Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications

2017 ◽  
Vol 164 (2) ◽  
pp. 263-284 ◽  
Author(s):  
Jessica A. Cintolo-Gonzalez ◽  
Danielle Braun ◽  
Amanda L. Blackford ◽  
Emanuele Mazzola ◽  
Ahmet Acar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Clinical Applications ◽  
Risk Models ◽  
Comprehensive Overview ◽  
Models Validation

scholarly journals Erratum to: Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications

2017 ◽  
Vol 164 (3) ◽  
pp. 745-745 ◽  
Author(s):  
Jessica A. Cintolo-Gonzalez ◽  
Danielle Braun ◽  
Amanda L. Blackford ◽  
Emanuele Mazzola ◽  
Ahmet Acar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Clinical Applications ◽  
Risk Models ◽  
Comprehensive Overview ◽  
Models Validation

Toward robust mammography-based models for breast cancer risk

2021 ◽  
Vol 13 (578) ◽  
pp. eaba4373 ◽  
Author(s):  
Adam Yala ◽  
Peter G. Mikhael ◽  
Fredrik Strand ◽  
Gigin Lin ◽  
Kevin Smith ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Massachusetts General Hospital ◽  
The United States ◽  
University Hospital ◽  
Chang Gung Memorial Hospital ◽  
Risk Models

Improved breast cancer risk models enable targeted screening strategies that achieve earlier detection and less screening harm than existing guidelines. To bring deep learning risk models to clinical practice, we need to further refine their accuracy, validate them across diverse populations, and demonstrate their potential to improve clinical workflows. We developed Mirai, a mammography-based deep learning model designed to predict risk at multiple timepoints, leverage potentially missing risk factor information, and produce predictions that are consistent across mammography machines. Mirai was trained on a large dataset from Massachusetts General Hospital (MGH) in the United States and tested on held-out test sets from MGH, Karolinska University Hospital in Sweden, and Chang Gung Memorial Hospital (CGMH) in Taiwan, obtaining C-indices of 0.76 (95% confidence interval, 0.74 to 0.80), 0.81 (0.79 to 0.82), and 0.79 (0.79 to 0.83), respectively. Mirai obtained significantly higher 5-year ROC AUCs than the Tyrer-Cuzick model (P < 0.001) and prior deep learning models Hybrid DL (P < 0.001) and Image-Only DL (P < 0.001), trained on the same dataset. Mirai more accurately identified high-risk patients than prior methods across all datasets. On the MGH test set, 41.5% (34.4 to 48.5) of patients who would develop cancer within 5 years were identified as high risk, compared with 36.1% (29.1 to 42.9) by Hybrid DL (P = 0.02) and 22.9% (15.9 to 29.6) by the Tyrer-Cuzick model (P < 0.001).

scholarly journals Assessing Breast Cancer Risk Models in Marin County, a Population With High Rates of Delayed Childbirth

2014 ◽  
Vol 14 (3) ◽  
pp. 212-220.e1 ◽  
Author(s):  
Mark Powell ◽  
Farid Jamshidian ◽  
Kate Cheyne ◽  
Joanne Nititham ◽  
Lee Ann Prebil ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Models ◽  
Marin County

scholarly journals Considerations When Using Breast Cancer Risk Models for Women with Negative BRCA1/BRCA2 Mutation Results

2019 ◽  
Vol 112 (4) ◽  
pp. 418-422
Author(s):  
Robert J MacInnis ◽  
Yuyan Liao ◽  
Julia A Knight ◽  
Roger L Milne ◽  
Alice S Whittemore ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Invasive Breast Cancer ◽  
Disease Incidence ◽  
Brca2 Mutation ◽  
Estimation Algorithm ◽  
Breast Cancers ◽  
Risk Models ◽  
Replication Studies

