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Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study

Guidelines for the management of elderly patients with early breast cancer are scarce. Additional adjuvant systemic treatment to surgery for early breast cancer in elderly populations is challenged by increasing comorbidities with age. In non-metastatic settings, treatment decisions are often made u...

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Autores principales: Fleck, Julia L., Hooijenga, Daniëlle, Phan, Raksmey, Xie, Xiaolan, Augusto, Vincent, Heudel, Pierre-Etienne
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449163/
https://www.ncbi.nlm.nih.gov/pubmed/37616325
http://dx.doi.org/10.1371/journal.pone.0290566
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author Fleck, Julia L.
Hooijenga, Daniëlle
Phan, Raksmey
Xie, Xiaolan
Augusto, Vincent
Heudel, Pierre-Etienne
author_facet Fleck, Julia L.
Hooijenga, Daniëlle
Phan, Raksmey
Xie, Xiaolan
Augusto, Vincent
Heudel, Pierre-Etienne
author_sort Fleck, Julia L.
collection PubMed
description Guidelines for the management of elderly patients with early breast cancer are scarce. Additional adjuvant systemic treatment to surgery for early breast cancer in elderly populations is challenged by increasing comorbidities with age. In non-metastatic settings, treatment decisions are often made under considerable uncertainty; this commonly leads to undertreatment and, consequently, poorer outcomes. This study aimed to develop a decision support tool that can help to identify candidate adjuvant post-surgery treatment schemes for elderly breast cancer patients based on tumor and patient characteristics. Our approach was to generate predictions of patient outcomes for different courses of action; these predictions can, in turn, be used to inform clinical decisions for new patients. We used a cohort of elderly patients (≥ 70 years) who underwent surgery with curative intent for early breast cancer to train the models. We tested seven classification algorithms using 5-fold cross-validation, with 80% of the data being randomly selected for training and the remaining 20% for testing. We assessed model performance using accuracy, precision, recall, F1-score, and AUC score. We used an autoencoder to perform dimensionality reduction prior to classification. We observed consistently better performance using logistic regression and linear discriminant analysis models when compared to the other models we tested. Classification performance generally improved when an autoencoder was used, except for when we predicted the need for adjuvant treatment. We obtained overall best results using a logistic regression model without autoencoding to predict the need for adjuvant treatment (F1-score = 0.869).
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spelling pubmed-104491632023-08-25 Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study Fleck, Julia L. Hooijenga, Daniëlle Phan, Raksmey Xie, Xiaolan Augusto, Vincent Heudel, Pierre-Etienne PLoS One Research Article Guidelines for the management of elderly patients with early breast cancer are scarce. Additional adjuvant systemic treatment to surgery for early breast cancer in elderly populations is challenged by increasing comorbidities with age. In non-metastatic settings, treatment decisions are often made under considerable uncertainty; this commonly leads to undertreatment and, consequently, poorer outcomes. This study aimed to develop a decision support tool that can help to identify candidate adjuvant post-surgery treatment schemes for elderly breast cancer patients based on tumor and patient characteristics. Our approach was to generate predictions of patient outcomes for different courses of action; these predictions can, in turn, be used to inform clinical decisions for new patients. We used a cohort of elderly patients (≥ 70 years) who underwent surgery with curative intent for early breast cancer to train the models. We tested seven classification algorithms using 5-fold cross-validation, with 80% of the data being randomly selected for training and the remaining 20% for testing. We assessed model performance using accuracy, precision, recall, F1-score, and AUC score. We used an autoencoder to perform dimensionality reduction prior to classification. We observed consistently better performance using logistic regression and linear discriminant analysis models when compared to the other models we tested. Classification performance generally improved when an autoencoder was used, except for when we predicted the need for adjuvant treatment. We obtained overall best results using a logistic regression model without autoencoding to predict the need for adjuvant treatment (F1-score = 0.869). Public Library of Science 2023-08-24 /pmc/articles/PMC10449163/ /pubmed/37616325 http://dx.doi.org/10.1371/journal.pone.0290566 Text en © 2023 Fleck et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Fleck, Julia L.
Hooijenga, Daniëlle
Phan, Raksmey
Xie, Xiaolan
Augusto, Vincent
Heudel, Pierre-Etienne
Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study
title Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study
title_full Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study
title_fullStr Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study
title_full_unstemmed Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study
title_short Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study
title_sort adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: a monocentric cohort retrospective study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449163/
https://www.ncbi.nlm.nih.gov/pubmed/37616325
http://dx.doi.org/10.1371/journal.pone.0290566
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