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  • 1
    In: Journal of the American Geriatrics Society, Wiley, Vol. 65, No. 3 ( 2017-03), p. 619-624
    Abstract: Evidence suggests vitamin D deficiency is associated with developing frailty. However, cardiometabolic factors are related to both conditions and may confound and/or mediate the vitamin D–frailty association. We aimed to determine the association of vitamin D concentration with incidence of frailty, and the role of cardiometabolic diseases (cardiovascular disease, diabetes, hyperlipidemia, hypertension) in this relationship. Design Prospective longitudinal cohort study (7 visits from 1994–2008). Setting Baltimore, Maryland. Participants Three hundred sixty‐nine women from the Women's Health and Aging Study II aged 70–79 years, free of frailty at baseline. Measurements Serum circulating 25‐hydroxyvitamin D (25[ OH ]D) concentration was assessed at baseline and categorized as: 〈 10; 10–19.9; 20‐29.9; and ≥30 ng/ mL . Frailty incidence was determined based on presence of three or more criteria: weight loss, low physical activity, exhaustion, weakness, and slowness. Cardiometabolic diseases were ascertained at baseline. Analyses included Cox regression models adjusted for key covariates. Results Incidence rate of frailty was 32.2 per 1,000 person‐years in participants with 25( OH )D  〈  10 ng/ mL , compared to 12.9 per 1,000 person‐years in those with 25( OH )D ≥ 30 ng/ mL (mean follow‐up = 8.5 ± 3.7 years). In cumulative incidence analyses, those with lower 25( OH )D exhibited higher frailty incidence, though differences were non‐significant ( P  = .057). In regression models adjusted for demographics, smoking, and season, 25( OH )D  〈  10 ng/ mL (vs ≥30 ng/ mL ) was associated with nearly three‐times greater frailty incidence (hazard ratio ( HR ) = 2.77, 95% CI  = 1.14, 6.71, P  = .02). After adjusting for BMI , the relationship of 25( OH )D  〈  10 ng/ mL (vs ≥30 ng/ mL ) with incident frailty persisted, but was attenuated after further accounting for cardiometabolic diseases ( HR  = 2.29, 95% CI  = 0.92, 5.69, P  = .07). Conclusion Low serum vitamin D concentration is associated with incident frailty in older women; interestingly, the relationship is no longer significant after accounting for the presence of cardiometabolic diseases. Future studies should explore mechanisms to explain this relationship.
    Type of Medium: Online Resource
    ISSN: 0002-8614 , 1532-5415
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2017
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  • 2
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2017
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 26, No. 5_Supplement ( 2017-05-01), p. PR02-PR02
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 26, No. 5_Supplement ( 2017-05-01), p. PR02-PR02
    Abstract: Background: Adding genetic and other biomarkers to breast cancer risk prediction models could markedly improve model discrimination; however, these expanded models have not been validated in a range of populations. In particular, the calibration of these new models how well the predicted absolute risks match observed risks has not been established. Good calibration is essential to confirm the utility of these risk models in precision prevention and treatment programs. Large cohort studies provide an ideal setting to validate risk models, as they can be used to validate both relative and absolute risks. However, in practice, genetic and biomarker data are often not available in the full cohort, but only on a sub sample of cases and controls. When the rules for sampling cases and controls into the sub sample are known, inverse-probability-of-sampling (IPW) weights can be used to estimate empirical absolute risks. When the sampling rules are unknown or complicated, the IPW weights can be estimated by regressing selection into the sub sample on matching and other inclusion criteria. Methods: We evaluated the performance of recently published breast cancer risk prediction models [Maas et al. JAMA Oncol 2016] in the Nurses Health Study (NHS) and Nurses Health Study II (NHSII). We first assess a prediction model that only includes questionnaire data (BMI, hormone replacement therapy (HRT), alcohol consumption, smoking status, height, parity, age at menarche and menopause, age at first birth, and family history of breast cancer). These data are available on all subjects in the NHS and NHSII blood subcohorts: 32,826 women in NHS (with disease follow-up from 1990-2012) and 29,611 women in NHS II (1999-2013). We will then validate a model that includes both questionnaire data and a polygenic risk score based on 92 established risk SNPs. Genetic