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Ho PJ, Lim EH, Mohamed Ri NKB, Hartman M, Wong FY, Li J. Will Absolute Risk Estimation for Time to Next Screen Work for an Asian Mammography Screening Population? Cancers (Basel) 2023; 15:cancers15092559. [PMID: 37174025 PMCID: PMC10177032 DOI: 10.3390/cancers15092559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 04/26/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
Personalized breast cancer risk profiling has the potential to promote shared decision-making and improve compliance with routine screening. We assessed the Gail model's performance in predicting the short-term (2- and 5-year) and the long-term (10- and 15-year) absolute risks in 28,234 asymptomatic Asian women. Absolute risks were calculated using different relative risk estimates and Breast cancer incidence and mortality rates (White, Asian-American, or the Singapore Asian population). Using linear models, we tested the association of absolute risk and age at breast cancer occurrence. Model discrimination was moderate (AUC range: 0.580-0.628). Calibration was better for longer-term prediction horizons (E/Olong-term ranges: 0.86-1.71; E/Oshort-term ranges:1.24-3.36). Subgroup analyses show that the model underestimates risk in women with breast cancer family history, positive recall status, and prior breast biopsy, and overestimates risk in underweight women. The Gail model absolute risk does not predict the age of breast cancer occurrence. Breast cancer risk prediction tools performed better with population-specific parameters. Two-year absolute risk estimation is attractive for breast cancer screening programs, but the models tested are not suitable for identifying Asian women at increased risk within this short interval.
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Affiliation(s)
- Peh Joo Ho
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome, Singapore 138672, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
| | - Elaine Hsuen Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore
| | - Nur Khaliesah Binte Mohamed Ri
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore 117549, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
- Department of Surgery, University Surgical Cluster, National University Hospital, Singapore 119228, Singapore
| | - Fuh Yong Wong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore 168583, Singapore
| | - Jingmei Li
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), 60 Biopolis Street, Genome, Singapore 138672, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore
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2
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Ashburner JM, Wang X, Li X, Khurshid S, Ko D, Trisini Lipsanopoulos A, Lee PR, Carmichael T, Turner AC, Jackson C, Ellinor PT, Benjamin EJ, Atlas SJ, Singer DE, Trinquart L, Lubitz SA, Anderson CD. Re-CHARGE-AF: Recalibration of the CHARGE-AF Model for Atrial Fibrillation Risk Prediction in Patients With Acute Stroke. J Am Heart Assoc 2021; 10:e022363. [PMID: 34666503 PMCID: PMC8751842 DOI: 10.1161/jaha.121.022363] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background Performance of existing atrial fibrillation (AF) risk prediction models in poststroke populations is unclear. We evaluated predictive utility of an AF risk model in patients with acute stroke and assessed performance of a fully refitted model. Methods and Results Within an academic hospital, we included patients aged 46 to 94 years discharged for acute ischemic stroke between 2003 and 2018. We estimated 5‐year predicted probabilities of AF using the Cohorts for Heart and Aging Research in Genomic Epidemiology for Atrial Fibrillation (CHARGE‐AF) model, by recalibrating CHARGE‐AF to the baseline risk of the sample, and by fully refitting a Cox proportional hazards model to the stroke sample (Re‐CHARGE‐AF) model. We compared discrimination and calibration between models and used 200 bootstrap samples for optimism‐adjusted measures. Among 551 patients with acute stroke, there were 70 incident AF events over 5 years (cumulative incidence, 15.2%; 95% CI, 10.6%–19.5%). Median predicted 5‐year risk from CHARGE‐AF was 4.8% (quartile 1–quartile 3, 2.0–12.6) and from Re‐CHARGE‐AF was 16.1% (quartile 1–quartile 3, 8.0–26.2). For CHARGE‐AF, discrimination was moderate (C statistic, 0.64; 95% CI, 0.57–0.70) and calibration was poor, underestimating AF risk (Greenwood‐Nam D’Agostino chi‐square, P<0.001). Calibration with recalibrated baseline risk was also poor (Greenwood‐Nam D’Agostino chi‐square, P<0.001). Re‐CHARGE‐AF improved discrimination (P=0.001) compared with CHARGE‐AF (C statistic, 0.74 [95% CI, 0.68–0.79]; optimism‐adjusted, 0.70 [95% CI, 0.65–0.75]) and was well calibrated (Greenwood‐Nam D’Agostino chi‐square, P=0.97). Conclusions Covariates from an established AF risk model enable accurate estimation of AF risk in a poststroke population after recalibration. A fully refitted model was required to account for varying baseline AF hazard and strength of associations between covariates and incident AF.
