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Breast Cancer Risk Assessment Tools for Stratifying Women into Risk Groups: A Systematic Review. Cancers (Basel) 2023; 15:cancers15041124. [PMID: 36831466 PMCID: PMC9953796 DOI: 10.3390/cancers15041124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND The benefits and harms of breast screening may be better balanced through a risk-stratified approach. We conducted a systematic review assessing the accuracy of questionnaire-based risk assessment tools for this purpose. METHODS Population: asymptomatic women aged ≥40 years; Intervention: questionnaire-based risk assessment tool (incorporating breast density and polygenic risk where available); Comparison: different tool applied to the same population; Primary outcome: breast cancer incidence; Scope: external validation studies identified from databases including Medline and Embase (period 1 January 2008-20 July 2021). We assessed calibration (goodness-of-fit) between expected and observed cancers and compared observed cancer rates by risk group. Risk of bias was assessed with PROBAST. RESULTS Of 5124 records, 13 were included examining 11 tools across 15 cohorts. The Gail tool was most represented (n = 11), followed by Tyrer-Cuzick (n = 5), BRCAPRO and iCARE-Lit (n = 3). No tool was consistently well-calibrated across multiple studies and breast density or polygenic risk scores did not improve calibration. Most tools identified a risk group with higher rates of observed cancers, but few tools identified lower-risk groups across different settings. All tools demonstrated a high risk of bias. CONCLUSION Some risk tools can identify groups of women at higher or lower breast cancer risk, but this is highly dependent on the setting and population.
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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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Cintolo-Gonzalez JA, Braun D, Blackford AL, Mazzola E, Acar A, Plichta JK, Griffin M, Hughes KS. Breast cancer risk models: a comprehensive overview of existing models, validation, and clinical applications. Breast Cancer Res Treat 2017; 164:263-284. [DOI: 10.1007/s10549-017-4247-z] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 04/12/2017] [Indexed: 01/01/2023]
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Arrospide A, Rue M, van Ravesteyn NT, Comas M, Larrañaga N, Sarriugarte G, Mar J. Evaluation of health benefits and harms of the breast cancer screening programme in the Basque Country using discrete event simulation. BMC Cancer 2015; 15:671. [PMID: 26459293 PMCID: PMC4603694 DOI: 10.1186/s12885-015-1700-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Accepted: 10/07/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Since the breast cancer screening programme in the Basque Country (BCSPBC) was started in 1996, more than 400,000 women aged 50 to 69 years have been invited to participate. Based on epidemiological observations and simulation techniques it is possible to extend observed short term data into anticipated long term results. The aim of this study was to assess the effectiveness of the programme through 2011 by quantifying the outcomes in breast cancer mortality, life-years gained, false positive results, and overdiagnosis. METHODS A discrete event simulation model was constructed to reproduce the natural history of breast cancer (disease-free, pre-clinical, symptomatic, and disease-specific death) and the actual observed characteristics of the screening programme during the evaluated period in the Basque women population. Goodness-of-fit statistics were applied for model validation. The screening effects were measured as differences in benefits and harms between the screened and unscreened populations. Breast cancer mortality reduction and life-years gained were considered as screening benefits, whereas, overdiagnosis and false positive results were assessed as harms. Results for a single cohort were also obtained. RESULTS The screening programme yielded a 16 % reduction in breast cancer mortality and a 10 % increase in the incidence of breast cancer through 2011. Almost 2 % of all the women in the programme had a false positive result during the evaluation period. When a single cohort was analysed, the number of deaths decreased by 13 %, and 4 % of screen-detected cancers were overdiagnosed. Each woman with BC detected by the screening programme gained 2.5 life years due to early detection corrected by lead time. CONCLUSIONS Fifteen years after the screening programme started, this study supports an important decrease in breast cancer mortality due to the screening programme, with reasonable risk of overdiagnosis and false positive results, and sustains the continuation of the breast cancer screening programme in the Basque population.
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Affiliation(s)
- Arantzazu Arrospide
- Gipuzkoa AP-OSI Research Unit, Integrated Health Organization Alto Deba, Avda Navarra 16, 20500, Arrasate-Mondragón, Gipuzkoa, Spain.
