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Hurson AN, Pal Choudhury P, Gao C, Hüsing A, Eriksson M, Shi M, Jones ME, Evans DGR, Milne RL, Gaudet MM, Vachon CM, Chasman DI, Easton DF, Schmidt MK, Kraft P, Garcia-Closas M, Chatterjee N. Prospective evaluation of a breast-cancer risk model integrating classical risk factors and polygenic risk in 15 cohorts from six countries. Int J Epidemiol 2022; 50:1897-1911. [PMID: 34999890 PMCID: PMC8743128 DOI: 10.1093/ije/dyab036] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 02/19/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Rigorous evaluation of the calibration and discrimination of breast-cancer risk-prediction models in prospective cohorts is critical for applications under clinical guidelines. We comprehensively evaluated an integrated model incorporating classical risk factors and a 313-variant polygenic risk score (PRS) to predict breast-cancer risk. METHODS Fifteen prospective cohorts from six countries with 239 340 women (7646 incident breast-cancer cases) of European ancestry aged 19-75 years were included. Calibration of 5-year risk was assessed by comparing expected and observed proportions of cases overall and within risk categories. Risk stratification for women of European ancestry aged 50-70 years in those countries was evaluated by the proportion of women and future cases crossing clinically relevant risk thresholds. RESULTS Among women <50 years old, the median (range) expected-to-observed ratio for the integrated model across 15 cohorts was 0.9 (0.7-1.0) overall and 0.9 (0.7-1.4) at the highest-risk decile; among women ≥50 years old, these were 1.0 (0.7-1.3) and 1.2 (0.7-1.6), respectively. The proportion of women identified above a 3% 5-year risk threshold (used for recommending risk-reducing medications in the USA) ranged from 7.0% in Germany (∼841 000 of 12 million) to 17.7% in the USA (∼5.3 of 30 million). At this threshold, 14.7% of US women were reclassified by adding the PRS to classical risk factors, with identification of 12.2% of additional future cases. CONCLUSION Integrating a 313-variant PRS with classical risk factors can improve the identification of European-ancestry women at elevated risk who could benefit from targeted risk-reducing strategies under current clinical guidelines.
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Affiliation(s)
- Amber N Hurson
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Parichoy Pal Choudhury
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Chi Gao
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Anika Hüsing
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Karolinska Univ Hospital, Stockholm, Sweden
| | - Min Shi
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA
| | - Michael E Jones
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - D Gareth R Evans
- Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester NIHR Biomedical Research Centre, Manchester University Hospitals NHS, Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Mia M Gaudet
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA, USA
| | - Celine M Vachon
- Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Marjanka K Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute—Antoni van Leeuwenhoek hospital, Amsterdam, The Netherlands
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Nilanjan Chatterjee
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
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Bonnet E, Daures JP, Landais P. Determination of thresholds of risk in women at average risk of breast cancer to personalize the organized screening program. Sci Rep 2021; 11:19104. [PMID: 34580360 PMCID: PMC8476568 DOI: 10.1038/s41598-021-98604-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 09/06/2021] [Indexed: 11/08/2022] Open
Abstract
In France, more than 10 million women at "average" risk of breast cancer (BC), are included in the organized BC screening. Existing predictive models of BC risk are not adapted to the French population. Thus, we set up a new score in the French Hérault region and looked for subgroups at a graded level of risk in women at "average" risk. We recruited a retrospective cohort of women, aged 50 to 60, who underwent the organized BC screening, and included 2241 non-cancer women and 527 who developed a BC during a 12-year follow-up period (2006-2018). The risk factors identified were high breast density (ACR BI-RADS grading)(B vs A: HR = 1.41, 95%CI [1.05; 1.9], p = 0.023; C vs A: HR = 1.65 [1.2; 2.27], p = 0.02 ; D vs A: HR = 2.11 [1.25;3.58], p = 0.006), a history of maternal breast cancer (HR = 1.61 [1.24; 2.09], p < 0.001), and socioeconomic difficulties (HR 1.23 [1.09; 1.55], p = 0.003). While early menopause (HR = 0.36 [0.13; 0.99], p = 0.003) and an age at menarche after 12 years (HR = 0.77 [0.63; 0.95], p = 0.047) were protective factors. We identified 3 groups at risk: lower, average, and higher, respectively. A low threshold was characterized at 1.9% of 12-year risk and a high threshold at 4.5% 12-year risk. Mean 12-year risks in the 3 groups of risk were 1.37%, 2.68%, and 5.84%, respectively. Thus, 12% of women presented a level of risk different from the average risk group, corresponding to 600,000 women involved in the French organized BC screening, enabling to propose a new strategy to personalize the national BC screening. On one hand, for women at lower risk, we proposed to reduce the frequency of mammograms and on the other hand, for women at higher risk, we suggested intensifying surveillance.