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Affiliation(s)
- Jeffrey M Ashburner
- Division of General Internal Medicine Massachusetts General Hospital Boston MA.,Department of Medicine Harvard Medical School Boston MA
| | - Xin Wang
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | - Xinye Li
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | - Shaan Khurshid
- Cardiovascular Research Center Massachusetts General Hospital Boston MA.,Division of Cardiology Massachusetts General Hospital Boston MA
| | - Darae Ko
- Section of Cardiovascular Medicine Boston University School of Medicine Boston MA
| | | | - Priscilla R Lee
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | - Taylor Carmichael
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | - Ashby C Turner
- Department of Neurology Massachusetts General Hospital & Harvard Medical School Boston MA
| | | | - Patrick T Ellinor
- Cardiovascular Research Center Massachusetts General Hospital Boston MA.,Cardiac Arrhythmia Service Massachusetts General Hospital Boston MA
| | - Emelia J Benjamin
- Boston University and National HeartLung, and Blood Institute's Framingham Heart Study Framingham MA.,Department of Medicine Department of Epidemiology Sections of Preventive Medicine and Cardiovascular Medicine Boston University School of MedicineBoston University School of Public Heath Boston MA
| | - Steven J Atlas
- Division of General Internal Medicine Massachusetts General Hospital Boston MA.,Department of Medicine Harvard Medical School Boston MA
| | - Daniel E Singer
- Division of General Internal Medicine Massachusetts General Hospital Boston MA.,Department of Medicine Harvard Medical School Boston MA
| | - Ludovic Trinquart
- Boston University and National HeartLung, and Blood Institute's Framingham Heart Study Framingham MA.,Department of Biostatistics Boston University School of Public Health Boston MA
| | - Steven A Lubitz
- Cardiovascular Research Center Massachusetts General Hospital Boston MA.,Cardiac Arrhythmia Service Massachusetts General Hospital Boston MA
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3
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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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Bojanic K, Vukadin S, Grgic K, Malenica L, Sarcevic F, Smolic R, Kralik K, Včev A, Wu GY, Smolic M. The accuracy of breast cancer risk self-assessment does not correlate with knowledge about breast cancer and knowledge and attitudes towards primary chemoprevention. Prev Med Rep 2020; 20:101229. [PMID: 33145151 PMCID: PMC7593623 DOI: 10.1016/j.pmedr.2020.101229] [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: 05/16/2020] [Revised: 10/11/2020] [Accepted: 10/13/2020] [Indexed: 01/02/2023] Open
Abstract
The increase of breast cancer (BC) incidence has drawn attention to BC risk as means of reducing mortality and morbidity of the disease. The aim of this study was to determine the accuracy of BC risk perception, evaluate factors that affect risk perception and assess the correlation between BC risk perception and attitudes towards BC chemoprevention. A cross-sectional study included total of 258 women with average and high-risk for BC according to the Breast Cancer Risk Assessment Tool (BCRAT). All data were collected by face-to-face interview by three trained 6th year medical school students using a 54-item questionnaire. Each participant's actual BC risk was compared to a perceived risk and the accuracy of the BC risk self-assessment was determined. 72% of high-risk women underestimated their BC risk (p < 0.001). One third of subjects with a family history of BC have also underestimated their own risk (p = 0.002). Women who responded to screening mammography were more informed about BC risk factors (p = 0.001). General knowledge about BC chemoprevention was surprisingly low, regardless of the accuracy of BC risk self-assessment. High-risk women appear to be unrealistically optimistic, since there was a significant difference between the accuracy of self-perceived risk and the objective BC risk.
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Affiliation(s)
- Kristina Bojanic
- Department of Biophysics and Radiology, Faculty of Dental Medicine and Health Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia.,Department of Biophysics and Radiology, Faculty of Medicine Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia.,Department of Radiology, Health Center Osijek, Osijek 31000, Croatia
| | - Sonja Vukadin
- Department of Pharmacology and Biochemistry, Faculty of Dental Medicine and Health Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia.,Department of Pharmacology, Faculty of Medicine Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia
| | - Kaja Grgic
- Department of Pharmacology, Faculty of Medicine Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia
| | - Luka Malenica
- Department of Patophysiology, Physiology and Immunology, Faculty of Dental Medicine and Health Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia
| | - Filip Sarcevic
- Department of Pharmacology, Faculty of Medicine Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia
| | - Robert Smolic
- Department of Patophysiology, Physiology and Immunology, Faculty of Dental Medicine and Health Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia.,Department of Patophysiology, Faculty of Medicine Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia.,Department of Internal Medicine, University Hospital Osijek, Osijek 31000, Croatia
| | - Kristina Kralik
- Department of Medical Statistics and Medical Informatics, Faculty of Medicine Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia
| | - Aleksandar Včev
- Department of Patophysiology, Physiology and Immunology, Faculty of Dental Medicine and Health Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia.,Department of Patophysiology, Faculty of Medicine Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia.,Department of Internal Medicine, University Hospital Osijek, Osijek 31000, Croatia
| | - George Y Wu
- Department of Internal Medicine, Division of Gastrenterology/Hepatology, University of Connecticut Health Center, 263 Farmington Avenue, Farmington, CT 06032, USA
| | - Martina Smolic
- Department of Pharmacology and Biochemistry, Faculty of Dental Medicine and Health Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia.,Department of Pharmacology, Faculty of Medicine Osijek, J. J. Strossmayer University of Osijek, Osijek 31000, Croatia
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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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6
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Danladi CD, Serakinci N. Risk Prediction Model Development for Late On-Set Breast Cancer Screening in Low- and Middle-Income Societies: A Model Study for North Cyprus. Healthcare (Basel) 2020; 8:healthcare8030213. [PMID: 32708661 PMCID: PMC7551407 DOI: 10.3390/healthcare8030213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/04/2020] [Accepted: 07/06/2020] [Indexed: 12/24/2022] Open
Abstract
Background: Early detection of breast cancer alters the prognosis and tools that can predict the risk for breast cancer in women will have a significant impact on healthcare systems in low- and middle-income regions, such as North Cyprus. Objective: In this study, we developed a simple breast cancer risk model for the women of North Cyprus. Methods: Data from 655 women, consisting of 318 breast cancer cases and 337 hospital-based controls, was used to develop and internally validate the model, external validation was carried out using, 653 women consisting of 126 cases and 527 controls. Data were obtained from medical records and interviews after informed consent. Results: A model was derived that consisted of age ≥50 years and <50 years and the presence and absence of >1 first-degree relatives (FDR) with breast cancer. From internal and external validations the model’s AUCs were, 0.66 (95% CI = 0.62–0.70) and 0.69 (95% CI = 0.63–0.74) respectively. Conclusions: A unique model for risk prediction of breast cancer was developed to aid in identifying high-risk women from North Cyprus that can benefit from mammogram screening. Further study on a large scale that includes environmental risk factors is warranted.
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Affiliation(s)
- Ceasar Dubor Danladi
- Department of Medical Genetics, Institute of Health Sciences, Near East University, Nicosia 99138, Turkish Republic of North Cyprus;
| | - Nedime Serakinci
- Department of Medical Genetics, Faculty of Medicine, Near East University, Nicosia 99138, Turkish Republic of North Cyprus
- Department of Molecular Biology and Genetics, Faculty of Art and Sciences, Near East University, Nicosia 99138, Turkish Republic of North Cyprus
- Correspondence: ; Tel.: +90-392-675-1000
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7
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Genetic and Genomic Advances in Breast Cancer Diagnosis and Treatment. Nurs Womens Health 2019; 23:518-525. [PMID: 31669147 DOI: 10.1016/j.nwh.2019.09.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 06/18/2019] [Accepted: 09/01/2019] [Indexed: 02/06/2023]
Abstract
Advances in genetic testing for people at high risk for cancer and in targeted gene therapy for breast cancer are rapidly emerging, including newly developed key hormone receptor-targeted therapies and individualized molecular fusion identification and treatment options. These advances are contributing to a new era in cancer treatment modalities and care delivery. As more innovative and advanced treatment options emerge, women's health outcomes and survival rates may improve. Nursing professionals in primary care and women's health specialties must be aware of the latest options for testing, referrals, and treatment modalities.
