1
|
McCarthy AM, Ehsan S, Hughes KS, Lehman CD, Conant EF, Kontos D, Armstrong K, Chen J. Feasibility of risk assessment for breast cancer molecular subtypes. Breast Cancer Res Treat 2024:10.1007/s10549-024-07404-9. [PMID: 38916820 DOI: 10.1007/s10549-024-07404-9] [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: 04/03/2024] [Accepted: 06/09/2024] [Indexed: 06/26/2024]
Abstract
PURPOSE Few breast cancer risk assessment models account for the risk profiles of different tumor subtypes. This study evaluated whether a subtype-specific approach improves discrimination. METHODS Among 3389 women who had a screening mammogram and were later diagnosed with invasive breast cancer we performed multinomial logistic regression with tumor subtype as the outcome and known breast cancer risk factors as predictors. Tumor subtypes were defined by expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) based on immunohistochemistry. Discrimination was assessed with the area under the receiver operating curve (AUC). Absolute risk of each subtype was estimated by proportioning Gail absolute risk estimates by the predicted probabilities for each subtype. We then compared risk factor distributions for women in the highest deciles of risk for each subtype. RESULTS There were 3,073 ER/PR+ HER2 - , 340 ER/PR +HER2 + , 126 ER/PR-ER2+, and 300 triple-negative breast cancers (TNBC). Discrimination differed by subtype; ER/PR-HER2+ (AUC: 0.64, 95% CI 0.59, 0.69) and TNBC (AUC: 0.64, 95% CI 0.61, 0.68) had better discrimination than ER/PR+HER2+ (AUC: 0.61, 95% CI 0.58, 0.64). Compared to other subtypes, patients at high absolute risk of TNBC were younger, mostly Black, had no family history of breast cancer, and higher BMI. Those at high absolute risk of HER2+ cancers were younger and had lower BMI. CONCLUSION Our study provides proof of concept that stratifying risk prediction for breast cancer subtypes may enable identification of patients with unique profiles conferring increased risk for tumor subtypes.
Collapse
Affiliation(s)
- Anne Marie McCarthy
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Blockley Hall, Room 833, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
| | - Sarah Ehsan
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Blockley Hall, Room 833, 423 Guardian Drive, Philadelphia, PA, 19104, USA
| | - Kevin S Hughes
- Department of Surgery, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Constance D Lehman
- Massachusetts General Hospital, Boston, MA, USA
- Department of Radiology, Perelman School of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Despina Kontos
- Columbia University Irving Medical Center, New York, NY, USA
| | | | - Jinbo Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Blockley Hall, Room 833, 423 Guardian Drive, Philadelphia, PA, 19104, USA
| |
Collapse
|
2
|
Dunn MR, Metwally EM, Vohra S, Hyslop T, Henderson LM, Reeder-Hayes K, Thompson CA, Lafata JE, Troester MA, Butler EN. Understanding mechanisms of racial disparities in breast cancer: an assessment of screening and regular care in the Carolina Breast Cancer Study. Cancer Causes Control 2024; 35:825-837. [PMID: 38217760 PMCID: PMC11045315 DOI: 10.1007/s10552-023-01833-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/16/2023] [Indexed: 01/15/2024]
Abstract
PURPOSE Screening history influences stage at detection, but regular preventive care may also influence breast tumor diagnostic characteristics. Few studies have evaluated healthcare utilization (both screening and primary care) in racially diverse screening-eligible populations. METHODS This analysis included 2,058 women age 45-74 (49% Black) from the Carolina Breast Cancer Study, a population-based cohort of women diagnosed with invasive breast cancer between 2008 and 2013. Screening history (threshold 0.5 mammograms per year) and pre-diagnostic healthcare utilization (i.e. regular care, based on responses to "During the past ten years, who did you usually see when you were sick or needed advice about your health?") were assessed as binary exposures. The relationship between healthcare utilization and tumor characteristics were evaluated overall and race-stratified. RESULTS Among those lacking screening, Black participants had larger tumors (5 + cm) (frequency 19.6% vs 11.5%, relative frequency difference (RFD) = 8.1%, 95% CI 2.8-13.5), but race differences were attenuated among screening-adherent participants (10.2% vs 7.0%, RFD = 3.2%, 0.2-6.2). Similar trends were observed for tumor stage and mode of detection (mammogram vs lump). Among all participants, those lacking both screening and regular care had larger tumors (21% vs 8%, RR = 2.51, 1.76-3.56) and advanced (3B +) stage (19% vs 6%, RR = 3.15, 2.15-4.63) compared to the referent category (screening-adherent and regular care). Under-use of regular care and screening was more prevalent in socioeconomically disadvantaged areas of North Carolina. CONCLUSIONS Access to regular care is an important safeguard for earlier detection. Our data suggest that health equity interventions should prioritize both primary care and screening.