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Affiliation(s)
- Emmanuel Bonnet
- Montpellier University, EA2415, Institut Universitaire de recherche clinique, 34093, Montpellier Cedex 5, France.
- Languedoc Mutualité, Nouvelles Technologies, AESIO, Montpellier, France.
| | - Jean-Pierre Daures
- Montpellier University, EA2415, Institut Universitaire de recherche clinique, 34093, Montpellier Cedex 5, France
- Languedoc Mutualité, Nouvelles Technologies, AESIO, Montpellier, France
| | - Paul Landais
- Montpellier University, EA2415, Institut Universitaire de recherche clinique, 34093, Montpellier Cedex 5, France
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Li SX, Milne RL, Nguyen-Dumont T, Wang X, English DR, Giles GG, Southey MC, Antoniou AC, Lee A, Li S, Winship I, Hopper JL, Terry MB, MacInnis RJ. Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models. JNCI Cancer Spectr 2021; 5:pkab021. [PMID: 33977228 PMCID: PMC8099999 DOI: 10.1093/jncics/pkab021] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/13/2020] [Accepted: 02/19/2021] [Indexed: 11/13/2022] Open
Abstract
Background The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm and the International Breast Cancer Intervention Study breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, which now incorporate polygenic risk scores (PRSs), using a prospective cohort study. Methods We used a case-cohort design, involving women in the Melbourne Collaborative Cohort Study aged 50-75 years when surveyed in 2003-2007, of whom 408 had a first primary breast cancer diagnosed within 10 years (cases), and 2783 were from the subcohort. Ten-year risks were calculated based on lifestyle factors, family history data, and a 313-variant PRS. Discrimination was assessed using a C-statistic compared with 0.50 and calibration using the ratio of expected to observed number of cases (E/O). Results When the PRS was added to models with lifestyle factors and family history, the C-statistic (95% confidence interval [CI]) increased from 0.57 (0.54 to 0.60) to 0.62 (0.60 to 0.65) using IBIS and from 0.56 (0.53 to 0.59) to 0.62 (0.59 to 0.64) using BOADICEA. IBIS underpredicted risk (E/O = 0.62, 95% CI = 0.48 to 0.80) for women in the lowest risk category (<1.7%) and overpredicted risk (E/O = 1.40, 95% CI = 1.18 to 1.67) in the highest risk category (≥5%), using the Hosmer-Lemeshow test for calibration in quantiles of risk and a 2-sided P value less than .001. BOADICEA underpredicted risk (E/O = 0.82, 95% CI = 0.67 to 0.99) in the second highest risk category (3.4%-5%); the Hosmer-Lemeshow test and a 2-sided P value was equal to .02. Conclusions Although the inclusion of a 313 genetic variant PRS doubles discriminatory accuracy (relative to reference 0.50), models with and without this PRS have relatively modest discrimination and might require recalibration before their clinical and wider use are promoted.