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8
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Clendenen TV, Ge W, Koenig KL, Afanasyeva Y, Agnoli C, Brinton LA, Darvishian F, Dorgan JF, Eliassen AH, Falk RT, Hallmans G, Hankinson SE, Hoffman-Bolton J, Key TJ, Krogh V, Nichols HB, Sandler DP, Schoemaker MJ, Sluss PM, Sund M, Swerdlow AJ, Visvanathan K, Zeleniuch-Jacquotte A, Liu M. Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model. Breast Cancer Res 2019; 21:42. [PMID: 30890167 PMCID: PMC6425605 DOI: 10.1186/s13058-019-1126-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 03/05/2019] [Indexed: 12/28/2022] Open
Abstract
Background Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Müllerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35–50. Methods In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers. Results The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer. Conclusions AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35–50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history. Electronic supplementary material The online version of this article (10.1186/s13058-019-1126-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Tess V Clendenen
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Wenzhen Ge
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Karen L Koenig
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Yelena Afanasyeva
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA
| | - Claudia Agnoli
- Epidemiology and Prevention Unit, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan, Italy
| | - Louise A Brinton
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Farbod Darvishian
- Department of Pathology, New York University School of Medicine, New York, NY, USA.,Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA
| | - Joanne F Dorgan
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Roni T Falk
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Göran Hallmans
- Department of Biobank Research, Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Susan E Hankinson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA
| | - Judith Hoffman-Bolton
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Vittorio Krogh
- Epidemiology and Prevention Unit, Fondazione IRCCS - Istituto Nazionale dei Tumori, Milan, Italy
| | - Hazel B Nichols
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Minouk J Schoemaker
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.,Division of Breast Cancer Research, The Institute of Cancer Research, London, UK
| | - Patrick M Sluss
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Malin Sund
- Department of Surgery, Umeå University Hospital, Umeå, Sweden
| | - Anthony J Swerdlow
- Division of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.,Sidney Kimmel Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Anne Zeleniuch-Jacquotte
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA.,Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA
| | - Mengling Liu
- Department of Population Health, New York University School of Medicine, 650 First Avenue, New York, NY, 10016, USA. .,Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA.
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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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Schonberg MA, Li VW, Eliassen AH, Davis RB, LaCroix AZ, McCarthy EP, Rosner BA, Chlebowski RT, Hankinson SE, Marcantonio ER, Ngo LH. Accounting for individualized competing mortality risks in estimating postmenopausal breast cancer risk. Breast Cancer Res Treat 2016; 160:547-562. [PMID: 27770283 PMCID: PMC5093031 DOI: 10.1007/s10549-016-4020-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 10/13/2016] [Indexed: 12/12/2022]
Abstract
PURPOSE Accurate risk assessment is necessary for decision-making around breast cancer prevention. We aimed to develop a breast cancer prediction model for postmenopausal women that would take into account their individualized competing risk of non-breast cancer death. METHODS We included 73,066 women who completed the 2004 Nurses' Health Study (NHS) questionnaire (all ≥57 years) and followed participants until May 2014. We considered 17 breast cancer risk factors (health behaviors, demographics, family history, reproductive factors) and 7 risk factors for non-breast cancer death (comorbidities, functional dependency) and mammography use. We used competing risk regression to identify factors independently associated with breast cancer. We validated the final model by examining calibration (expected-to-observed ratio of breast cancer incidence, E/O) and discrimination (c-statistic) using 74,887 subjects from the Women's Health Initiative Extension Study (WHI-ES; all were ≥55 years and followed for 5 years). RESULTS Within 5 years, 1.8 % of NHS participants were diagnosed with breast cancer (vs. 2.0 % in WHI-ES, p = 0.02), and 6.6 % experienced non-breast cancer death (vs. 5.2 % in WHI-ES, p < 0.001). Using a model selection procedure which incorporated the Akaike Information Criterion, c-statistic, statistical significance, and clinical judgement, our final model included 9 breast cancer risk factors, 5 comorbidities, functional dependency, and mammography use. The model's c-statistic was 0.61 (95 % CI [0.60-0.63]) in NHS and 0.57 (0.55-0.58) in WHI-ES. On average, our model under predicted breast cancer in WHI-ES (E/O 0.92 [0.88-0.97]). CONCLUSIONS We developed a novel prediction model that factors in postmenopausal women's individualized competing risks of non-breast cancer death when estimating breast cancer risk.
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Affiliation(s)
- Mara A Schonberg
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Beth Israel Deaconess Medical Center, 1309 Beacon, Office 219, Brookline, MA, 02446, USA.
| | - Vicky W Li
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - A Heather Eliassen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Roger B Davis
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Andrea Z LaCroix
- Division of Epidemiology, Family Medicine and Public Health, University of California San Diego, La Jolla, CA, USA
| | - Ellen P McCarthy
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Bernard A Rosner
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Rowan T Chlebowski
- Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan E Hankinson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics and Epidemiology, University of Massachusetts, 713 North Pleasant Street, Amherst, MA, USA
| | - Edward R Marcantonio
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Long H Ngo
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
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Duggan MA, Anderson WF, Altekruse S, Penberthy L, Sherman ME. The Surveillance, Epidemiology, and End Results (SEER) Program and Pathology: Toward Strengthening the Critical Relationship. Am J Surg Pathol 2016; 40:e94-e102. [PMID: 27740970 PMCID: PMC5106320 DOI: 10.1097/pas.0000000000000749] [Citation(s) in RCA: 279] [Impact Index Per Article: 34.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute collects data on cancer diagnoses, treatment, and survival for approximately 30% of the United States (US) population. To reflect advances in research and oncology practice, approaches to cancer control are evolving from simply enumerating the development of cancers by organ site in populations to including monitoring of cancer occurrence by histopathologic and molecular subtype, as defined by driver mutations and other alterations. SEER is an important population-based resource for understanding the implications of pathology diagnoses across demographic groups, geographic regions, and time and provides unique insights into the practice of oncology in the US that are not attainable from other sources. It provides incidence, survival, and mortality data for histopathologic cancer subtypes, and data by molecular subtyping are expanding. The program is developing systems to capture additional biomarker data, results from special populations, and expand biospecimen banking to enable cutting-edge cancer research and oncology practice. Pathology has always been central and critical to the effectiveness of SEER, and strengthening this relationship in this modern era of cancer diagnosis could be mutually beneficial. Achieving this goal requires close interactions between pathologists and the SEER program. This review provides a brief overview of SEER, focuses on facets relevant to pathology practice and research, and highlights the opportunities and challenges for pathologists to benefit from and enhance the value of SEER data.