data are available on case-control samples nested within the blood subcohorts: 2308 breast cancer cases and 3344 controls from NHS and 612 breast cancer cases and 933 controls from NHSII. We estimated IPW weights among controls using logistic regression in the blood subcohorts, with sampling as control being the outcome and the following predictors: age at baseline, menopausal status, HRT, length of HRT use for premenopausal women at baseline, and length of follow up time. We used the iCARE software package (Maas P, Chatterjee N, Wheeler W et al. 2015) to calculate predicted 5 and 10-year absolute risks of breast cancer based on the published models, empirical 5 and 10-year incidence across deciles of predicted risk, and Hosmer-Lemeshow goodness of fit and AUC statistics. Results: For the risk model without genetic information, predicted risks in the blood subcohorts ranged from 6.5/1,000 (1st decile) to 20.1/1,000 (10th decile) for NHS. Although empirical risks increased across deciles at approximately the same rate as predicted rates, empirical risks were higher than predicted (Hosmer-Lemeshow p & lt;10-10). Validation analyses in the NHS II blood subcohort are ongoing. Due to matching and selection on control status, the baseline distribution of questionnaire risk factors differed between the blood subcohorts and the controls from the nested case-control samples. The IPW-weighted distribution in controls closely matched the distribution in the full subcohorts, suggesting a well-calculated weight. We will present IPW-based validation of the risk model in the nested case-control samples (work in progress). Conclusions: These results confirm that breast cancer risk prediction models can discriminate between high-risk and low-risk women, but they also highlight that the accuracy of absolute risk estimates can vary across populations. Findings from this study can add insights into model improvement and model application. Moreover, the method of using IPW weights to approximate a full cohort analysis provides a potential solution for utilizing nested case-control studies in future validation analyses. This abstract is also being presented as Poster A05. Citation Format: Chi Gao, Parichoy Pal Choudhury, Paige Maas, Rulla Tamimi, Heather Eliassen, Nilanjan Chatterjee, Montserrat Garcia-Closas, Peter Kraft. Validation of breast cancer risk prediction model using Nurses Health and Nurse Health II Studies. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr PR02.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
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  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 2254-2254
    Abstract: Background: The majority of female lung cancer cases in Asia are never-smokers with distinct risk factor profiles. Given the high burden of disease in this population, there is an increasing need to improve the understanding of lung cancer. Current risk models for lung cancer focus on active smokers and individuals of European ancestry. Therefore, we developed statistical models by integrating genetic and environmental risk factors to estimate absolute and population attribute risk of lung cancer among never-smoking women in Asia. Methods: We built absolute risk models for lung cancer among never-smoking women using data from 71,300 women (760 incident cases) in the Shanghai Women’s Health Study (SWHS), a population-based prospective cohort study. Relative risks were estimated using a multivariable Cox regression model with questionnaire-based risk factors. To account for missing genetic data for some subjects, we simulated genotypes for 10 common single nucleotide polymorphisms (SNP) using information on minor allele frequencies (MAF) and odds ratio estimates from previous genome-wide association studies (GWAS), conditional on family history of lung cancer. We used the iCARE tool to build two models for predicting lifetime (40 years) and 6-year absolute risk of lung cancer using age-specific lung cancer incidence rates, age-specific competing mortality rates, and risk factor distribution with: 1) questionnaire-based risk factors only and 2) questionnaire and genetic data. We then used the full absolute risk model to estimate the population attributable risk (PAR) due to modifiable risk factors, namely coal use and exposure to environmental tobacco smoke (ETS). Results: The questionnaire-based only model included family history of lung cancer, coal use, exposure to ETS, and body mass index (BMI). The full model also included data on 10 lung cancer related SNPs from our previous GWAS and had a wider spread in distribution of absolute lifetime risk (median=2.41%; range=0.43-12.36) compared to the questionnaire-based