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Affiliation(s)
- Sherly X Li
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Xiaochuan Wang
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Dallas R English
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
| | - Andrew Lee
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
| | - Ingrid Winship
- Department of Genomic Medicine, Royal Melbourne Hospital, Melbourne, Victoria, Australia
- Department of Medicine, Royal Melbourne Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA
| | - Robert J MacInnis
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Victoria, Australia
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Vellal AD, Sirinukunwattan K, Kensler KH, Baker GM, Stancu AL, Pyle ME, Collins LC, Schnitt SJ, Connolly JL, Veta M, Eliassen AH, Tamimi RM, Heng YJ. Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer. JNCI Cancer Spectr 2021; 5:pkaa119. [PMID: 33644680 PMCID: PMC7898083 DOI: 10.1093/jncics/pkaa119] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 11/04/2020] [Accepted: 12/18/2020] [Indexed: 12/16/2022] Open
Abstract
Background New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method to the BBD BC nested case-control study within the Nurses' Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of BC. Methods Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 whole slide images from 293 cases who developed BC and 1132 controls who did not. Percentages of each tissue region were calculated, and 615 morphometric features were extracted. Elastic net regression was used to create a BC morphometric signature. Associations between BC risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and body mass index evaluated the relationship between tissue composition and BC risk. All statistical tests were 2-sided. Results Among controls, direction of associations between BBD subtypes, parity, and number of births with breast composition varied by tissue region; select regions were associated with childhood body size, body mass index, age of menarche, and menopausal status (all P < .05). A higher proportion of epithelial tissue was associated with increased BC risk (odds ratio = 1.39, 95% confidence interval = 0.91 to 2.14, for highest vs lowest quartiles, P trend = .047). No morphometric signature was associated with BC. Conclusions The amount of epithelial tissue may be incorporated into risk assessment models to improve BC risk prediction.
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Affiliation(s)
- Adithya D Vellal
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Korsuk Sirinukunwattan
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Engineering Science, Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, UK
- Big Data Institute, University of Oxford, Li Ka Shing Centre for Health Information and Discovery, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford University NHS Foundation Trust, Oxford, UK
| | - Kevin H Kensler
- Division of Population Sciences, Dana Farber Cancer Institute, Boston, MA, USA
| | - Gabrielle M Baker
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Andreea L Stancu
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael E Pyle
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Laura C Collins
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Stuart J Schnitt
- Dana-Farber/Brigham and Women's Cancer Center, Harvard Medical School, Dana-Farber Cancer Institute-Brigham and Women's Hospital, Boston, MA, USA
| | - James L Connolly
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mitko Veta
- Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Yujing J Heng
- Department of Pathology, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
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5
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Pal Choudhury P, Wilcox AN, Brook MN, Zhang Y, Ahearn T, Orr N, Coulson P, Schoemaker MJ, Jones ME, Gail MH, Swerdlow AJ, Chatterjee N, Garcia-Closas M. Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification. J Natl Cancer Inst 2020; 112:278-285. [PMID: 31165158 DOI: 10.1093/jnci/djz113] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 01/31/2019] [Accepted: 05/29/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification. METHODS Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35-74 years. Risk projections in a target population of US white non-Hispanic women age 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS). RESULTS The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years. CONCLUSIONS iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.