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Affiliation(s)
- Máire A. Duggan
- Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - William F. Anderson
- Divisions of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Sean Altekruse
- Cancer Control and Populations Sciences, National Cancer Institute, Bethesda, Maryland, USA
| | - Lynne Penberthy
- Cancer Control and Populations Sciences, National Cancer Institute, Bethesda, Maryland, USA
| | - Mark E. Sherman
- Cancer Prevention, National Cancer Institute, Bethesda, Maryland, USA
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12
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Schonberg MA, Li VW, Eliassen AH, Davis RB, LaCroix AZ, McCarthy EP, Rosner BA, Chlebowski RT, Rohan TE, Hankinson SE, Marcantonio ER, Ngo LH. Performance of the Breast Cancer Risk Assessment Tool Among Women Age 75 Years and Older. J Natl Cancer Inst 2016; 108:djv348. [PMID: 26625899 PMCID: PMC5072372 DOI: 10.1093/jnci/djv348] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Revised: 06/17/2015] [Accepted: 10/20/2015] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The Breast Cancer Risk Assessment Tool (BCRAT, "Gail model") is commonly used for breast cancer prediction; however, it has not been validated for women age 75 years and older. METHODS We used Nurses' Health Study (NHS) data beginning in 2004 and Women's Health Initiative (WHI) data beginning in 2005 to compare BCRAT's performance among women age 75 years and older with that in women age 55 to 74 years in predicting five-year breast cancer incidence. BCRAT risk factors include: age, race/ethnicity, age at menarche, age at first birth, family history, history of benign breast biopsy, and atypia. We examined BCRAT's calibration by age by comparing expected/observed (E/O) ratios of breast cancer incidence. We examined discrimination by computing c-statistics for the model by age. All statistical tests were two-sided. RESULTS Seventy-three thousand seventy-two NHS and 97 081 WHI women participated. NHS participants were more likely to be non-Hispanic white (96.2% vs 84.7% in WHI, P < .001) and were less likely to develop breast cancer (1.8% vs 2.0%, P = .02). E/O ratios by age in NHS were 1.16 (95% confidence interval [CI] = 1.09 to 1.23, age 57-74 years) and 1.31 (95% CI = 1.18 to 1.45, age ≥ 75 years, P = .02), and in WHI 1.03 (95% CI = 0.97 to 1.09, age 55-74 years) and 1.10 (95% CI = 1.00 to 1.21, age ≥ 75 years, P = .21). E/O ratio 95% confidence intervals crossed one among women age 75 years and older when samples were limited to women who underwent mammography and were without significant illness. C-statistics ranged between 0.56 and 0.58 in both cohorts regardless of age. CONCLUSIONS BCRAT accurately predicted breast cancer for women age 75 years and older who underwent mammography and were without significant illness but had modest discrimination. Models that consider individual competing risks of non-breast cancer death may improve breast cancer risk prediction for older women.
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Affiliation(s)
- Mara A Schonberg
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - Vicky W Li
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - A Heather Eliassen
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - Roger B Davis
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - Andrea Z LaCroix
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - Ellen P McCarthy
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - Bernard A Rosner
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - Rowan T Chlebowski
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - Thomas E Rohan
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - Susan E Hankinson
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - Edward R Marcantonio
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
| | - Long H Ngo
- Affiliations of authors:Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center , Boston, MA (MAS, VWL, RBD, EPM, ERM, LHN); Department of Epidemiology, Harvard School of Public Health , Boston, MA and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health , Boston, MA (AHE, BAR, SEH); Division of Epidemiology, Family and Preventive Medicine, University of California San Diego , La Jolla, CA (AZL); Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center , Torrance, CA (RTC) ; Department of Epidemiology & Population Health, Albert Einstein College of Medicine , Bronx, NY (TER); Department of Biostatistics and Epidemiology, University of Massachusetts , Amherst, MA (SEH)
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Park Y. Predicting Cancer Risk: Practical Considerations in Developing and Validating a Cancer Risk Prediction Model. CURR EPIDEMIOL REP 2015. [DOI: 10.1007/s40471-015-0048-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Anothaisintawee T, Teerawattananon Y, Wiratkapun C, Srinakarin J, Woodtichartpreecha P, Hirunpat S, Wongwaisayawan S, Lertsithichai P, Kasamesup V, Thakkinstian A. Development and validation of a breast cancer risk prediction model for Thai women: a cross-sectional study. Asian Pac J Cancer Prev 2015; 15:6811-7. [PMID: 25169530 DOI: 10.7314/apjcp.2014.15.16.6811] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Breast cancer risk prediction models are widely used in clinical practice. They should be useful in identifying high risk women for screening in limited-resource countries. However, previous models showed poor performance in derived and validated settings. Therefore, we aimed to develop and validate a breast cancer risk prediction model for Thai women. MATERIALS AND METHODS This cross-sectional study consisted of derived and validation phases. Data collected at Ramathibodi and other two hospitals were used for deriving and externally validating models, respectively. Multiple logistic regression was applied to construct the model. Calibration and discrimination performances were assessed using the observed/expected ratio and concordance statistic (C-statistic), respectively. A bootstrap with 200 repetitions was applied for internal validation. RESULTS Age, menopausal status, body mass index, and use of oral contraceptives were significantly associated with breast cancer and were included in the model. Observed/expected ratio and C-statistic were 1.00 (95% CI: 0.82, 1.21) and 0.651 (95% CI: 0.595, 0.707), respectively. Internal validation showed good performance with a bias of 0.010 (95% CI: 0.002, 0.018) and C-statistic of 0.646(95% CI: 0.642, 0.650). The observed/expected ratio and C-statistic from external validation were 0.97 (95% CI: 0.68, 1.35) and 0.609 (95% CI: 0.511, 0.706), respectively. Risk scores were created and was stratified as low (0-0.86), low-intermediate (0.87-1.14), intermediate-high (1.15-1.52), and high-risk (1.53-3.40) groups. CONCLUSIONS A Thai breast cancer risk prediction model was created with good calibration and fair discrimination performance. Risk stratification should aid to prioritize high risk women to receive an organized breast cancer screening program in Thailand and other limited-resource countries.