only model (median=2.72%; range=1.93-4.87). We used the full model to estimate the PAR and found that 1.74% and 6.33% of lung cancer cases could be prevented if never-smoking women in Shanghai did not use coal and were not exposed to ETS, respectively. Furthermore, we found that the full model estimated that 2.5% of the study population had a 6-year absolute risk of lung cancer higher than 1.51%, which is the suggested risk threshold for screening by existing risk models. Conclusion: We built risk models for never-smoking Asian women and estimated the contribution of coal use and ETS to the burden of lung cancer in Shanghai. This initial work shows promise for expanding and validating risk models in this population with potential translational implications, such as providing insight to identifying high risk individuals that may be eligible for lung cancer screening and primary prevention efforts. Citation Format: Batel Blechter, Parichoy Pal Choudhury, Xiao-ou Shu, Wei Zheng, Qiuyin Cai, Gong Yang, Jason Y.Y. Wong, Bu-Tian Ji, Wei Hu, Anne Rositch, Nilanjan Chatterjee, Nathaniel Rothman, Qing Lan. Risk models for lung cancer in never-smoking women in Shanghai with implications for screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2254.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 4
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 962-962
    Abstract: Background: Prospective validation of breast cancer risk models integrating classical risk factors and genetic variants is required for risk-stratified prevention and screening strategies. The objective of this study was to validate a breast cancer risk model integrating classical risk factors and a 313-variant polygenic risk score (PRS) in multiple prospective cohort studies, and to project five-year risk of breast cancer in six different countries. Methods: The study population included 7,529 cases and 230,103 controls from 14 prospective cohort studies in Australia, Germany, the Netherlands, Sweden, UK, and USA. We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building, validation, and risk projection. Expected five-year risk of invasive or in situ breast cancer was compared to observed risk, overall and within deciles of expected risk using goodness of fit statistics. We evaluated calibration of the relative risk through meta-analysis across cohorts, and of the absolute risk within each cohort. Model discrimination was evaluated using the area under the curve (AUC), and percentages of women crossing risk thresholds. Projections of five-year risk distributions were estimated for women of European ancestry aged 50-70 years in the general populations of these six countries. Results: Analysis showed overall good calibration of the integrated iCARE-based model relative risk for both women younger than 50 years (χ2=14.9, P=0.09) and aged 50 years or older (χ2=14.6, P=0.10), with a small overestimation of risk for women in the highest decile of expected risk (RR = 3.5 expected vs 2.3 (95% CI 1.6 to 3.2) observed for women & lt;50 years; and 2.8 expected vs 2.3 (95% CI 2.0 to 2.7) observed for women 50+ years). The age-adjusted AUCs for the integrated model were 63.1 (95% CI 60.9 to 65.3) and 62.9 (95% CI 61.8 to 64.0), for the two age groups respectively. The calibration of absolute risk showed substantial variation across cohorts, particularly for the older group, but had no systematic bias. Model based projections in the general populations showed that compared to the population average, women in the 1st and 99th percentiles of the integrated risk score had relative risks 0.19 and 3.56 respectively. The proportion of women of European ancestry aged 50-70 years with a five-year risk greater than 3% (threshold for consideration of risk-lowering drugs by U.S. Preventive Services Task Force) ranged from 7.1% in Germany to 18.2% in the US, which corresponds to ~5.5 million women in the US. Conclusions: Five-year risk predictions from a model with classical risk factors and PRS are well calibrated and provide substantial risk stratification across multiple cohorts in six different countries. Further studies are needed to evaluate the clinical utility of the validated model for risk stratified screening and prevention of breast cancer. Citation Format: Parichoy Pal Choudhury, Amber Wilcox, Chi Gao, Brian Carter, Anika Husing, Mark Brook, Mikael Eriksson, Kara Martin, Chris Scott, Min Shi, Thomas Ahearn, Michael Jones, Nick Orr, Minouk Schoemaker, Kamila Czene, Jenny Chang-Claude, Jacques Simard, Doug Easton, Marjanka K. Schmidt, Dale Sandler, Clarice R. Weinberg, Celine Vachon, Roger Milne, Per Hall, Anthony Swerdlow, Rudolph Kaaks, Myrto Barrdahl, Mia Gaudet, Antonis Antoniou, Peter Kraft, Montserrat Garcia-Closas, Nilanjan Chatterjee. Validation of breast cancer risk model incorporating classical risk factors and polygenic risk scores in 14 prospective cohort studies in 6 countries [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 962.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
