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A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery

BACKGROUND: Breast-conservation surgery with radiotherapy is a treatment highly recommended by the guidelines from the National Comprehensive Cancer Network. However, several variables influence the final receipt of radiotherapy and it might not be administered to breast cancer patients. Our objecti...

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Autores principales: Soto-Ferrari, Milton, Prieto, Diana, Munene, Gitonga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5490206/
https://www.ncbi.nlm.nih.gov/pubmed/28659177
http://dx.doi.org/10.1186/s12911-017-0479-4
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author Soto-Ferrari, Milton
Prieto, Diana
Munene, Gitonga
author_facet Soto-Ferrari, Milton
Prieto, Diana
Munene, Gitonga
author_sort Soto-Ferrari, Milton
collection PubMed
description BACKGROUND: Breast-conservation surgery with radiotherapy is a treatment highly recommended by the guidelines from the National Comprehensive Cancer Network. However, several variables influence the final receipt of radiotherapy and it might not be administered to breast cancer patients. Our objective is to propose a systematic framework to identify the clinical and non-clinical variables that influence the receipt of unexpected radiotherapy treatment by means of Bayesian networks and a proposed heuristic approach. METHODS: We used cancer registry data of Detroit, San Francisco-Oakland, and Atlanta from years 2007–2012 downloaded from the Surveillance, Epidemiology, and End Results Program. The samples had patients diagnosed with in situ and early invasive cancer with 14 clinical and non-clinical variables. Bayesian networks were fitted to the data of each region and systematically analyzed through the proposed Zoom-in heuristic. A comparative analysis with logistic regressions is also presented. RESULTS: For Detroit, patients under stage 0, grade undetermined, histology lobular carcinoma in situ, and age between 26−50 were found more likely to receive breast-conservation surgery without radiotherapy. For stages I, IIA, and IIB patients with age between 51−75, and grade II were found to be more likely to receive breast-conservation surgery with radiotherapy. For San Francisco-Oakland, patients under stage 0, grade undetermined, and age >75 are more likely to receive BCS. For stages I, IIA, and IIB patients with age >75 are more likely to receive breast-conservation surgery without radiotherapy. For Atlanta, patients under stage 0, grade undetermined, year 2011, and primary site C509 are more likely to receive breast-conservation surgery without radiotherapy. For stages I, IIA, and IIB patients in year 2011, and grade III are more likely to receive breast-conservation surgery without radiotherapy. CONCLUSION: For in situ breast cancer and early invasive breast cancer, the results are in accordance with the guidelines and very well demonstrates the usefulness of the Zoom-in heuristic in systematically characterizing a group receiving a treatment. We found a subset of the population from Detroit with ductal carcinoma in situ for which breast-conservation surgery without radiotherapy was received, but potential reasons for this treatment are still unknown. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-017-0479-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-54902062017-06-30 A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery Soto-Ferrari, Milton Prieto, Diana Munene, Gitonga BMC Med Inform Decis Mak Research Article BACKGROUND: Breast-conservation surgery with radiotherapy is a treatment highly recommended by the guidelines from the National Comprehensive Cancer Network. However, several variables influence the final receipt of radiotherapy and it might not be administered to breast cancer patients. Our objective is to propose a systematic framework to identify the clinical and non-clinical variables that influence the receipt of unexpected radiotherapy treatment by means of Bayesian networks and a proposed heuristic approach. METHODS: We used cancer registry data of Detroit, San Francisco-Oakland, and Atlanta from years 2007–2012 downloaded from the Surveillance, Epidemiology, and End Results Program. The samples had patients diagnosed with in situ and early invasive cancer with 14 clinical and non-clinical variables. Bayesian networks were fitted to the data of each region and systematically analyzed through the proposed Zoom-in heuristic. A comparative analysis with logistic regressions is also presented. RESULTS: For Detroit, patients under stage 0, grade undetermined, histology lobular carcinoma in situ, and age between 26−50 were found more likely to receive breast-conservation surgery without radiotherapy. For stages I, IIA, and IIB patients with age between 51−75, and grade II were found to be more likely to receive breast-conservation surgery with radiotherapy. For San Francisco-Oakland, patients under stage 0, grade undetermined, and age >75 are more likely to receive BCS. For stages I, IIA, and IIB patients with age >75 are more likely to receive breast-conservation surgery without radiotherapy. For Atlanta, patients under stage 0, grade undetermined, year 2011, and primary site C509 are more likely to receive breast-conservation surgery without radiotherapy. For stages I, IIA, and IIB patients in year 2011, and grade III are more likely to receive breast-conservation surgery without radiotherapy. CONCLUSION: For in situ breast cancer and early invasive breast cancer, the results are in accordance with the guidelines and very well demonstrates the usefulness of the Zoom-in heuristic in systematically characterizing a group receiving a treatment. We found a subset of the population from Detroit with ductal carcinoma in situ for which breast-conservation surgery without radiotherapy was received, but potential reasons for this treatment are still unknown. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12911-017-0479-4) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-28 /pmc/articles/PMC5490206/ /pubmed/28659177 http://dx.doi.org/10.1186/s12911-017-0479-4 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Soto-Ferrari, Milton
Prieto, Diana
Munene, Gitonga
A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery
title A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery
title_full A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery
title_fullStr A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery
title_full_unstemmed A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery
title_short A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery
title_sort bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5490206/
https://www.ncbi.nlm.nih.gov/pubmed/28659177
http://dx.doi.org/10.1186/s12911-017-0479-4
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