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Affiliation(s)
| | - Amber N Wilcox
- Johns Hopkins University, Baltimore, MD.,Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda
| | | | - Yan Zhang
- Department of Biostatistics, Bloomberg School of Public Health
| | - Thomas Ahearn
- Johns Hopkins University, Baltimore, MD.,Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda
| | - Nick Orr
- Department of Biostatistics, Bloomberg School of Public Health.,Department of Oncology, School of Medicine.,Division of Breast Cancer Research, The Institute of Cancer Research, London, UK.,Centre for Cancer Research and Cell Biology, Queen's University Belfast, Belfast, UK
| | | | | | | | - Mitchell H Gail
- Johns Hopkins University, Baltimore, MD.,Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology.,Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | | | - Montserrat Garcia-Closas
- Johns Hopkins University, Baltimore, MD.,Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda
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Solikhah S, Nurdjannah S. Assessment of the risk of developing breast cancer using the Gail model in Asian females: A systematic review. Heliyon 2020; 6:e03794. [PMID: 32346636 PMCID: PMC7182726 DOI: 10.1016/j.heliyon.2020.e03794] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 02/25/2020] [Accepted: 04/14/2020] [Indexed: 12/13/2022] Open
Abstract
Introduction Currently, the Breast Cancer Risk Assessment Tool (BCRAT), also known as the Gail model (GM) has been widely recognized and adapted for to study disparity in racial and ethnic groups in America including Asian and Pacific Islander American females. However, its applicability outside America remains uncertain due to diversity in epidemiology and risk factors of breast cancer in populations especially in Asian females. We sought to evaluate the performance of the GM to predict breast cancer risk in Asian countries. Material and methods This study identified articles published from 2010 by searching PubMed, MEDLINE, Scopus, Web of Science, Google Scholar and gray literature. The initial search terms were breast cancer, mammary, carcinoma, tumor, neoplasm, risk assessment tool, BCRAT, breast cancer prediction, Gail model, Asia, and Asian. Results The search yielded 20 articles, with 7 articles addressing the AUC and/or the expected (E) to observed (O) ratio of predicted breast cancer risk, representing the accuracy of the GM in the Asian population. One publication reported the sensitivity and specificity but no AUC. None of the studies were accepted as the standard for reporting prognostic models. Several studies reported good prognostic testing and likely developed a new model modifying the items in the instrument. Conclusion The results are not strong enough to develop breast cancer risk in the setting of Asian countries. Involving the breast cancer risk of the Asian population in developing a prognostic model with good statistical understanding is particularly important and can reduce flawed or biased models. Identifying the best methods to achieve well-suited prognostic models in the Asian population should be a priority.
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Affiliation(s)
- Solikhah Solikhah
- Faculty of Public Health, Universitas Ahmad Dahlan, Yogyakarta, 55166, Indonesia.,Dynamic Social Study Center, Universitas Ahmad Dahlan, Yogyakarta, 55166, Indonesia
| | - Sitti Nurdjannah
- Faculty of Public Health, Universitas Ahmad Dahlan, Yogyakarta, 55166, Indonesia
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7
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Validation of two US breast cancer risk prediction models in German women. Cancer Causes Control 2020; 31:525-536. [PMID: 32253639 DOI: 10.1007/s10552-020-01272-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 01/31/2020] [Indexed: 12/17/2022]
Abstract
PURPOSE There are no models for German women that predict absolute risk of invasive breast cancer (BC), i.e., the probability of developing BC over a prespecified time period, given a woman's age and characteristics, while accounting for competing risks. We thus validated two absolute BC risk models (BCRAT, BCRmod) developed for US women in German women. BCRAT uses a woman's medical, reproductive, and BC family history; BCRmod adds modifiable risk factors (body mass index, hormone replacement therapy and alcohol use). METHODS We assessed model calibration by comparing observed BC numbers (O) to expected numbers (E) computed from BCRmod/BCRAT for German women enrolled in the prospective European Prospective Investigation into Cancer and Nutrition (EPIC), and after updating the models with German BC incidence/competing mortality rates. We also compared 1-year BC risk predicted for all German women using the German Health Interview and Examination Survey for Adults (DEGS) with overall German BC incidence. Discriminatory performance was quantified by the area under the receiver operator characteristics curve (AUC). RESULTS Among 22,098 EPIC-Germany women aged 40+ years, 745 BCs occurred (median follow-up: 11.9 years). Both models had good calibration for total follow-up, EBCRmod/O = 1.08 (95% confidence interval: 0.95-1.21), and EBCRAT/O = 0.99(0.87-1.11), and over 5 years. Compared to German BC incidence rates, both models somewhat overestimated 1-year risk for women aged 55+ and 70+ years. For total follow-up, AUCBCRmod = 0.61(0.58-0.63) and AUCBCRAT = 0.58(0.56-0.61), with similar values for 5-year follow-up. CONCLUSION US BC risk models showed adequate calibration in German women. Discriminatory performance was comparable to that in US women. These models thus could be applied for risk prediction in German women.