Collapse
Affiliation(s)
- Matthew R Dunn
- Department of Epidemiology, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
| | - Eman M Metwally
- Department of Epidemiology, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| | - Sanah Vohra
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
- David Geffen School of Medicine, University of California, Los Angeles, USA
| | - Terry Hyslop
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Louise M Henderson
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
- Division of Pulmonary Disease and Critical Care Medicine, Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Katherine Reeder-Hayes
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
- Division of Oncology, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Caroline A Thompson
- Department of Epidemiology, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| | - Jennifer Elston Lafata
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
- Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
| | - Melissa A Troester
- Department of Epidemiology, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA.
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
- Department of Pathology and Laboratory Medicine, University of North Carolina, Chapel Hill, NC, USA.
| | - Eboneé N Butler
- Department of Epidemiology, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| |
Collapse
|
3
|
Pleasant V. A Public Health Emergency: Breast Cancer Among Black Communities in the United States. Obstet Gynecol Clin North Am 2024; 51:69-103. [PMID: 38267132 DOI: 10.1016/j.ogc.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
While Black people have a similar incidence of breast cancer compared to White people, they have a 40% increased death rate. Black people are more likely to be diagnosed with aggressive subtypes such as triple-negative breast cancer. However, despite biological factors, systemic racism and social determinants of health create delays in care and barriers to treatment. While genetic testing holds incredible promise for Black people, uptake remains low and results may be challenging to interpret. There is a need for more robust, multidisciplinary, and antiracist interventions to reverse breast cancer-related racial disparities.
Collapse
Affiliation(s)
- Versha Pleasant
- Department of Obstetrics and Gynecology, Cancer Genetics & Breast Health Clinic, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA.
| |
Collapse
|
4
|
Olufosoye O, Soler R, Babagbemi K. Disparities in genetic testing for breast cancer among black and Hispanic women in the United States. Clin Imaging 2024; 107:110066. [PMID: 38228024 DOI: 10.1016/j.clinimag.2023.110066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/03/2023] [Accepted: 12/06/2023] [Indexed: 01/18/2024]
Abstract
Women from racial and ethnic minorities are at a higher risk for developing breast cancer. Despite significant advancements in breast cancer screening, treatment, and overall survival rates, disparities persist among Black and Hispanic women. These disparities manifest as breast cancer at an earlier age with worse prognosis, lower rates of genetic screening, higher rates of advanced-stage diagnosis, and higher rates of breast cancer mortality compared to Caucasian women. The underutilization of available resources, such as genetic testing, counseling, and risk assessment tools, by Black and Hispanic women is one of many reasons contributing to these disparities. This review aims to explore the racial disparities that exist in genetic testing among Black and Hispanic women. Barriers that contribute to racial disparities include limited access to resources, insufficient knowledge and awareness, inconsistent care management, and slow progression of incorporation of genetic data and information from women of racial/ethnic minorities into risk assessment models and genetic databases. These barriers continue to impede rates of genetic testing and counseling among Black and Hispanic mothers. Consequently, it is imperative to address these barriers to promote early risk assessment, genetic testing and counseling, early detection rates, and ultimately, lower mortality rates among women belonging to racial and ethnic minorities.
Collapse
Affiliation(s)
- Oludamilola Olufosoye
- Central Michigan University, College of Medicine, Mount Pleasant, MI 48858, United States of America.
| | - Roxana Soler
- Nova Southeastern University, College of Allopathic Medicine, Ft Lauderdale, FL 33328, United States of America
| | - Kemi Babagbemi
- Division of Radiology, Weill Cornell Medicine, New York, NY 10065, United States of America
| |
Collapse
|
5
|
Wilkerson AD, Gentle CK, Ortega C, Al-Hilli Z. Disparities in Breast Cancer Care-How Factors Related to Prevention, Diagnosis, and Treatment Drive Inequity. Healthcare (Basel) 2024; 12:462. [PMID: 38391837 PMCID: PMC10887556 DOI: 10.3390/healthcare12040462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/24/2024] Open
Abstract
Breast cancer survival has increased significantly over the last few decades due to more effective strategies for prevention and risk modification, advancements in imaging detection, screening, and multimodal treatment algorithms. However, many have observed disparities in benefits derived from such improvements across populations and demographic groups. This review summarizes published works that contextualize modern disparities in breast cancer prevention, diagnosis, and treatment and presents potential strategies for reducing disparities. We conducted searches for studies that directly investigated and/or reported disparities in breast cancer prevention, detection, or treatment. Demographic factors, social determinants of health, and inequitable healthcare delivery may impede the ability of individuals and communities to employ risk-mitigating behaviors and prevention strategies. The disparate access to quality screening and timely diagnosis experienced by various groups poses significant hurdles to optimal care and survival. Finally, barriers to access and inequitable healthcare delivery patterns reinforce inequitable application of standards of care. Cumulatively, these disparities underlie notable differences in the incidence, severity, and survival of breast cancers. Efforts toward mitigation will require collaborative approaches and partnerships between communities, governments, and healthcare organizations, which must be considered equal stakeholders in the fight for equity in breast cancer care and outcomes.
