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Machine learning techniques for personalized breast cancer risk prediction : comparison with the BCRAT and BOADICEA models

Ming, Chang and Viassolo, Valeria and Probst-Hensch, Nicole and Chappuis, Pierre O. and Dinov, Ivo D. and Katapodi, Maria C.. (2019) Machine learning techniques for personalized breast cancer risk prediction : comparison with the BCRAT and BOADICEA models. Breast Cancer Research, 21. p. 75.

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Abstract

Comprehensive breast cancer risk prediction models enable identifying and targeting women at high-risk, while reducing interventions in those at low-risk. Breast cancer risk prediction models used in clinical practice have low discriminatory accuracy (0.53-0.64). Machine learning (ML) offers an alternative approach to standard prediction modeling that may address current limitations and improve accuracy of those tools. The purpose of this study was to compare the discriminatory accuracy of ML-based estimates against a pair of established methods-the Breast Cancer Risk Assessment Tool (BCRAT) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) models.; We quantified and compared the performance of eight different ML methods to the performance of BCRAT and BOADICEA using eight simulated datasets and two retrospective samples: a random population-based sample of U.S. breast cancer patients and their cancer-free female relatives (N = 1143), and a clinical sample of Swiss breast cancer patients and cancer-free women seeking genetic evaluation and/or testing (N = 2481).; Predictive accuracy (AU-ROC curve) reached 88.28% using ML-Adaptive Boosting and 88.89% using ML-random forest versus 62.40% with BCRAT for the U.S. population-based sample. Predictive accuracy reached 90.17% using ML-adaptive boosting and 89.32% using ML-Markov chain Monte Carlo generalized linear mixed model versus 59.31% with BOADICEA for the Swiss clinic-based sample.; There was a striking improvement in the accuracy of classification of women with and without breast cancer achieved with ML algorithms compared to the state-of-the-art model-based approaches. High-accuracy prediction techniques are important in personalized medicine because they facilitate stratification of prevention strategies and individualized clinical management.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Chronic Disease Epidemiology > Genetic Epidemiology of Non-Communicable Diseases (Probst-Hensch)
03 Faculty of Medicine > Departement Public Health > Sozial- und Präventivmedizin > Genetic Epidemiology of Non-Communicable Diseases (Probst-Hensch)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
UniBasel Contributors:Probst Hensch, Nicole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:BioMed Central
e-ISSN:1465-542X
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
Last Modified:10 Jul 2019 14:15
Deposited On:10 Jul 2019 14:15

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