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8
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Nickson C, Procopio P, Velentzis LS, Carr S, Devereux L, Mann GB, James P, Lee G, Wellard C, Campbell I. Prospective validation of the NCI Breast Cancer Risk Assessment Tool (Gail Model) on 40,000 Australian women. Breast Cancer Res 2018; 20:155. [PMID: 30572910 PMCID: PMC6302513 DOI: 10.1186/s13058-018-1084-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 11/25/2018] [Indexed: 01/24/2023] Open
Abstract
Background There is a growing interest in delivering more personalised, risk-based breast cancer screening protocols. This requires population-level validation of practical models that can stratify women into breast cancer risk groups. Few studies have evaluated the Gail model (NCI Breast Cancer Risk Assessment Tool) in a population screening setting; we validated this tool in a large, screened population. Methods We used data from 40,158 women aged 50–69 years (via the lifepool cohort) participating in Australia’s BreastScreen programme. We investigated the association between Gail scores and future invasive breast cancer, comparing observed and expected outcomes by Gail score ranked groups. We also used machine learning to rank Gail model input variables by importance and then assessed the incremental benefit in risk prediction obtained by adding variables in order of diminishing importance. Results Over a median of 4.3 years, the Gail model predicted 612 invasive breast cancers compared with 564 observed cancers (expected/observed (E/O) = 1.09, 95% confidence interval (CI) 1.00–1.18). There was good agreement across decile groups of Gail scores (χ2 = 7.1, p = 0.6) although there was some overestimation of cancer risk in the top decile of our study group (E/O = 1.65, 95% CI 1.33–2.07). Women in the highest quintile (Q5) of Gail scores had a 2.28-fold increased risk of breast cancer (95% CI 1.73–3.02, p < 0.0001) compared with the lowest quintile (Q1). Compared with the median quintile, women in Q5 had a 34% increased risk (95% CI 1.06–1.70, p = 0.014) and those in Q1 had a 41% reduced risk (95% CI 0.44–0.79, p < 0.0001). Similar patterns were observed separately for women aged 50–59 and 60–69 years. The model’s overall discrimination was modest (area under the curve (AUC) 0.59, 95% CI 0.56–0.61). A reduced Gail model excluding information on ethnicity and hyperplasia was comparable to the full Gail model in terms of correctly stratifying women into risk groups. Conclusions This study confirms that the Gail model (or a reduced model excluding information on hyperplasia and ethnicity) can effectively stratify a screened population aged 50–69 years according to the risk of future invasive breast cancer. This information has the potential to enable more personalised, risk-based screening strategies that aim to improve the balance of the benefits and harms of screening. Electronic supplementary material The online version of this article (10.1186/s13058-018-1084-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Carolyn Nickson
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia. .,Cancer Research Division, Cancer Council NSW, Woolloomooloo, NSW, 2011, Australia.