Collapse
Affiliation(s)
- Avia D Wilkerson
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Corey K Gentle
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Camila Ortega
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zahraa Al-Hilli
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Breast Center, Integrated Surgical Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| |
Collapse
|
6
|
Wolfson EA, Schonberg MA, Eliassen AH, Bertrand KA, Shvetsov YB, Rosner BA, Palmer JR, LaCroix AZ, Chlebowski RT, Nelson RA, Ngo LH. Validating a model for predicting breast cancer and nonbreast cancer death in women aged 55 years and older. J Natl Cancer Inst 2024; 116:81-96. [PMID: 37676833 PMCID: PMC10777669 DOI: 10.1093/jnci/djad188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/24/2023] [Accepted: 08/29/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND To support mammography screening decision making, we developed a competing-risk model to estimate 5-year breast cancer risk and 10-year nonbreast cancer death for women aged 55 years and older using Nurses' Health Study data and examined model performance in the Black Women's Health Study (BWHS). Here, we examine model performance in predicting 10-year outcomes in the BWHS, Women's Health Initiative-Extension Study (WHI-ES), and Multiethnic Cohort (MEC) and compare model performance to existing breast cancer prediction models. METHODS We used competing-risk regression and Royston and Altman methods for validating survival models to calculate our model's calibration and discrimination (C index) in BWHS (n = 17 380), WHI-ES (n = 106 894), and MEC (n = 49 668). The Nurses' Health Study development cohort (n = 48 102) regression coefficients were applied to the validation cohorts. We compared our model's performance with breast cancer risk assessment tool (Gail) and International Breast Cancer Intervention Study (IBIS) models by computing breast cancer risk estimates and C statistics. RESULTS When predicting 10-year breast cancer risk, our model's C index was 0.569 in BWHS, 0.572 in WHI-ES, and 0.576 in MEC. The Gail model's C statistic was 0.554 in BWHS, 0.564 in WHI-ES, and 0.551 in MEC; IBIS's C statistic was 0.547 in BWHS, 0.552 in WHI-ES, and 0.562 in MEC. The Gail model underpredicted breast cancer risk in WHI-ES; IBIS underpredicted breast cancer risk in WHI-ES and in MEC but overpredicted breast cancer risk in BWHS. Our model calibrated well. Our model's C index for predicting 10-year nonbreast cancer death was 0.760 in WHI-ES and 0.763 in MEC. CONCLUSIONS Our competing-risk model performs as well as existing breast cancer prediction models in diverse cohorts and predicts nonbreast cancer death. We are developing a website to disseminate our model.
Collapse
Affiliation(s)
- Emily A Wolfson
- Division of General Medicine and Primary Care, Department of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mara A Schonberg
- 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 School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University and Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Yurii B Shvetsov
- University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Bernard A Rosner
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University and Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Andrea Z LaCroix
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | | | - Rebecca A Nelson
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, 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
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| |
Collapse
|
7
|
Zirpoli GR, Pfeiffer RM, Bertrand KA, Huo D, Lunetta KL, Palmer JR. Addition of polygenic risk score to a risk calculator for prediction of breast cancer in US Black women. Breast Cancer Res 2024; 26:2. [PMID: 38167144 PMCID: PMC10763003 DOI: 10.1186/s13058-023-01748-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Previous work in European ancestry populations has shown that adding a polygenic risk score (PRS) to breast cancer risk prediction models based on epidemiologic factors results in better discriminatory performance as measured by the AUC (area under the curve). Following publication of the first PRS to perform well in women of African ancestry (AA-PRS), we conducted an external validation of the AA-PRS and then evaluated the addition of the AA-PRS to a risk calculator for incident breast cancer in Black women based on epidemiologic factors (BWHS model). METHODS Data from the Black Women's Health Study, an ongoing prospective cohort study of 59,000 US Black women followed by biennial questionnaire since 1995, were used to calculate AUCs and 95% confidence intervals (CIs) for discriminatory accuracy of the BWHS model, the AA-PRS alone, and a new model that combined them. Analyses were based on data from 922 women with invasive breast cancer and 1844 age-matched controls. RESULTS AUCs were 0.577 (95% CI 0.556-0.598) for the BWHS model and 0.584 (95% CI 0.563-0.605) for the AA-PRS. For a model that combined estimates from the questionnaire-based BWHS model with the PRS, the AUC increased to 0.623 (95% CI 0.603-0.644). CONCLUSIONS This combined model represents a step forward for personalized breast cancer preventive care for US Black women, as its performance metrics are similar to those from models in other populations. Use of this new model may mitigate exacerbation of breast cancer disparities if and when it becomes feasible to include a PRS in routine health care decision-making.