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Affiliation(s)
- Thunyarat Anothaisintawee
- Department of Family Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand E-mail :
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Gail MH. Twenty-five years of breast cancer risk models and their applications. J Natl Cancer Inst 2015; 107:djv042. [PMID: 25722355 PMCID: PMC4651108 DOI: 10.1093/jnci/djv042] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2015] [Accepted: 02/05/2015] [Indexed: 11/14/2022] Open
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Min JW, Chang MC, Lee HK, Hur MH, Noh DY, Yoon JH, Jung Y, Yang JH. Validation of risk assessment models for predicting the incidence of breast cancer in korean women. J Breast Cancer 2014; 17:226-35. [PMID: 25320620 PMCID: PMC4197352 DOI: 10.4048/jbc.2014.17.3.226] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2013] [Accepted: 06/23/2014] [Indexed: 11/30/2022] Open
Abstract
Purpose The Gail model is one of the most widely used tools to assess the risk of breast cancer. However, it is known to overestimate breast cancer risk for Asian women. Here, we validate the Gail model and the Korean model using Korean data, and subsequently update and revalidate the Korean model using recent data. Methods We validated the modified Gail model (model 2), Asian American Gail model, and a previous Korean model using screening patient data collected between January 1999 and July 2004. The occurrence of breast cancer was confirmed by matching the resident registration number with data from the Korean Breast Cancer Registration Program. The expected-to-observed (E/O) ratio was used to validate the reliability of the program, and receiver operating characteristics curve analysis was used to evaluate the program's discriminatory power. There has been a rapid increase in the incidence of breast cancer in Korea, and we updated and revalidated the Korean model using incidence and mortality rate data from recent years. Results Among 40,229 patients who were included in the validation, 161 patients were confirmed to have developed breast cancer within 5 years of screening. The E/O ratios and 95% confidence intervals (CI) were 2.46 (2.10-2.87) for the modified Gail model and 1.29 (1.11-1.51) for the Asian American Gail model. The E/O ratio and 95% CI for the Korean model was 0.50 (0.43-0.59). For the updated Korean model, the E/O ratio and 95% CI were 0.85 (0.73-1.00). In the discriminatory power, the area under curve and 95% CI of the modified Gail model, Asian American Gail model, Korean model and updated Korean model were 0.547 (0.500-0.594), 0.543 (0.495-0.590), 0.509 (0.463-0.556), and 0.558 (0.511-0.605), respectively. Conclusion The updated Korean model shows a better performance than the other three models. It is hoped that this study can provide the basis for a clinical risk assessment program and a future prospective study of breast cancer prevention.
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Affiliation(s)
- Jun Won Min
- Department of Surgery, Dankook University College of Medicine, Cheonan, Korea
| | - Myung-Chul Chang
- Department of Surgery, Dankook University College of Medicine, Cheonan, Korea
| | - Hae Kyung Lee
- Department of Surgery, Cheil General Hospital and Women's Healthcare Center, Kwandong University College of Medicine, Seoul, Korea
| | - Min Hee Hur
- Department of Surgery, Cheil General Hospital and Women's Healthcare Center, Kwandong University College of Medicine, Seoul, Korea
| | - Dong-Young Noh
- Department of Surgery, Seoul National University College of Medicine, Seoul, Korea
| | - Jung Han Yoon
- Department of Surgery, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | - Yongsik Jung
- Department of Surgery, Ajou University School of Medicine, Suwon, Korea
| | - Jung-Hyun Yang
- Department of Surgery, Konkuk University School of Medicine, Seoul, Korea
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Howell A, Anderson AS, Clarke RB, Duffy SW, Evans DG, Garcia-Closas M, Gescher AJ, Key TJ, Saxton JM, Harvie MN. Risk determination and prevention of breast cancer. Breast Cancer Res 2014; 16:446. [PMID: 25467785 PMCID: PMC4303126 DOI: 10.1186/s13058-014-0446-2] [Citation(s) in RCA: 204] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is an increasing public health problem. Substantial advances have been made in the treatment of breast cancer, but the introduction of methods to predict women at elevated risk and prevent the disease has been less successful. Here, we summarize recent data on newer approaches to risk prediction, available approaches to prevention, how new approaches may be made, and the difficult problem of using what we already know to prevent breast cancer in populations. During 2012, the Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer to identify gaps in our knowledge. The risk-and-prevention panel involved in this exercise was asked to expand and update its report and review recent relevant peer-reviewed literature. The enlarged position paper presented here highlights the key gaps in risk-and-prevention research that were identified, together with recommendations for action. The panel estimated from the relevant literature that potentially 50% of breast cancer could be prevented in the subgroup of women at high and moderate risk of breast cancer by using current chemoprevention (tamoxifen, raloxifene, exemestane, and anastrozole) and that, in all women, lifestyle measures, including weight control, exercise, and moderating alcohol intake, could reduce breast cancer risk by about 30%. Risk may be estimated by standard models potentially with the addition of, for example, mammographic density and appropriate single-nucleotide polymorphisms. This review expands on four areas: (a) the prediction of breast cancer risk, (b) the evidence for the effectiveness of preventive therapy and lifestyle approaches to prevention, (c) how understanding the biology of the breast may lead to new targets for prevention, and (d) a summary of published guidelines for preventive approaches and measures required for their implementation. We hope that efforts to fill these and other gaps will lead to considerable advances in our efforts to predict risk and prevent breast cancer over the next 10 years.