| | - Pietro Procopio
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia.,Cancer Research Division, Cancer Council NSW, Woolloomooloo, NSW, 2011, Australia
| | - Louiza S Velentzis
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia.,Cancer Research Division, Cancer Council NSW, Woolloomooloo, NSW, 2011, Australia
| | - Sarah Carr
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia
| | - Lisa Devereux
- Lifepool Study, Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia
| | - Gregory Bruce Mann
- Breast Service, Royal Women's and Royal Melbourne Hospital, Parkville, Victoria, 3050, Australia.,Department of Surgery, The University of Melbourne, Parkville, 3010, Australia
| | - Paul James
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, 3000, Australia.,Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Parkville, Victoria, 3052, Australia
| | - Grant Lee
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia
| | - Cameron Wellard
- Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, 3010, Australia
| | - Ian Campbell
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, 3000, Australia.,Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, 3000, Australia
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9
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Wang X, Huang Y, Li L, Dai H, Song F, Chen K. Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis. Breast Cancer Res 2018; 20:18. [PMID: 29534738 PMCID: PMC5850919 DOI: 10.1186/s13058-018-0947-5] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 02/26/2018] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The Gail model has been widely used and validated with conflicting results. The current study aims to evaluate the performance of different versions of the Gail model by means of systematic review and meta-analysis with trial sequential analysis (TSA). METHODS Three systematic review and meta-analyses were conducted. Pooled expected-to-observed (E/O) ratio and pooled area under the curve (AUC) were calculated using the DerSimonian and Laird random-effects model. Pooled sensitivity, specificity and diagnostic odds ratio were evaluated by bivariate mixed-effects model. TSA was also conducted to determine whether the evidence was sufficient and conclusive. RESULTS Gail model 1 accurately predicted breast cancer risk in American women (pooled E/O = 1.03; 95% CI 0.76-1.40). The pooled E/O ratios of Caucasian-American Gail model 2 in American, European and Asian women were 0.98 (95% CI 0.91-1.06), 1.07 (95% CI 0.66-1.74) and 2.29 (95% CI 1.95-2.68), respectively. Additionally, Asian-American Gail model 2 overestimated the risk for Asian women about two times (pooled E/O = 1.82; 95% CI 1.31-2.51). TSA showed that evidence in Asian women was sufficient; nonetheless, the results in American and European women need further verification. The pooled AUCs for Gail model 1 in American and European women and Asian females were 0.55 (95% CI 0.53-0.56) and 0.75 (95% CI 0.63-0.88), respectively, and the pooled AUCs of Caucasian-American Gail model 2 for American, Asian and European females were 0.61 (95% CI 0.59-0.63), 0.55 (95% CI 0.52-0.58) and 0.58 (95% CI 0.55-0.62), respectively. The pooled sensitivity, specificity and diagnostic odds ratio of Gail model 1 were 0.63 (95% CI 0.27-0.89), 0.91 (95% CI 0.87-0.94) and 17.38 (95% CI 2.66-113.70), respectively, and the corresponding indexes of Gail model 2 were 0.35 (95% CI 0.17-0.59), 0.86 (95% CI 0.76-0.92) and 3.38 (95% CI 1.40-8.17), respectively. CONCLUSIONS The Gail model was more accurate in predicting the incidence of breast cancer in American and European females, while far less useful for individual-level risk prediction. Moreover, the Gail model may overestimate the risk in Asian women and the results were further validated by TSA, which is an addition to the three previous systematic review and meta-analyses. TRIAL REGISTRATION PROSPERO CRD42016047215 .
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Affiliation(s)
- Xin Wang
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Yubei Huang
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Lian Li
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Hongji Dai
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Fengju Song
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Key Laboratory of Cancer Prevention and Therapy, Tianjin Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Huanhu Xi Road, Tiyuan Bei, Hexi District, Tianjin, 300060 People’s Republic of China
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10
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Dartois L, Gauthier É, Heitzmann J, Baglietto L, Michiels S, Mesrine S, Boutron-Ruault MC, Delaloge S, Ragusa S, Clavel-Chapelon F, Fagherazzi G. A comparison between different prediction models for invasive breast cancer occurrence in the French E3N cohort. Breast Cancer Res Treat 2015; 150:415-26. [PMID: 25744293 DOI: 10.1007/s10549-015-3321-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 02/23/2015] [Indexed: 02/07/2023]
Abstract
Breast cancer remains a global health concern with a lack of high discriminating prediction models. The k-nearest-neighbor algorithm (kNN) estimates individual risks using an intuitive tool. This study compares the performances of this approach with the Cox and the Gail models for the 5-year breast cancer risk prediction. The study included 64,995 women from the French E3N prospective cohort. The sample was divided into a learning (N = 51,821) series to learn the models using fivefold cross-validation and a validation (N = 13,174) series to evaluate them. The area under the receiver operating characteristic curve (AUC) and the expected over observed number of cases (E/O) ratio were estimated. In the two series, 393 and 78 premenopausal and 537 and 98 postmenopausal breast cancers were diagnosed. The discrimination values of the best combinations of predictors obtained from cross-validation ranged from 0.59 to 0.60. In the validation series, the AUC values in premenopausal and postmenopausal women were 0.583 [0.520; 0.646] and 0.621 [0.563; 0.679] using the kNN and 0.565 [0.500; 0.631] and 0.617 [0.561; 0.673] using the Cox model. The E/O ratios were 1.26 and 1.28 in premenopausal women and 1.44 and 1.40 in postmenopausal women. The applied Gail model provided AUC values of 0.614 [0.554; 0.675] and 0.549 [0.495; 0.604] and E/O ratios of 0.78 and 1.12. This study shows that the prediction performances differed according to menopausal status when using parametric statistical tools. The k-nearest-neighbor approach performed well, and discrimination was improved in postmenopausal women compared with the Gail model.
