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Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations
BACKGROUND: The clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinic...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463251/ https://www.ncbi.nlm.nih.gov/pubmed/32565540 http://dx.doi.org/10.1038/s41416-020-0937-0 |
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author | Ming, Chang Viassolo, Valeria Probst-Hensch, Nicole Dinov, Ivo D. Chappuis, Pierre O. Katapodi, Maria C. |
author_facet | Ming, Chang Viassolo, Valeria Probst-Hensch, Nicole Dinov, Ivo D. Chappuis, Pierre O. Katapodi, Maria C. |
author_sort | Ming, Chang |
collection | PubMed |
description | BACKGROUND: The clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices. METHODS: We used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20–80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol. RESULTS: The predictive accuracy of ML-based algorithms (0.843 ≤ AU-ROC ≤ 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as ‘near population’ risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50. CONCLUSION: ML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision- making for the initiation of screening. |
format | Online Article Text |
id | pubmed-7463251 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-74632512020-09-11 Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations Ming, Chang Viassolo, Valeria Probst-Hensch, Nicole Dinov, Ivo D. Chappuis, Pierre O. Katapodi, Maria C. Br J Cancer Article BACKGROUND: The clinical utility of machine-learning (ML) algorithms for breast cancer risk prediction and screening practices is unknown. We compared classification of lifetime breast cancer risk based on ML and the BOADICEA model. We explored the differences in risk classification and their clinical impact on screening practices. METHODS: We used three different ML algorithms and the BOADICEA model to estimate lifetime breast cancer risk in a sample of 112,587 individuals from 2481 families from the Oncogenetic Unit, Geneva University Hospitals. Performance of algorithms was evaluated using the area under the receiver operating characteristic (AU-ROC) curve. Risk reclassification was compared for 36,146 breast cancer-free women of ages 20–80. The impact on recommendations for mammography surveillance was based on the Swiss Surveillance Protocol. RESULTS: The predictive accuracy of ML-based algorithms (0.843 ≤ AU-ROC ≤ 0.889) was superior to BOADICEA (AU-ROC = 0.639) and reclassified 35.3% of women in different risk categories. The largest reclassification (20.8%) was observed in women characterised as ‘near population’ risk by BOADICEA. Reclassification had the largest impact on screening practices of women younger than 50. CONCLUSION: ML-based reclassification of lifetime breast cancer risk occurred in approximately one in three women. Reclassification is important for younger women because it impacts clinical decision- making for the initiation of screening. Nature Publishing Group UK 2020-06-22 2020-09-01 /pmc/articles/PMC7463251/ /pubmed/32565540 http://dx.doi.org/10.1038/s41416-020-0937-0 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ming, Chang Viassolo, Valeria Probst-Hensch, Nicole Dinov, Ivo D. Chappuis, Pierre O. Katapodi, Maria C. Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations |
title | Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations |
title_full | Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations |
title_fullStr | Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations |
title_full_unstemmed | Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations |
title_short | Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations |
title_sort | machine learning-based lifetime breast cancer risk reclassification compared with the boadicea model: impact on screening recommendations |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463251/ https://www.ncbi.nlm.nih.gov/pubmed/32565540 http://dx.doi.org/10.1038/s41416-020-0937-0 |
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