Abstract The performance of breast cancer risk models for women with a family history but negative BRCA1 and/or BRCA2 mutation test results is uncertain. We calculated the cumulative 10-year invasive breast cancer risk at cohort entry for 14 657 unaffected women (96.1% had an affected relative) not known to carry BRCA1 or BRCA2 mutations at baseline using three pedigree-based models (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm, BRCAPRO, and International Breast Cancer Intervention Study). During follow-up, 482 women were diagnosed with invasive breast cancer. Mutation testing was conducted independent of incident cancers. All models underpredicted risk by 26.3%–56.7% for women who tested negative but whose relatives had not been tested (n = 1363; 63 breast cancers). Although replication studies with larger sample sizes are needed, until these models are recalibrated for women who test negative and have no relatives tested, caution should be used when considering changing the breast cancer risk management intensity of such women based on risk estimates from these models.

scholarly journals Performance of Common Genetic Variants in Breast-Cancer Risk Models

2010 ◽  
Vol 362 (11) ◽  
pp. 986-993 ◽  
Author(s):  
Sholom Wacholder ◽  
Patricia Hartge ◽  
Ross Prentice ◽  
Montserrat Garcia-Closas ◽  
Heather Spencer Feigelson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Genetic Variants ◽  
Risk Models ◽  
Common Genetic Variants

scholarly journals Risk models for breast cancer

2019 ◽  
Vol 15 (4) ◽  
pp. 503-510
Author(s):  
T. V. Pyatchanina ◽  
A. N. Ohorodnyk
Keyword(s):  
Breast Cancer ◽  
Prognostic Factors ◽  
Family History ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Scientific Evidence ◽  
Breast Cancer Patients ◽  
Brca1 And Brca2 ◽  
Risk Models ◽  
Brca1 And Brca2 Genes

Scientific evidence indicates the stabilization of indicators of morbidity and mortality from breast cancer in women in Ukraine and the existence of a number of models for predicting the breast cancer risk with the consideration of life style factors, detectable mutations of BRCA1 and BRCA2 genes, family history, as well as predicative and prognostic factors (clinical, molecular-biological) to determine the possible ways of the tumor process and the survival of breast cancer patients.

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.

scholarly journals Inclusion of Plasma Prolactin Levels in Current Risk Prediction Models of Premenopausal and Postmenopausal Breast Cancer

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Marike Gabrielson ◽  
Kumari Ubhayasekera ◽  
Bo Ek ◽  
Mikael Andersson Franko ◽  
Mikael Eriksson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Mammographic Density ◽  
Postmenopausal Breast Cancer ◽  
Premenopausal Women ◽  
Plasma Prolactin ◽  
Risk Models ◽  
Current Risk

Abstract Background Circulating plasma prolactin is associated with breast cancer risk and may improve our ability to identify high-risk women. Mammographic density is a strong risk factor for breast cancer, but the association with prolactin is unclear. We studied the association between breast cancer, established breast cancer risk factors and plasma prolactin, and improvement of risk prediction by adding prolactin. Methods We conducted a nested case-control study including 721 breast cancer patients and 1400 age-matched controls. Plasma prolactin levels were assayed using immunoassay and mammographic density measured by STRATUS. Odds ratios (ORs) were calculated by multivariable adjusted logistic regression, and improvement in the area under the curve for the risk of breast cancer by adding prolactin to established risk models. Statistical tests were two-sided. Results In multivariable adjusted analyses, prolactin was associated with risk of premenopausal (OR, top vs bottom quintile = 1.9; 1.88 (95% confidence interval [CI] = 1.08 to 3.26) but not with postmenopausal breast cancer. In postmenopausal cases prolactin increased by 10.6% per cBIRADS category (Ptrend = .03). In combined analyses of prolactin and mammographic density, ORs for women in the highest vs lowest tertile of both was 3.2 (95% CI = 1.3 to 7.7) for premenopausal women and 2.44 (95% CI = 1.44 to 4.14) for postmenopausal women. Adding prolactin to current risk models improved the area under the curve of the Gail model (+2.4 units, P = .02), Tyrer-Cuzick model (+3.8, P = .02), and the CAD2Y model (+1.7, P = .008) in premenopausal women. Conclusion Circulating plasma prolactin and mammographic density appear independently associated with breast cancer risk among premenopausal women, and prolactin may improve risk prediction by current risk models.

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