Collapse
Affiliation(s)
- Gary R Zirpoli
- Slone Epidemiology Center at Boston University, Boston, MA, USA
| | - Ruth M Pfeiffer
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
- Division of Cancer Epidemiology and Biostatistics, National Cancer Institute, Bethesda, USA.
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University, Boston, MA, USA
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Dezheng Huo
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Center for Clinical Cancer Genetics & Global Health, The University of Chicago, Chicago, IL, USA
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center at Boston University, Boston, MA, USA.
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
| |
Collapse
|
8
|
Kamil D, Wojcik KM, Smith L, Zhang J, Wilson OWA, Butera G, Jayasekera J. A Scoping Review of Personalized, Interactive, Web-Based Clinical Decision Tools Available for Breast Cancer Prevention and Screening in the United States. MDM Policy Pract 2024; 9:23814683241236511. [PMID: 38500600 PMCID: PMC10946080 DOI: 10.1177/23814683241236511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/04/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction. Personalized web-based clinical decision tools for breast cancer prevention and screening could address knowledge gaps, enhance patient autonomy in shared decision-making, and promote equitable care. The purpose of this review was to present evidence on the availability, usability, feasibility, acceptability, quality, and uptake of breast cancer prevention and screening tools to support their integration into clinical care. Methods. We used the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews Checklist to conduct this review. We searched 6 databases to identify literature on the development, validation, usability, feasibility, acceptability testing, and uptake of the tools into practice settings. Quality assessment for each tool was conducted using the International Patient Decision Aid Standard instrument, with quality scores ranging from 0 to 63 (lowest-highest). Results. We identified 10 tools for breast cancer prevention and 9 tools for screening. The tools included individual (e.g., age), clinical (e.g., genomic risk factors), and health behavior (e.g., alcohol use) characteristics. Fourteen tools included race/ethnicity, but no tool incorporated contextual factors (e.g., insurance, access) associated with breast cancer. All tools were internally or externally validated. Six tools had undergone usability testing in samples including White (median, 71%; range, 9%-96%), insured (99%; 97%-100%) women, with college education or higher (60%; 27%-100%). All of the tools were developed and tested in academic settings. Seven (37%) tools showed potential evidence of uptake in clinical practice. The tools had an average quality assessment score of 21 (range, 9-39). Conclusions. There is limited evidence on testing and uptake of breast cancer prevention and screening tools in diverse clinical settings. The development, testing, and integration of tools in academic and nonacademic settings could potentially improve uptake and equitable access to these tools. Highlights There were 19 personalized, interactive, Web-based decision tools for breast cancer prevention and screening.Breast cancer outcomes were personalized based on individual clinical characteristics (e.g., age, medical history), genomic risk factors (e.g., BRCA1/2), race and ethnicity, and health behaviors (e.g., smoking). The tools did not include contextual factors (e.g., insurance status, access to screening facilities) that could potentially contribute to breast cancer outcomes.Validation, usability, acceptability, and feasibility testing were conducted mostly among White and/or insured patients with some college education (or higher) in academic settings. There was limited evidence on testing and uptake of the tools in nonacademic clinical settings.
Collapse
Affiliation(s)
- Dalya Kamil
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Kaitlyn M. Wojcik
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Laney Smith
- Frederick P. Whiddon College of Medicine, Mobile, AL, USA
| | | | - Oliver W. A. Wilson
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Gisela Butera
- Office of Research Services, National Institutes of Health Library, Bethesda, MD, USA
| | - Jinani Jayasekera
- Health Equity and Decision Sciences Research Laboratory, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
9
|
Tehranifar P, Bertrand KA. Enhancing Mammography and Empowering Solutions for Breast Cancer Disparities. Cancer Epidemiol Biomarkers Prev 2023; 32:1479-1481. [PMID: 37908191 DOI: 10.1158/1055-9965.epi-23-0926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 11/02/2023] Open
Abstract
Mammography enables early detection of breast cancer, a critical factor in improving treatment outcomes and breast cancer mortality. Yet, not all women benefit equally, and striking racial disparities in breast cancer mortality persist, with Black women 40% more likely to die from breast cancer compared with non-Hispanic White women. The current issue of Cancer Epidemiology, Biomarkers & Prevention presents three informative reports revealing racial and ethnic variations in mammography's performance in risk stratification, detection, and surveillance. The performance dynamics of mammography across different racial and ethnic groups highlight the urgency for additional research and innovative interventions to ensure equitable breast cancer control. We emphasize a pressing need for a comprehensive evaluation of multilevel influences on the performance and implementation of mammography in racially and ethnically diverse populations, complemented by equally urgent efforts to address factors influencing the risk of aggressive tumor subtypes and timely and effective treatment delivery. See related articles by Kerlikowske et al., p. 1524, Hubbard et al., p. 1531, Nyante et al., p. 1542.