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  • 5
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2017
    In:  Cancer Epidemiology, Biomarkers & Prevention Vol. 26, No. 5_Supplement ( 2017-05-01), p. A05-A05
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 26, No. 5_Supplement ( 2017-05-01), p. A05-A05
    Abstract: This abstract is being presented as a short talk in the scientific program. A full abstract is printed in the Proffered Abstracts section (PR02) of the Conference Proceedings. Citation Format: Chi Gao, Parichoy Pal Choudhury, Paige Maas, Rulla Tamimi, Heather Eliassen, Nilanjan Chatterjee, Montserrat Garcia-Closas, Peter Kraft. Validation of breast cancer risk prediction model using Nurses Health and Nurse Health II Studies. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A05.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
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  • 6
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2020-07-03)
    Abstract: Genome-wide association studies (GWAS) have led to the identification of hundreds of susceptibility loci across cancers, but the impact of further studies remains uncertain. Here we analyse summary-level data from GWAS of European ancestry across fourteen cancer sites to estimate the number of common susceptibility variants (polygenicity) and underlying effect-size distribution. All cancers show a high degree of polygenicity, involving at a minimum of thousands of loci. We project that sample sizes required to explain 80% of GWAS heritability vary from 60,000 cases for testicular to over 1,000,000 cases for lung cancer. The maximum relative risk achievable for subjects at the 99th risk percentile of underlying polygenic risk scores (PRS), compared to average risk, ranges from 12 for testicular to 2.5 for ovarian cancer. We show that PRS have potential for risk stratification for cancers of breast, colon and prostate, but less so for others because of modest heritability and lower incidence.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2553671-0
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  • 7
    In: JNCI: Journal of the National Cancer Institute, Oxford University Press (OUP), Vol. 113, No. 3 ( 2021-03-01), p. 329-337
    Abstract: We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.
    Type of Medium: Online Resource
    ISSN: 0027-8874 , 1460-2105
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2992-0
    detail.hit.zdb_id: 1465951-7
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  • 8
    In: Cancer Epidemiology, Biomarkers & Prevention, American Association for Cancer Research (AACR), Vol. 31, No. 8 ( 2022-08-02), p. 1593-1601
    Abstract: Risk estimates for women carrying germline mutations in breast cancer susceptibility genes are mainly based on studies of European ancestry women. Methods: We investigated associations between pathogenic variants (PV) in 34 genes with breast cancer risk in 871 cases [307 estrogen receptor (ER)-positive, 321 ER-negative, and 243 ER-unknown] and 1,563 controls in the Ghana Breast Health Study (GBHS), and estimated lifetime risk for carriers. We compared results with those for European, Asian, and African American ancestry women. Results: The frequency of PV in GBHS for nine breast cancer genes was 8.38% in cases and 1.22% in controls. Relative risk estimates for overall breast cancer were: (OR, 13.70; 95% confidence interval (CI), 4.03–46.51) for BRCA1, (OR, 7.02; 95% CI, 3.17–15.54) for BRCA2, (OR, 17.25; 95% CI, 2.15–138.13) for PALB2, 5 cases and no controls carried TP53 PVs, and 2.10, (0.72–6.14) for moderate-risk genes combined (ATM, BARD1, CHEK2, RAD51C, RAD52D). These estimates were similar to those previously reported in other populations and were modified by ER status. No other genes evaluated had mutations associated at P & lt; 0.05 with overall risk. The estimated lifetime risks for mutation carriers in BRCA1, BRCA2, and PALB2 and moderate-risk genes were 18.4%, 9.8%, 22.4%, and 3.1%, respectively, markedly lower than in Western populations with higher baseline risks. Conclusions: We confirmed associations between PV and breast cancer risk in Ghanaian women and provide absolute risk estimates that could inform counseling in Ghana and other West African countries. Impact: These findings have direct relevance for breast cancer genetic counseling for women in West Africa.
