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Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 4652540
Author(s) Ming, C.; Viassolo, V.; Probst-Hensch, N.; Dinov, I. D.; Chappuis, P. O.; Katapodi, M. C.
Author(s) at UniBasel Probst Hensch, Nicole
Katapodi, Maria
Year 2020
Title Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations
Journal British Journal of Cancer (BJC)
Volume 123
Number 5
Pages / Article-Number 860-867
Mesh terms Adult; Aged; Breast Neoplasms, epidemiology; Early Detection of Cancer; Female; Humans; Machine Learning; Middle Aged; Retrospective Studies; Risk; Young Adult
Abstract 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
Publisher Springer Nature
ISSN/ISBN 0007-0920 ; 1532-1827
URL https://doi.org/10.1038/s41416-020-0937-0
edoc-URL https://edoc.unibas.ch/91190/
Full Text on edoc Available
Digital Object Identifier DOI 10.1038/s41416-020-0937-0
PubMed ID http://www.ncbi.nlm.nih.gov/pubmed/32565540
ISI-Number WOS:000541687700001
Document type (ISI) Journal Article
 
   

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21/06/2024