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Affiliation(s)
- Anthony Howell
- Genesis Breast Cancer Prevention Centre, University Hospital of South Manchester, Southmoor Road, Wythenshawe, M29 9LT Manchester, UK
- The Christie, NHS Foundation Trust, Wilmslow Road, Manchester, M20 2QJ UK
- Breakthrough Breast Cancer Research Unit, Institute of Cancer Sciences, University of Manchester, Wilmslow Road, Manchester, M20 2QJ UK
| | - Annie S Anderson
- Centre for Public Health Nutrition Research, Division of Cancer Research, Level 7, University of Dundee, Ninewells Hospital & Medical School, Mailbox 7, George Pirie Way, Dundee, DD1 9SY UK
| | - Robert B Clarke
- Breakthrough Breast Cancer Research Unit, Institute of Cancer Sciences, University of Manchester, Wilmslow Road, Manchester, M20 2QJ UK
| | - Stephen W Duffy
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - D Gareth Evans
- Genesis Breast Cancer Prevention Centre, University Hospital of South Manchester, Southmoor Road, Wythenshawe, M29 9LT Manchester, UK
- The Christie, NHS Foundation Trust, Wilmslow Road, Manchester, M20 2QJ UK
- Manchester Centre for Genomic Medicine, The University of Manchester, Manchester Academic Health Science Centre, Central Manchester Foundation Trust, St. Mary’s Hospital, Oxford Road, Manchester, M13 9WL UK
| | - Montserat Garcia-Closas
- Division of Genetics and Epidemiology, Institute of Cancer Research, Cotswold Road, Sutton, SM2 5NG London, UK
| | - Andy J Gescher
- Department of Cancer Studies and Molecular Medicine, University of Leicester, University Road, Leicester, LE2 7LX UK
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Roosevelt Drive, Oxford, OX3 7LF UK
| | - John M Saxton
- School of Health Sciences, Faculty of Medicine and Health Sciences, University of East Anglia, University Drive, Norwich, NR4 7TJ UK
| | - Michelle N Harvie
- Genesis Breast Cancer Prevention Centre, University Hospital of South Manchester, Southmoor Road, Wythenshawe, M29 9LT Manchester, UK
- The Christie, NHS Foundation Trust, Wilmslow Road, Manchester, M20 2QJ UK
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Lee JY, Klimberg S, Bondurant KL, Phillips MM, Kadlubar SA. Cross-sectional study to assess the association of population density with predicted breast cancer risk. Breast J 2014; 20:615-21. [PMID: 25200109 DOI: 10.1111/tbj.12330] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The Gail and CARE models estimate breast cancer risk for white and African-American (AA) women, respectively. The aims of this study were to compare metropolitan and nonmetropolitan women with respect to predicted breast cancer risks based on known risk factors, and to determine if population density was an independent risk factor for breast cancer risk. A cross-sectional survey was completed by 15,582 women between 35 and 85 years of age with no history of breast cancer. Metropolitan and nonmetropolitan women were compared with respect to risk factors, and breast cancer risk estimates, using general linear models adjusted for age. For both white and AA women, tisk factors used to estimate breast cancer risk included age at menarche, history of breast biopsies, and family history. For white women, age at first childbirth was an additional risk factor. In comparison to their nonmetropolitan counterparts, metropolitan white women were more likely to report having a breast biopsy, have family history of breast cancer, and delay childbirth. Among white metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.44% and 1.32% (p < 0.001), and lifetime risks of breast cancer were 10.81% and 10.01% (p < 0.001), respectively. AA metropolitan residents were more likely than those from nonmetropolitan areas to have had a breast biopsy. Among AA metropolitan and nonmetropolitan women, mean estimated 5-year risks were 1.16% and 1.12% (p = 0.039) and lifetime risks were 8.94%, and 8.85% (p = 0.344). Metropolitan residence was associated with higher predicted breast cancer risks for white women. Among AA women, metropolitan residence was associated with a higher predicted breast cancer risk at 5 years, but not over a lifetime. Population density was not an independent risk factor for breast cancer.
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Affiliation(s)
- Jeannette Y Lee
- Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, Arkansas
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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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Ogce F, Ozkan S, Okcin F, Yaren A, Demiray G. Evaluation of quality of life of breast cancer patient next- of-kin in Turkey. Asian Pac J Cancer Prev 2013; 14:2771-6. [PMID: 23803030 DOI: 10.7314/apjcp.2013.14.5.2771] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Quality of life (QoL) issues are of importance in relatives of women with breast cancer (BC)as caregivers in neglecting their own needs due to care of a patient and also as women regarding the potential risk of themselves developing BC. The objectives in the present study were to compare the QoL of female relatives of women in treatment for breast cancer. To date, no study had examined multi-dimensional QoL in accompanying people as compared them into two groups of female relatives whose first degree and second degree. METHODS QoL of female relatives was assessed using the Quality of Life-Family Version (QOL-FV) scale. Relationships between socio-demographic characteristics and QoL scores were analyzed using the Mann-Whitney U, Kruskal Wallis and Crosstabs tests. RESULTS The mean age of the female relatives was 37.6 years, and nearly 48% had a university education. It was found that first degree relatives had worse QoL in all domains except physical well- being than second degree relatives. CONCLUSION This study showed that being female relatives of BC, especially first-degree, affect QoL negatively. Health care providers are of an important role in the stage of information related to genetic influence of BC.
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Affiliation(s)
- Filiz Ogce
- Ege University Faculty of Nursing, Bornova, Izmir, Turkey.