Collapse
Affiliation(s)
- Parisa Tehranifar
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York
| | - Kimberly A Bertrand
- Slone Epidemiology Center at Boston University, Boston, Massachusetts
- Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| |
Collapse
|
10
|
Paige JS, Lee CI, Wang PC, Hsu W, Brentnall AR, Hoyt AC, Naeim A, Elmore JG. Variability Among Breast Cancer Risk Classification Models When Applied at the Level of the Individual Woman. J Gen Intern Med 2023; 38:2584-2592. [PMID: 36749434 PMCID: PMC10465429 DOI: 10.1007/s11606-023-08043-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 01/13/2023] [Indexed: 02/08/2023]
Abstract
BACKGROUND Breast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain. OBJECTIVE To quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer. DESIGN Comparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model. SUBJECTS Women 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome. MAIN MEASURES Comparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%). KEY RESULTS A total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level. CONCLUSIONS Breast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.
Collapse
Affiliation(s)
- Jeremy S Paige
- Department of Radiology, University of California, Los Angeles, CA, USA
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
| | - Pin-Chieh Wang
- Department of Medicine, Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, and Office of Health Informatics and Analytics, University of California, Los Angeles, Los Angeles, USA
| | - William Hsu
- Department of Radiology, University of California, Los Angeles, CA, USA
| | - Adam R Brentnall
- Centre for Evaluation and Methods, Wolfson Institute of Population Health, Charterhouse Square, Queen Mary University of London, London, UK
| | - Anne C Hoyt
- Department of Radiology, University of California, Los Angeles, CA, USA
| | - Arash Naeim
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Joann G Elmore
- Department of Medicine, Division of General Internal Medicine and Health Services Research and the National Clinician Scholars Program, David Geffen School of Medicine, University of California, Los Angeles, 1100 Glendon Ave, Ste. 900, Los Angeles, CA, 90024, USA.
| |
Collapse
|
11
|
Siegel SD, Brooks MM, Berman JD, Lynch SM, Sims-Mourtada J, Schug ZT, Curriero FC. Neighborhood factors and triple negative breast cancer: The role of cumulative exposure to area-level risk factors. Cancer Med 2023. [PMID: 36916687 DOI: 10.1002/cam4.5808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 01/08/2023] [Accepted: 03/01/2023] [Indexed: 03/16/2023] Open
Abstract
BACKGROUND Despite similar incidence rates among Black and White women, breast cancer mortality rates are 40% higher among Black women. More than half of the racial difference in breast cancer mortality can be attributed to triple negative breast cancer (TNBC), an aggressive subtype of invasive breast cancer that disproportionately affects Black women. Recent research has implicated neighborhood conditions in the etiology of TNBC. This study investigated the relationship between cumulative neighborhood-level exposures and TNBC risk. METHODS This single-institution retrospective study was conducted on a cohort of 3316 breast cancer cases from New Castle County, Delaware (from 2012 to 2020), an area of the country with elevated TNBC rates. Cases were stratified into TNBC and "Non-TNBC" diagnosis and geocoded by residential address. Neighborhood exposures included census tract-level measures of unhealthy alcohol use, metabolic dysfunction, breastfeeding, and environmental hazards. An overall cumulative risk score was calculated based on tract-level exposures. RESULTS Univariate analyses showed each tract-level exposure was associated with greater TNBC odds. In multivariate analyses that controlled for patient-level race and age, tract-level exposures were not associated with TNBC odds. However, in a second multivariate model that included patient-level variables and considered tract-level risk factors as a cumulative exposure risk score, each one unit increase in cumulative exposure was significantly associated with a 10% increase in TNBC odds. Higher cumulative exposure risk scores were found in census tracts with relatively high proportions of Black residents. CONCLUSIONS Cumulative exposure to neighborhood-level risk factors that disproportionately affect Black communities was associated with greater TNBC risk.