    Type of Medium: Online Resource
    ISSN: 1055-9965 , 1538-7755
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
    detail.hit.zdb_id: 2036781-8
    detail.hit.zdb_id: 1153420-5
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  • 9
    In: American Journal of Epidemiology, Oxford University Press (OUP), Vol. 192, No. 6 ( 2023-06-02), p. 995-1005
    Abstract: Data sharing is essential for reproducibility of epidemiologic research, replication of findings, pooled analyses in consortia efforts, and maximizing study value to address multiple research questions. However, barriers related to confidentiality, costs, and incentives often limit the extent and speed of data sharing. Epidemiological practices that follow Findable, Accessible, Interoperable, Reusable (FAIR) principles can address these barriers by making data resources findable with the necessary metadata, accessible to authorized users, and interoperable with other data, to optimize the reuse of resources with appropriate credit to its creators. We provide an overview of these principles and describe approaches for implementation in epidemiology. Increasing degrees of FAIRness can be achieved by moving data and code from on-site locations to remote, accessible (“Cloud”) data servers, using machine-readable and nonproprietary files, and developing open-source code. Adoption of these practices will improve daily work and collaborative analyses and facilitate compliance with data sharing policies from funders and scientific journals. Achieving a high degree of FAIRness will require funding, training, organizational support, recognition, and incentives for sharing research resources, both data and code. However, these costs are outweighed by the benefits of making research more reproducible, impactful, and equitable by facilitating the reuse of precious research resources by the scientific community.
    Type of Medium: Online Resource
    ISSN: 0002-9262 , 1476-6256
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2030043-8
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  • 10
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 2256-2256
    Abstract: Introduction: Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. However, robust validation of such models in prospective cohort studies is needed to determine their accuracy in identifying women at different risk levels. Methods: We incorporated Breast Imaging and Reporting Data System (BI-RADS) breast density to a literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS). The Individualized Coherent Absolute Risk Estimator (iCARE) tool was used to build and validate a 5-year absolute risk model for breast cancer. The model was evaluated for calibration and discrimination in three prospective cohorts of women of European ancestry (1,468 cases, 19,104 controls): US-based Nurses’ Health Study (NHS I and II) and Mayo Mammography Health Study (MMHS); and Sweden-based Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Analyses were done separately for women younger (NHS II, KARMA) and older than 50 years (NHS I, MMHS, KARMA). Improvements in risk stratification were assessed among US non-Hispanic White women aged 50-70 years. Results: For women younger than 50 years, the model with questionnaire-based risk factors, PRS and BI-RADS was well calibrated across risk deciles in NHS II, but overestimated risk at the highest risk decile in KARMA. For women 50 years or older, the model showed good calibration in all studies, with evidence of slight overestimation at the highest risk decile and underestimation at the lowest risk decile. The model with PRS and BI-RADS was well calibrated for women at high-risk in both age groups. Incorporation of BI-RADS to questionnaire-based risk factors and PRS improved risk discrimination: area under the curves (AUC) 67.0% (95% CI: 63.5-70.6%) vs. 65.6% (95% CI: 61.9-69.3%) for models with and without BI-RADS for younger women and 66.1% (95% CI 64.4-67.8%) vs. 65.5% (95% CI: 63.8-67.2%) for older women. The model with BI-RADS identified 18.4% population of non-Hispanic US women 50-70 years old above 3% 5-year risk (used for recommending risk-reducing medication in the US), with 42.4% of future cases expected to occur in this group. Addition of BI-RADS led to the reclassification of ~8.0% of US non-Hispanic White women aged 50-70 years at the high-risk threshold (3% 5-year risk), resulting in identification of 4.2% of additional future cases. Conclusion: Integrating BI-RADS with questionnaire-based risk factors and PRS resulted in improved risk stratification among women of European ancestry, with evidence of slight overestimation of risk for women at elevated risk. Additional validation of the integrated model in diverse populations is needed prior to considering clinical applications. Citation Format: Charlotta V. Mulder, Yon Ho Jee, Xin Yang, Christopher G. Scott, Chi Gao, Amber N. Hurson, Mikael Eriksson, Per Hall, Peter Kraft, Celine M. Vachon, Antonis C. Antoniou, Gretchen Gierach, Montserrat Garcia-Closas, Parichoy Pal Choudhury. Validation of an integrated breast cancer risk model with mammographic density in three prospective cohort studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2256.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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    detail.hit.zdb_id: 410466-3
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