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21
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Boggs DA, Rosenberg L, Pencina MJ, Adams-Campbell LL, Palmer JR. Validation of a breast cancer risk prediction model developed for Black women. J Natl Cancer Inst 2013; 105:361-7. [PMID: 23411594 DOI: 10.1093/jnci/djt008] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND A breast cancer risk prediction model for black women, developed from data in the Women's Contraceptive and Reproductive Experiences (CARE) study, has been validated in women aged 50 years or older but not among younger women or for specific breast cancer subtypes. METHODS We assessed calibration and discrimination of the CARE model in the Black Women's Health Study (BWHS) with data from 45 942 women aged 30 to 69 years at baseline. RESULTS During a mean follow-up of 9.5 years, we identified 852 invasive breast cancers. The CARE model predicted 749.6 breast cancers, yielding an expected-to-observed (E/O) ratio of 0.88 (95% confidence interval [CI] = 0.82 to 0.94). The E/O ratio did not appreciably differ between women aged less than 50 years and those aged 50 years or older. The model underpredicted risk to the greatest degree among women aged 25 years or older at birth of first child (E/O = 0.71, 95% CI = 0.63 to 0.81); the model was well calibrated among women aged less than 25 years at birth of first child. The prevalence of later age at birth of first child was higher in the BWHS than in the CARE study, and breast cancer incidence was higher in the BWHS compared with national rates used in the CARE model. With respect to discriminatory accuracy, the concordance statistic was 0.57 (95% CI = 0.55 to 0.59) for breast cancer overall, 0.59 (95% CI = 0.57 to 0.61) for estrogen receptor (ER)-positive breast cancer, and 0.54 (95% CI = 0.50 to 0.57) for ER-negative breast cancer. CONCLUSIONS The CARE model underpredicted breast cancer risk in the BWHS, at least in part because of older age at first birth in this cohort, which led to higher breast cancer incidence rates. Our results suggest that inclusion of age at first birth may improve model performance. Discriminatory accuracy was modest and worse for ER-negative breast cancer.
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Affiliation(s)
- Deborah A Boggs
- Slone Epidemiology Center, 1010 Commonwealth Ave, Boston, MA 02215, USA.
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Pastor-Barriuso R, Ascunce N, Ederra M, Erdozáin N, Murillo A, Alés-Martínez JE, Pollán M. Recalibration of the Gail model for predicting invasive breast cancer risk in Spanish women: a population-based cohort study. Breast Cancer Res Treat 2013; 138:249-59. [PMID: 23378108 PMCID: PMC3586062 DOI: 10.1007/s10549-013-2428-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2012] [Accepted: 01/21/2013] [Indexed: 01/10/2023]
Abstract
The Gail model for predicting the absolute risk of invasive breast cancer has been validated extensively in US populations, but its performance in the international setting remains uncertain. We evaluated the predictive accuracy of the Gail model in 54,649 Spanish women aged 45-68 years who were free of breast cancer at the 1996-1998 baseline mammographic examination in the population-based Navarre Breast Cancer Screening Program. Incident cases of invasive breast cancer and competing deaths were ascertained until the end of 2005 (average follow-up of 7.7 years) through linkage with population-based cancer and mortality registries. The Gail model was tested for calibration and discrimination in its original form and after recalibration to the lower breast cancer incidence and risk factor prevalence in the study cohort, and compared through cross-validation with a Navarre model fully developed from this cohort. The original Gail model overpredicted significantly the 835 cases of invasive breast cancer observed in the cohort (ratio of expected to observed cases 1.46, 95 % CI 1.36-1.56). The recalibrated Gail model was well calibrated overall (expected-to-observed ratio 1.00, 95 % CI 0.94-1.07), but it tended to underestimate risk for women in low-risk quintiles and to overestimate risk in high-risk quintiles (P = 0.01). The Navarre model showed good cross-validated calibration overall (expected-to-observed ratio 0.98, 95 % CI 0.92-1.05) and in different cohort subsets. The Navarre and Gail models had modest cross-validated discrimination indexes of 0.542 (95 % CI 0.521-0.564) and 0.544 (95 % CI 0.523-0.565), respectively. Although the original Gail model cannot be applied directly to populations with different underlying rates of invasive breast cancer, it can readily be recalibrated to provide unbiased estimates of absolute risk in such populations. Nevertheless, its limited discrimination ability at the individual level highlights the need to develop extended models with additional strong risk factors.
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Affiliation(s)
- Roberto Pastor-Barriuso
- National Center for Epidemiology, Carlos III Institute of Health, Monforte de Lemos 5, 28029 Madrid, Spain.
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Gao F, Machin D, Chow KY, Sim YF, Duffy SW, Matchar DB, Goh CH, Chia KS. Assessing risk of breast cancer in an ethnically South-East Asia population (results of a multiple ethnic groups study). BMC Cancer 2012; 12:529. [PMID: 23164155 PMCID: PMC3529190 DOI: 10.1186/1471-2407-12-529] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2011] [Accepted: 11/08/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Gail and others developed a model (GAIL) using age-at-menarche, age-at-birth of first live child, number of previous benign breast biopsy examinations, and number of first-degree-relatives with breast cancer as well as baseline age-specific breast cancer risks for predicting the 5-year risk of invasive breast cancer for Caucasian women. However, the validity of the model for projecting risk in South-East Asian women is uncertain. We evaluated GAIL and attempted to improve its performance for Singapore women of Chinese, Malay and Indian origins. METHODS Data from the Singapore Breast Screening Programme (SBSP) are used. Motivated by lower breast cancer incidence in many Asian countries, we utilised race-specific invasive breast cancer and other cause mortality rates for Singapore women to produce GAIL-SBSP. By using risk factor information from a nested case-control study within SBSP, alternative models incorporating fewer then additional risk factors were determined. Their accuracy was assessed by comparing the expected cases (E) with the observed (O) by the ratio (E/O) and 95% confidence interval (CI) and the respective concordance statistics estimated. RESULTS From 28,883 women, GAIL-SBSP predicted 241.83 cases during the 5-year follow-up while 241 were reported (E/O=1.00, CI=0.88 to 1.14). Except for women who had two or more first-degree-relatives with breast cancer, satisfactory prediction was present in almost all risk categories. This agreement was reflected in Chinese and Malay, but not in Indian women. We also found that a simplified model (S-GAIL-SBSP) including only age-at-menarche, age-at-birth of first live child and number of first-degree-relatives performed similarly with associated concordance statistics of 0.5997. Taking account of body mass index and parity did not improve the calibration of S-GAIL-SBSP. CONCLUSIONS GAIL can be refined by using national race-specific invasive breast cancer rates and mortality rates for causes other than breast cancer. A revised model containing only three variables (S-GAIL-SBSP) provides a simpler approach for projecting absolute risk of invasive breast cancer in South-East Asia women. Nevertheless its role in counseling the individual women regarding their risk of breast cancer remains problematical and needs to be validated in independent data.
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Affiliation(s)
- Fei Gao
- Division of Clinical Trials and Epidemiological Sciences, National Cancer Centre Singapore, 11 Hospital Drive, Singapore 169610.
