scholarly journals Discriminatory accuracy and potential clinical utility of genomic profiling for breast cancer risk in BRCA-negative women

2010 ◽  
Vol 127 (2) ◽  
pp. 479-487 ◽  
Author(s):  
E. Comen ◽  
L. Balistreri ◽  
M. Gönen ◽  
A. Dutra-Clarke ◽  
M. Fazio ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Clinical Utility ◽  
Genomic Profiling ◽  
Discriminatory Accuracy ◽  
Potential Clinical Utility ◽  
Brca Negative

scholarly journals Breast cancer risk and germline genomic profiling of women with neurofibromatosis type 1 who developed breast cancer

2017 ◽  
Vol 57 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Xia Wang ◽  
Jamie K. Teer ◽  
Renee N. Tousignant ◽  
Albert M. Levin ◽  
David Boulware ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Neurofibromatosis Type 1 ◽  
Neurofibromatosis Type ◽  
Genomic Profiling

scholarly journals Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models

Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5194
Author(s):  
Sherly X. Li ◽  
Roger L. Milne ◽  
Tú Nguyen-Dumont ◽  
Dallas R. English ◽  
Graham G. Giles ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Age Groups ◽  
Risk Scores ◽  
P Value ◽  
Risk Models ◽  
Discriminatory Accuracy ◽  
Incident Cases

Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50–65 years and unaffected at commencement of follow-up two (conducted in 2003–2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50–54, 55–59, 60–65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56–0.62) and IBIS (0.57, 95% CI 0.54–0.61) than the other models (p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.

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 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.

scholarly journals Discriminatory Accuracy of the Gail Model for Breast Cancer Risk Assessment among Iranian Women

2020 ◽  
Author(s):  
Sahar ROSTAMI ◽  
Ali RAFEI ◽  
Maryam DAMGHANIAN ◽  
Zohreh KHAKBAZAN ◽  
Farzad MALEKI ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Assessment ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Cancer Risk Assessment ◽  
Breast Cancer Patients ◽  
Gail Model ◽  
Female Population ◽  
Discriminatory Accuracy ◽  
Breast Cancer Risk Assessment

Background: The Gail model is the most well-known tool for breast cancer risk assessment worldwide. Although it was validated in various Western populations, inconsistent results were reported from Asian populations. We used data from a large case-control study and evaluated the discriminatory accuracy of the Gail model for breast cancer risk assessment among the Iranian female population. Methods: We used data from 942 breast cancer patients and 975 healthy controls at the Cancer Institute of Iran, Tehran, Iran, in 2016. We refitted the Gail model to our case-control data (the IR-Gail model). We compared the discriminatory power of the IR-Gail with the original Gail model, using ROC curve analyses and estimation of the area under the ROC curve (AUC). Results: Except for the history of biopsies that showed an extremely high relative risk (OR=9.1), the observed ORs were similar to the estimates observed in Gail's study. Incidence rates of breast cancer were extremely lower in Iran than in the USA, leading to a lower average absolute risk among the Iranian population (2.78, ±SD 2.45). The AUC was significantly improved after refitting the model, but it remained modest (0.636 vs. 0.627, ΔAUC = 0.009, bootstrapped P=0.008). We reported that the cut-point of 1.67 suggested in the Gail study did not discriminate between breast cancer patients and controls among the Iranian female population. Conclusion: Although the coefficients from the local study improved the discriminatory accuracy of the model, it remained modest. Cohort studies are warranted to evaluate the validity of the model for Iranian women.

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