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Affiliation(s)
- Laureen Dartois
- Inserm (Institut National de la Santé et de la Recherche Médicale), Centre for Research in Epidemiology and Population Health (CESP), U1018, Team 9, 114 rue Édouard Vaillant, 94805, Villejuif Cedex, France
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11
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Abstract
PURPOSE OF REVIEW Breast cancer and gynecological cancers impact a significant portion of women each year. Identifying women at high risk is essential for implementation of screening and risk reduction recommendations. Risk assessment for these cancers involves an evaluation of many factors. This review discusses an overview of hereditary breast and gynecological cancers and the process of a cancer genetic risk assessment. RECENT FINDINGS Risk assessment models for breast cancer should be used with caution, especially in populations in which they are not validated. Additionally, the BRCAPRO model may underestimate the likelihood of BRCA mutations in certain populations.The utilization of next-generation sequencing panels is increasing. Benefits and limitations of panel testing are described in the literature. There are currently no guidelines for the use of panel testing; however, some reports of institutional experiences and recommendations are available. SUMMARY Cancer genetic risk assessment is complex, and models developed to estimate risk may not apply to all populations. Identifying genetic factors related to cancer risk is also becoming increasingly complex with the clinical implementation of panel testing. This testing approach should be critically evaluated by healthcare providers. Further research is needed to create evidence-based guidelines for panel testing and management recommendations for moderately penetrant genes.
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12
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Alés-Martínez JE, Ruiz A, Chacón JI, Lluch Hernández A, Ramos M, Córdoba O, Aguirre E, Barnadas A, Jara C, González S, Plazaola A, Florián J, Andrés R, Sánchez Rovira P, Frau A. Preventive treatments for breast cancer: recent developments. Clin Transl Oncol 2014; 17:257-63. [PMID: 25445174 PMCID: PMC4357652 DOI: 10.1007/s12094-014-1250-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Accepted: 10/17/2014] [Indexed: 11/15/2022]
Abstract
Breast cancer is a burden for western societies, and an increasing one in emerging economies, because of its high incidence and enormous psychological, social, sanitary and economic costs. However, breast cancer is a preventable disease in a significant proportion. Recent developments in the armamentarium of effective drugs for breast cancer prevention (namely exemestane and anastrozole), the new recommendation from the National Institute for Health and Care Excellence to use preventative drugs in women at high risk as well as updated Guidelines from the US Preventive Services Task Force and the American Society of Clinical Oncology should give renewed momentum to the pharmacological prevention of breast cancer. In this article we review recent major developments in the field and examine their ongoing repercussion for breast cancer prevention. As a practical example, the potential impact of preventive measures in Spain is evaluated and a course of practical actions is delineated.