- Public Health Division of Gipuzkoa, BIODONOSTIA Research Institute, Paseo Dr Beguiristain s/n, 20014, Donostia, Gipuzkoa, Spain.
- REDISSEC (Red de Investigación en Servicios de Salud en Enfermedades Crónicas - Spanish Health Services Research on Chronic Patients Network), Bilbao, Spain.
| | - Montserrat Rue
- REDISSEC (Red de Investigación en Servicios de Salud en Enfermedades Crónicas - Spanish Health Services Research on Chronic Patients Network), Bilbao, Spain.
- Basic Medical Sciences department, Biomedical Research Institute of Lleida, University of Lleida, Avda. Rovira Roure 80, 25198, Lleida, Spain.
| | - Nicolien T van Ravesteyn
- Department of Public Health, Erasmus University Medical Center Rotterdam, Dr Molewaterplein 50, 3015 GE, Rotterdam, The Netherlands.
| | - Merce Comas
- REDISSEC (Red de Investigación en Servicios de Salud en Enfermedades Crónicas - Spanish Health Services Research on Chronic Patients Network), Bilbao, Spain.
- Evaluation and Epidemiology Department, Hospital del Mar - IMIM (Hospital del Mar Medical Research Institute), Passeig Maritim 25-29, 08003, Barcelona, Spain.
| | - Nerea Larrañaga
- Public Health Division of Gipuzkoa, BIODONOSTIA Research Institute, Paseo Dr Beguiristain s/n, 20014, Donostia, Gipuzkoa, Spain.
- CIBER of Epidemiology and Public Heath, C/Monforte de Lemos 3-5, 28029, Madrid, Spain.
| | - Garbiñe Sarriugarte
- Breast Cancer Early Detection Programme, Public Health Division of Bizkaia, Basque Government, Alameda Rekalde 39, 48008, Bilbao, Bizkaia, Spain.
| | - Javier Mar
- Gipuzkoa AP-OSI Research Unit, Integrated Health Organization Alto Deba, Avda Navarra 16, 20500, Arrasate-Mondragón, Gipuzkoa, Spain.
- Public Health Division of Gipuzkoa, BIODONOSTIA Research Institute, Paseo Dr Beguiristain s/n, 20014, Donostia, Gipuzkoa, Spain.
- REDISSEC (Red de Investigación en Servicios de Salud en Enfermedades Crónicas - Spanish Health Services Research on Chronic Patients Network), Bilbao, Spain.
- Health Management Service, Integrated Health Organization Alto Deba, Avda Navarra 16, 20500, Arrasate-Mondragón, Gipuzkoa, Spain.
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Development of a risk assessment tool for projecting individualized probabilities of developing breast cancer for Chinese women. Tumour Biol 2014; 35:10861-9. [PMID: 25085581 DOI: 10.1007/s13277-014-1967-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Accepted: 04/11/2014] [Indexed: 01/13/2023] Open
Abstract
The optimal approach regarding breast cancer screening for Chinese women is unclear due to the relative low incidence rate. A risk assessment tool may be useful for selection of high-risk subsets of population for mammography screening in low-incidence and resource-limited developing country. The odd ratios for six main risk factors of breast cancer were pooled by review manager after a systematic research of literature. Health risk appraisal (HRA) model was developed to predict an individual's risk of developing breast cancer in the next 5 years from current age. The performance of this HRA model was assessed based on a first-round screening database. Estimated risk of breast cancer increased with age. Increases in the 5-year risk of developing breast cancer were found with the existence of any of included risk factors. When individuals who had risk above median risk (3.3‰) were selected from the validation database, the sensitivity is 60.0% and the specificity is 47.8%. The unweighted area under the curve (AUC) was 0.64 (95% CI = 0.50-0.78). The risk-prediction model reported in this article is based on a combination of risk factors and shows good overall predictive power, but it is still weak at predicting which particular women will develop the disease. It would be very helpful for the improvement of a current model if more population-based prospective follow-up studies were used for the validation.
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