Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models.
Background: 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) of...
| Published in: | Breast Cancer Research Vol. 21; no. 1 |
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| Main Authors: | , , , , , |
| Format: | research Journal Article |
| Published: |
BioMed Central
6/20/2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137097199&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137097199 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14655411 8UYJ jtl: Breast Cancer Research issn: 14655411 maglogo: N pubinfo: dt: 6/20/2019 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 137097199 137097199 NLM31221197 137097199 10.1186/s13058-019-1158-4 NLM31221197 137097199 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models. aug: au: Ming, Chang Viassolo, Valeria Probst-Hensch, Nicole Chappuis, Pierre O. Dinov, Ivo D. Katapodi, Maria C. affil: Nursing Science, Faculty of Medicine, University of Basel, Bernoullistrasse 28, Room 118, 4056, Basel, Switzerland sug: subj: Models, Theoretical Disease Susceptibility Breast Neoplasms Etiology Algorithms Population Surveillance Adult Female Prognosis Middle Age ROC Curve Risk Assessment Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Adult: 19-44 years Middle Aged: 45-64 years Female ab: Background: 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.Methods: 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).Results: 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.Conclusions: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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