Refining Breast Cancer Risk Stratification: Additional Genes, Additional Information

2016 ◽  
pp. 44-56 ◽  
Author(s):  
Allison W. Kurian ◽  
Antonis C. Antoniou ◽  
Susan M. Domchek
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Clinical Utility ◽  
Prediction Models ◽  
Future Research ◽  
Average Risk ◽  
Clinical Validity ◽  
Screening And Prevention

Recent advances in genomic technology have enabled far more rapid, less expensive sequencing of multiple genes than was possible only a few years ago. Advances in bioinformatics also facilitate the interpretation of large amounts of genomic data. New strategies for cancer genetic risk assessment include multiplex sequencing panels of 5 to more than 100 genes (in which rare mutations are often associated with at least two times the average risk of developing breast cancer) and panels of common single-nucleotide polymorphisms (SNPs), combinations of which are generally associated with more modest cancer risks (more than twofold). Although these new multiple-gene panel tests are used in oncology practice, questions remain about the clinical validity and the clinical utility of their results. To translate this increasingly complex genetic information for clinical use, cancer risk prediction tools are under development that consider the joint effects of all susceptibility genes, together with other established breast cancer risk factors. Risk-adapted screening and prevention protocols are underway, with ongoing refinement as genetic knowledge grows. Priority areas for future research include the clinical validity and clinical utility of emerging genetic tests; the accuracy of developing cancer risk prediction models; and the long-term outcomes of risk-adapted screening and prevention protocols, in terms of patients’ experiences and survival.

scholarly journals Uncertainty quantification in breast cancer risk prediction models using self-reported family health history

2017 ◽  
Vol 1 (1) ◽  
pp. 53-59 ◽  
Author(s):  
Lance T. Pflieger ◽  
Clinton C. Mason ◽  
Julio C. Facelli
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Clinical Setting ◽  
Prediction Models ◽  
Family Health ◽  
Clinical Settings ◽  
Risk Prediction Models ◽  
Health History

Introduction. Family health history (FHx) is an important factor in breast and ovarian cancer risk assessment. As such, multiple risk prediction models rely strongly on FHx data when identifying a patient’s risk. These models were developed using verified information and when translated into a clinical setting assume that a patient’s FHx is accurate and complete. However, FHx information collected in a typical clinical setting is known to be imprecise and it is not well understood how this uncertainty may affect predictions in clinical settings. Methods. Using Monte Carlo simulations and existing measurements of uncertainty of self-reported FHx, we show how uncertainty in FHx information can alter risk classification when used in typical clinical settings. Results. We found that various models ranged from 52% to 64% for correct tier-level classification of pedigrees under a set of contrived uncertain conditions, but that significant misclassification are not negligible. Conclusions. Our work implies that (i) uncertainty quantification needs to be considered when transferring tools from a controlled research environment to a more uncertain environment (i.e, a health clinic) and (ii) better FHx collection methods are needed to reduce uncertainty in breast cancer risk prediction in clinical settings.

scholarly journals A systematic review and quality assessment of individualised breast cancer risk prediction models

2019 ◽  
Vol 121 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Javier Louro ◽  
Margarita Posso ◽  
Michele Hilton Boon ◽  
Marta Román ◽  
Laia Domingo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Quality Assessment ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Models

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

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Chang Ming ◽  
Valeria Viassolo ◽  
Nicole Probst-Hensch ◽  
Pierre O. Chappuis ◽  
Ivo D. Dinov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Population Based ◽  
Breast Cancer Patients ◽  
Adaptive Boosting ◽  
Discriminatory Accuracy

Abstract 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.

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