Collapse
Affiliation(s)
- Scott D Siegel
- Institute for Research on Equity & Community Health, Christiana Care Health System, Newark, Delaware, USA.,Helen F. Graham Cancer Center & Research Institute, Christiana Care Health System, Newark, Delaware, USA
| | - Madeline M Brooks
- Institute for Research on Equity & Community Health, Christiana Care Health System, Newark, Delaware, USA
| | - Jesse D Berman
- Division of Environmental Health Sciences, University of Minnesota School of Public Health, Minneapolis, Minnesota, USA
| | - Shannon M Lynch
- Cancer Prevention and Control, Fox Chase Cancer Center, Philadelphia, Pennsylvania, USA
| | - Jennifer Sims-Mourtada
- Helen F. Graham Cancer Center & Research Institute, Christiana Care Health System, Newark, Delaware, USA
| | - Zachary T Schug
- The Wistar Institute Cancer Center, Philadelphia, Pennsylvania, USA
| | - Frank C Curriero
- Department of Epidemiology, Johns Hopkins School of Public Health, John Hopkins Spatial Science for Public Health Center, Baltimore, Maryland, USA
| |
Collapse
|
12
|
Chambers P, Forster MD, Patel A, Duncan N, Kipps E, Wong ICK, Jani Y, Wei L. Development and validation of a risk score (Delay-7) to predict the occurrence of a treatment delay following cycle 1 chemotherapy. ESMO Open 2023; 8:100743. [PMID: 36542904 PMCID: PMC10024092 DOI: 10.1016/j.esmoop.2022.100743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/09/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The risk of toxicity-related dose delays, with cancer treatment, should be included as part of pretreatment education and be considered by clinicians upon prescribing chemotherapy. An objective measure of individual risk could influence clinical decisions, such as escalation of standard supportive care and stratification of some patients, to receive proactive toxicity monitoring. PATIENTS AND METHODS We developed a logistic regression prediction model (Delay-7) to assess the overall risk of a chemotherapy dose delay of 7 days for patients receiving first-line treatments for breast, colorectal and diffuse large B-cell lymphoma. Delay-7 included hospital treated, age at the start of chemotherapy, gender, ethnicity, body mass index, cancer diagnosis, chemotherapy regimen, colony stimulating factor use, first cycle dose modifications and baseline blood values. Baseline blood values included neutrophils, platelets, haemoglobin, creatinine and bilirubin. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope). Net benefit was used to understand the risk thresholds where the model would perform better than the 'treat all' or 'treat none' strategies. RESULTS A total of 4604 patients were included in our study of whom 628 (13.6%) incurred a 7-day delay to the second cycle of chemotherapy. Delay-7 showed good discrimination and calibration, with c-statistic of 0.68 (95% confidence interval 0.66-0.7), following internal validation and calibration-in-the-large of -0.006. CONCLUSIONS Delay-7 predicts a patient's individualised risk of a treatment-related delay at cycle two of treatment. The score can be used to stratify interventions to reduce the occurrence of treatment-related toxicity.
Collapse
Affiliation(s)
- P Chambers
- Department of Practice and Policy, School of Pharmacy, University College London, London, UK; The Centre for Medicines Optimisation Research and Education, University College London NHS Foundation Trust, London, UK.
| | - M D Forster
- University College London Cancer Institute, London, UK
| | - A Patel
- The Royal Marsden Hospital, London, UK
| | - N Duncan
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - E Kipps
- The Royal Marsden Hospital, London, UK
| | - I C K Wong
- Department of Practice and Policy, School of Pharmacy, University College London, London, UK; The Centre for Medicines Optimisation Research and Education, University College London NHS Foundation Trust, London, UK
| | - Y Jani
- Department of Practice and Policy, School of Pharmacy, University College London, London, UK; The Centre for Medicines Optimisation Research and Education, University College London NHS Foundation Trust, London, UK
| | - L Wei
- Department of Practice and Policy, School of Pharmacy, University College London, London, UK; The Centre for Medicines Optimisation Research and Education, University College London NHS Foundation Trust, London, UK
| |
Collapse
|
13
|
Schonberg MA, Wolfson EA, Eliassen AH, Bertrand KA, Shvetsov YB, Rosner BA, Palmer JR, Ngo LH. A model for predicting both breast cancer risk and non-breast cancer death among women > 55 years old. Breast Cancer Res 2023; 25:8. [PMID: 36694222 PMCID: PMC9872276 DOI: 10.1186/s13058-023-01605-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 01/16/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Guidelines recommend shared decision making (SDM) for mammography screening for women ≥ 75 and not screening women with < 10-year life expectancy. High-quality SDM requires consideration of women's breast cancer (BC) risk, life expectancy, and values but is hard to implement because no models simultaneously estimate older women's individualized BC risk and life expectancy. METHODS Using competing risk regression and data from 83,330 women > 55 years who completed the 2004 Nurses' Health Study (NHS) questionnaire, we developed (in 2/3 of the cohort, n = 55,533) a model to predict 10-year non-breast cancer (BC) death. We considered 60 mortality risk factors and used best-subsets regression, the Akaike information criterion, and c-index, to identify the best-fitting model. We examined model performance in the remaining 1/3 of the NHS cohort (n = 27,777) and among 17,380 Black Women's Health Study (BWHS) participants, ≥ 55 years, who completed the 2009 questionnaire. We then included the identified mortality predictors in a previously developed competing risk BC prediction model and examined model performance for predicting BC risk. RESULTS Mean age of NHS development cohort participants was 70.1 years (± 7.0); over 10 years, 3.1% developed BC, 0.3% died of BC, and 20.1% died of other causes; NHS validation cohort participants were similar. BWHS participants were younger (mean age 63.7 years [± 6.7]); over 10-years 3.1% developed BC, 0.4% died of BC, and 11.1% died of other causes. The final non-BC death prediction model included 21 variables (age; body mass index [BMI]; physical function [3 measures]; comorbidities [12]; alcohol; smoking; age at menopause; and mammography use). The final BC prediction model included age, BMI, alcohol and hormone use, family history, age at menopause, age at first birth/parity, and breast biopsy history. When risk factor regression coefficients were applied in the validation cohorts, the c-index for predicting 10-year non-BC death was 0.790 (0.784-0.796) in NHS and 0.768 (0.757-0.780) in BWHS; for predicting 5-year BC risk, the c-index was 0.612 (0.538-0.641) in NHS and 0.573 (0.536-0.611) in BWHS. CONCLUSIONS We developed and validated a novel competing-risk model that predicts 10-year non-BC death and 5-year BC risk. Model risk estimates may help inform SDM around mammography screening.