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Affiliation(s)
- J E Alés-Martínez
- Hospital Nuestra Señora de Sonsoles, GEICAM Spanish Breast Cancer Group, Ávila, Spain,
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13
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Arrospide A, Forné C, Rué M, Torà N, Mar J, Baré M. An assessment of existing models for individualized breast cancer risk estimation in a screening program in Spain. BMC Cancer 2013; 13:587. [PMID: 24321553 PMCID: PMC4029404 DOI: 10.1186/1471-2407-13-587] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Accepted: 12/03/2013] [Indexed: 11/11/2022] Open
Abstract
Background The aim of this study was to evaluate the calibration and discriminatory power of three predictive models of breast cancer risk. Methods We included 13,760 women who were first-time participants in the Sabadell-Cerdanyola Breast Cancer Screening Program, in Catalonia, Spain. Projections of risk were obtained at three and five years for invasive cancer using the Gail, Chen and Barlow models. Incidence and mortality data were obtained from the Catalan registries. The calibration and discrimination of the models were assessed using the Hosmer-Lemeshow C statistic, the area under the receiver operating characteristic curve (AUC) and the Harrell’s C statistic. Results The Gail and Chen models showed good calibration while the Barlow model overestimated the number of cases: the ratio between estimated and observed values at 5 years ranged from 0.86 to 1.55 for the first two models and from 1.82 to 3.44 for the Barlow model. The 5-year projection for the Chen and Barlow models had the highest discrimination, with an AUC around 0.58. The Harrell’s C statistic showed very similar values in the 5-year projection for each of the models. Although they passed the calibration test, the Gail and Chen models overestimated the number of cases in some breast density categories. Conclusions These models cannot be used as a measure of individual risk in early detection programs to customize screening strategies. The inclusion of longitudinal measures of breast density or other risk factors in joint models of survival and longitudinal data may be a step towards personalized early detection of BC.
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Affiliation(s)
| | | | | | | | | | - Marisa Baré
- Health Services Research Network in Chronic Diseases (REDISSEC), Spain.
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14
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Eadie L, Enfield L, Taylor P, Michell M, Gibson A. Breast cancer risk scores in a standard screening population. BREAST CANCER MANAGEMENT 2013. [DOI: 10.2217/bmt.13.52] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
SUMMARY Aim: Information regarding the characteristics and breast cancer risk factors of British women in the standard population attending breast cancer screening is limited. Such information could be useful in personalizing screening and care, and informing and educating women about their risk. Materials & methods: Information about various breast cancer risk factors was obtained from 355 women aged between 46 and 74 years at a UK inner-city breast cancer screening clinic using questionnaires. The risk of breast cancer was calculated using the modified Gail model and analyzed using descriptive and regression statistics. Results: There were 26 women recalled for further assessment and two cases confirmed as invasive breast cancer. Forty-seven women reported first-degree relatives with breast cancer. A total of 58% of our sample was overweight or obese, although 84% reported meeting the recommended target of ≥150 min of exercise per week. A total of 44% were smokers and 23% reported consuming alcohol on a regular basis. The mean lifetime risk score was 9.0% and the mean 5-year risk score was 1.5%. Various non-Gail model risk factors were found to be correlated with risk scores, but the only factor that was significantly different between women recalled for further assessment and those who were not was age of menarche. Conclusion: The results suggest that determining risk factor data in a standard screening population could be useful both to the women, who may have modifiable lifestyle factors that can be changed to improve their risk, and to the clinics, which can identify women at a higher risk who may be unaware and not present themselves as candidates for risk assessment.
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Affiliation(s)
- Leila Eadie
- Centre for Rural Health, Aberdeen University, Aberdeen, UK
| | - Louise Enfield
- Department of Medical Physics & Bioengineering, University College London, London, WC1E 6BT, UK
| | - Paul Taylor
- Centre for Health Informatics & Multiprofessional Education, University College London, London, N19 5LW, UK
| | - Michael Michell
- South East London Breast Screening Programme, Breast Radiology, King’s College Hospital London, SE5 9RS, UK
| | - Adam Gibson
- Department of Medical Physics & Bioengineering, University College London, London, WC1E 6BT, UK
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