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Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations

Ming, C. and Viassolo, V. and Probst-Hensch, N. and Dinov, I. D. and Chappuis, P. O. and Katapodi, M. C.. (2020) Machine learning-based lifetime breast cancer risk reclassification compared with the BOADICEA model: impact on screening recommendations. British Journal of Cancer (BJC), 123 (5). pp. 860-867.

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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 </= 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.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Chronic Disease Epidemiology > Exposome Science (Probst-Hensch)
03 Faculty of Medicine > Departement Public Health > Sozial- und Präventivmedizin > Exposome Science (Probst-Hensch)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
UniBasel Contributors:Probst Hensch, Nicole and Katapodi, Maria C
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Springer Nature
ISSN:0007-0920
e-ISSN:1532-1827
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
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Last Modified:26 Jul 2023 08:28
Deposited On:28 Dec 2022 13:35

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