Collapse
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.
| | - Emily A Wolfson
- 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 School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA
| | - Kimberly A Bertrand
- Slone Epidemiology Center, Boston University, Boston University School of Medicine, Boston, MA, USA
| | - Yurii B Shvetsov
- University of Hawaii Cancer Center, University of Hawaii at Manoa, Manoa, HI, USA
| | - Bernard A Rosner
- Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard School of Public Health, Boston, MA, USA
| | - Julie R Palmer
- Slone Epidemiology Center, Boston University, Boston University School of Medicine, 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
| |
Collapse
|
14
|
Wilkerson AD, Obi M, Ortega C, Sebikali-Potts A, Wei W, Pederson HJ, Al-Hilli Z. Young Black Women May be More Likely to Have First Mammogram Cancers: A New Perspective in Breast Cancer Disparities. Ann Surg Oncol 2023; 30:2856-2869. [PMID: 36602665 DOI: 10.1245/s10434-022-12995-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 12/10/2022] [Indexed: 01/06/2023]
Abstract
INTRODUCTION Black women are diagnosed with breast cancer at earlier ages and are 42% more likely to die from the disease than White women. Recommendations for commencement of screening mammography remain discordant. This study sought to determine the frequency of first mammogram cancers among Black women versus other self-reported racial groups. METHODS In this retrospective cohort study, clinical and mammographic data were obtained from 738 women aged 40-45 years who underwent treatment for breast cancer between 2010 and 2019 within a single hospital system. First mammogram cancers were defined as those with tissue diagnoses within 3 months of baseline mammogram. Multivariate logistic regression was applied to assess variables associated with first mammogram cancer detection. RESULTS Black women were significantly more likely to have first mammogram cancer diagnoses (39/82, 47.6%) compared with White women (162/610, 26.6%) and other groups (16/46, 34.8%) [p < 0.001]. Black women were also more likely to have a body mass index > 30 (p < 0.001), higher clinical T categories (p = 0.02), and present with more advanced clinical stages (p = 0.03). Every month delay in mammographic screening beyond age 40 years (odds ratio [OR] 1.06, 95% confidence interval [CI] 1.05-1.07; p < 0.0001), Black race (OR 2.24, 95% CI 1.10-4.53; p = 0.03), and lack of private insurance (OR 2.41, 95% CI 1.22-4.73; p = 0.01) were associated with an increased likelihood of cancer detection on first mammogram. CONCLUSION Our findings suggests that Black women aged 40-45 years may be more likely to have cancer detected on their first mammogram and would benefit from starting screening mammography no later than age 40 years, and for those with elevated lifetime risk, even sooner.
Collapse
Affiliation(s)
- Avia D Wilkerson
- Department of General Surgery, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Megan Obi
- Department of General Surgery, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Camila Ortega
- Department of General Surgery, Cleveland Clinic Foundation, Cleveland, OH, USA
| | | | - Wei Wei
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Holly J Pederson
- Department of Breast Services, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Zahraa Al-Hilli
- Department of General Surgery, Cleveland Clinic Foundation, Cleveland, OH, USA. .,Department of Breast Services, Cleveland Clinic Foundation, Cleveland, OH, USA.
