-nc-sa/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)
spellingShingle Original Paper
West, Foster L.
Munbodh, Reshma
Patrick, John C.
Rivard, Mark J.
Roles, Sean A.
Saleh, Ziad H.
Automated position and size selection of round applicators for AccuBoost breast brachytherapy
title Automated position and size selection of round applicators for AccuBoost breast brachytherapy
title_full Automated position and size selection of round applicators for AccuBoost breast brachytherapy
title_fullStr Automated position and size selection of round applicators for AccuBoost breast brachytherapy
title_full_unstemmed Automated position and size selection of round applicators for AccuBoost breast brachytherapy
title_short Automated position and size selection of round applicators for AccuBoost breast brachytherapy
title_sort automated position and size selection of round applicators for accuboost breast brachytherapy
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7787202/
https://www.ncbi.nlm.nih.gov/pubmed/33437307
http://dx.doi.org/10.5114/jcb.2020.101692
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