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A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification

Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, pred...

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Autores principales: Massafra, Raffaella, Comes, Maria Colomba, Bove, Samantha, Didonna, Vittorio, Diotaiuti, Sergio, Giotta, Francesco, Latorre, Agnese, La Forgia, Daniele, Nardone, Annalisa, Pomarico, Domenico, Ressa, Cosmo Maurizio, Rizzo, Alessandro, Tamborra, Pasquale, Zito, Alfredo, Lorusso, Vito, Fanizzi, Annarita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9484691/
https://www.ncbi.nlm.nih.gov/pubmed/36121822
http://dx.doi.org/10.1371/journal.pone.0274691
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author Massafra, Raffaella
Comes, Maria Colomba
Bove, Samantha
Didonna, Vittorio
Diotaiuti, Sergio
Giotta, Francesco
Latorre, Agnese
La Forgia, Daniele
Nardone, Annalisa
Pomarico, Domenico
Ressa, Cosmo Maurizio
Rizzo, Alessandro
Tamborra, Pasquale
Zito, Alfredo
Lorusso, Vito
Fanizzi, Annarita
author_facet Massafra, Raffaella
Comes, Maria Colomba
Bove, Samantha
Didonna, Vittorio
Diotaiuti, Sergio
Giotta, Francesco
Latorre, Agnese
La Forgia, Daniele
Nardone, Annalisa
Pomarico, Domenico
Ressa, Cosmo Maurizio
Rizzo, Alessandro
Tamborra, Pasquale
Zito, Alfredo
Lorusso, Vito
Fanizzi, Annarita
author_sort Massafra, Raffaella
collection PubMed
description Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure.
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spelling pubmed-94846912022-09-20 A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification Massafra, Raffaella Comes, Maria Colomba Bove, Samantha Didonna, Vittorio Diotaiuti, Sergio Giotta, Francesco Latorre, Agnese La Forgia, Daniele Nardone, Annalisa Pomarico, Domenico Ressa, Cosmo Maurizio Rizzo, Alessandro Tamborra, Pasquale Zito, Alfredo Lorusso, Vito Fanizzi, Annarita PLoS One Research Article Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori “Giovanni Paolo II” in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure. Public Library of Science 2022-09-19 /pmc/articles/PMC9484691/ /pubmed/36121822 http://dx.doi.org/10.1371/journal.pone.0274691 Text en © 2022 Massafra 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
Massafra, Raffaella
Comes, Maria Colomba
Bove, Samantha
Didonna, Vittorio
Diotaiuti, Sergio
Giotta, Francesco
Latorre, Agnese
La Forgia, Daniele
Nardone, Annalisa
Pomarico, Domenico
Ressa, Cosmo Maurizio
Rizzo, Alessandro
Tamborra, Pasquale
Zito, Alfredo
Lorusso, Vito
Fanizzi, Annarita
A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification
title A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification
title_full A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification
title_fullStr A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification
title_full_unstemmed A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification
title_short A machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification
title_sort machine learning ensemble approach for 5- and 10-year breast cancer invasive disease event classification
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9484691/
https://www.ncbi.nlm.nih.gov/pubmed/36121822
http://dx.doi.org/10.1371/journal.pone.0274691
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