| |
Collapse
|
15
|
Eriksson M, Destounis S, Czene K, Zeiberg A, Day R, Conant EF, Schilling K, Hall P. A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care. Sci Transl Med 2022; 14:eabn3971. [PMID: 35544593 DOI: 10.1126/scitranslmed.abn3971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Screening with digital breast tomosynthesis (DBT) improves breast cancer detection and reduces false positives. However, currently, no breast cancer risk model takes advantage of the additional information generated by DBT imaging for breast cancer risk prediction. We developed and internally validated a DBT-based short-term risk model for predicting future late-stage and interval breast cancers after negative screening exams. We included the available 805 incident breast cancers and a random sample of 5173 healthy women matched on year of study entry in a nested case-control study from 154,200 multiethnic women, aged 35 to 74, attending DBT screening in the United States between 2014 and 2019. A relative risk model was trained using elastic net logistic regression and nested cross-validation to estimate risks for using imaging features and age. An absolute risk model was developed using derived risks and U.S. incidence and competing mortality rates. Absolute risks, discrimination performance, and risk stratification were estimated in the left-out validation set. The discrimination performance of 1-year risk was 0.82 (95% CI, 0.79 to 0.85) with good calibration (P = 0.7). Using the U.S. Preventive Service Task Force guidelines, 14% of the women were at high risk, 19.6 times higher compared to general risk. In this high-risk group, 76% of stage II and III cancers and 59% of stage 0 cancers were observed (P < 0.01). Using mammographic features generated from DBT screens, our image-based risk prediction model could guide radiologists in selecting women for clinical care, potentially leading to earlier detection and improved prognoses.
Collapse
Affiliation(s)
- Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska institutet, SE-171 77 Stockholm, Sweden
| | | | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska institutet, SE-171 77 Stockholm, Sweden
| | - Andrew Zeiberg
- Radiology Associates of Burlington County, Hainesport, NJ 08036, USA
| | - Robert Day
- Zwanger-Pesiri Radiology, Lindenhurst, NY 11757, USA
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | | | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska institutet, SE-171 77 Stockholm, Sweden.,Department of Oncology, Södersjukhuset University Hospital, Stockholm SE-118 61, Sweden
| |
Collapse
|
16
|
Colditz GA, Bennett DL, Tappenden J, Beers C, Ackermann N, Wu N, Luo J, Humble S, Linnenbringer E, Davis K, Jiang S, Toriola AT. Joanne Knight Breast Health Cohort at Siteman Cancer Center. Cancer Causes Control 2022; 33:623-629. [PMID: 35059919 PMCID: PMC8904336 DOI: 10.1007/s10552-022-01554-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 01/11/2022] [Indexed: 12/01/2022]
Abstract
PURPOSE The Joanne Knight Breast Health Cohort was established to link breast cancer risk factors, mammographic breast density, benign breast biopsies and associated tissue markers, and blood markers in a diverse population of women undergoing routine mammographic screening to study risk factors and validate models for breast cancer risk prediction. METHODS Women were recruited from November 2008 to April 2012 through the mammography service at the Joanne Knight Breast Health Center at Washington University in St. Louis, Missouri. Baseline questionnaire risk factors, blood, and screening mammograms were collected from 12,153 women. Of these, 1,672 were excluded for prior history of any cancer (except non-melanoma skin) or diagnosis of breast cancer within 6 months of blood draw/registration for the study, for a total of 10,481 women. Follow-up is through linking to electronic health records, tumor registry, and death register. Routine screening mammograms are collected every 1-2 years and incident benign breast biopsies and cancers are identified through record linkage to pathology and tumor registries. Formal fixed tissue samples are retrieved and stored for analysis. County-level measures of structural inequality were derived from publicly available resources. RESULTS Cohort Composition: median age at entry was 54.8 years and 26.7% are African American. Through 2020, 74% of participants have had a medical center visit within the past year and 80% within the past 2 years representing an average of 9.7 person-years of follow-up from date of blood draw per participant. 9,997 women are continuing in follow-up. Data collected at baseline include breast cancer risk factors, plasma and white blood cells, and mammograms prior to baseline, at baseline, and during follow-up. CONCLUSION This cohort assembled and followed in a routine mammography screening and care setting that serves a diverse population of women in the St. Louis region now provides opportunities to integrate study of questionnaire measures, plasma and DNA markers, benign and malignant tissue markers, and repeated breast image features into prospective evaluation for breast cancer etiology and outcomes.
Collapse
Affiliation(s)
- Graham A Colditz
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA.
| | - Debbie L Bennett
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Jennifer Tappenden
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Courtney Beers
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Nicole Ackermann
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Ningying Wu
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Jingqin Luo
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Sarah Humble
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Erin Linnenbringer
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Kia Davis
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Shu Jiang
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| | - Adetunji T Toriola
- Division and Public Health Sciences and Siteman Cancer Center, Washington University School of Medicine, 660 South Euclid Avenue, Campus Box 8100, Saint Louis, MO, 63110, USA
| |
Collapse
|