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Lifetime risk for cancer death by sex and smoking status: the lifetime risk pooling project. Cancer Causes Control 2012; 23:1729-37. [PMID: 22825072 DOI: 10.1007/s10552-012-9959-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2011] [Accepted: 04/06/2012] [Indexed: 01/11/2023]
Abstract
BACKGROUND Understanding how sex and tobacco exposure may modify lifetime risks for cancer mortality is important for effective communication of risk in targeted public health messages. OBJECTIVE To determine lifetime risk estimates for cancer death associated with sex and smoking status in the United States. METHODS A pooled cohort design using ten well-defined epidemiologic cohorts including middle-aged and older individuals was used to estimate the lifetime risk for cancer death at selected index ages, with death from non-cancer causes as the competing risk, by sex and smoking status. RESULTS There were a total of 11,317 cancer-related deaths. At age 45 years, the lifetime risk of cancer death for male smokers is 27.7 % (95 % CI 24.0-31.4 %) compared to 15.8 % (95 % CI 12.7-18.9 %) for male non-smokers. At age 45 years, the lifetime risk of cancer death for female smokers is 21.7 % (95 % CI 18.8-24.6 %) compared to 13.2 % (95 % CI 11.0-15.4 %) for female non-smokers. Remaining lifetime risk for cancer death declined with age, and men have a greater risk for cancer death compared to women. Adjustment for competing risk of death, particularly representing cardiovascular mortality, yielded a greater change in lifetime risk estimates for men and smokers compared to women and non-smokers. CONCLUSIONS At the population level, the lifetime risk for cancer death remains significantly higher for smokers compared to non-smokers, regardless of sex. These estimates may provide clinicians with useful information for counseling individual patients and highlight the need for continued public health efforts related to smoking cessation.
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Banegas MP, Gail MH, LaCroix A, Thompson B, Martinez ME, Wactawski-Wende J, John EM, Hubbell FA, Yasmeen S, Katki HA. Evaluating breast cancer risk projections for Hispanic women. Breast Cancer Res Treat 2011; 132:347-53. [PMID: 22147080 DOI: 10.1007/s10549-011-1900-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2011] [Accepted: 11/23/2011] [Indexed: 10/14/2022]
Abstract
For Hispanic women, the Breast Cancer Risk Assessment Tool (BCRAT; "Gail Model") combines 1990-1996 breast cancer incidence for Hispanic women with relative risks for breast cancer risk factors from non-Hispanic white (NHW) women. BCRAT risk projections have never been comprehensively evaluated for Hispanic women. We compared the relative risks and calibration of BCRAT risk projections for 6,353 Hispanic to 128,976 NHW postmenopausal participants aged 50 and older in the Women's Health Initiative (WHI). Calibration was assessed by the ratio of the number of breast cancers observed with that expected by the BCRAT (O/E). We re-evaluated calibration for an updated BCRAT that combined BCRAT relative risks with 1993-2007 breast cancer incidence that is contemporaneous with the WHI. Cox regression was used to estimate relative risks. Discriminatory accuracy was assessed using the concordance statistic (AUC). In the WHI Main Study, the BCRAT underestimated the number of breast cancers by 18% in both Hispanics (O/E = 1.18, P = 0.06) and NHWs (O/E = 1.18, P < 0.001). Updating the BCRAT improved calibration for Hispanic women (O/E = 1.08, P = 0.4) and NHW women (O/E = 0.98, P = 0.2). For Hispanic women, relative risks for number of breast biopsies (1.71 vs. 1.27, P = 0.03) and age at first birth (0.97 vs. 1.24, P = 0.02) differed between the WHI and BCRAT. The AUC was higher for Hispanic women than NHW women (0.63 vs. 0.58, P = 0.03). Updating the BCRAT with contemporaneous breast cancer incidence rates improved calibration in the WHI. The modest discriminatory accuracy of the BCRAT for Hispanic women might improve by using risk factor relative risks specific to Hispanic women.
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Affiliation(s)
- Matthew P Banegas
- School of Public Health, Department of Health Services, University of Washington, Box 357660, Seattle, WA 98195, USA.
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Risk prediction models of breast cancer: a systematic review of model performances. Breast Cancer Res Treat 2011; 133:1-10. [DOI: 10.1007/s10549-011-1853-z] [Citation(s) in RCA: 69] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2011] [Accepted: 10/25/2011] [Indexed: 10/15/2022]
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A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance. Breast Cancer Res Treat 2011; 132:365-77. [DOI: 10.1007/s10549-011-1818-2] [Citation(s) in RCA: 115] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2011] [Accepted: 10/01/2011] [Indexed: 12/21/2022]
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Waters EA, Klein WMP, Moser RP, Yu M, Waldron WR, McNeel TS, Freedman AN. Correlates of unrealistic risk beliefs in a nationally representative sample. J Behav Med 2011; 34:225-35. [PMID: 21110077 PMCID: PMC3088765 DOI: 10.1007/s10865-010-9303-7] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2010] [Accepted: 11/12/2010] [Indexed: 10/18/2022]
Abstract
Unrealistically optimistic or pessimistic risk perceptions may be associated with maladaptive health behaviors. This study characterized factors associated with unrealistic optimism (UO) and unrealistic pessimism (UP) about breast cancer. Data from the 2005 National Health Interview Survey were analyzed (N = 14,426 women). After accounting for objective risk status, many (43.8%) women displayed UO, 12.3% displayed UP, 34.5% had accurate risk perceptions (their perceived risk matched their calculated risk), and 9.5% indicated "don't know/no response." Multivariate multinomial logistic regression indicated that UO was associated with higher education and never smoking. UP was associated with lower education, lower income, being non-Hispanic Black, having ≥3 comorbidities, current smoking, and being overweight. UO was more likely to emerge in younger and older than in middle-aged individuals. UO and UP are associated with different demographic, health, and behavioral characteristics. Population segments that are already vulnerable to negative health outcomes displayed more UP than less vulnerable populations.
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Affiliation(s)
- Erika A Waters
- Department of Surgery (Prevention and Control), Washington University School of Medicine, Campus Box 8100, 660 S. Euclid, St. Louis, MO 63110, USA.
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