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Tran TXM, Chang Y, Ryu S, Park B. Mammographic breast features and risk of cardiovascular diseases in korean women. Heart Lung 2024; 67:176-182. [PMID: 38838416 DOI: 10.1016/j.hrtlng.2024.05.002] [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: 03/16/2024] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND There is a growing amount of evidence on the association between cardiovascular diseases (CVDs) and breast calcification. Thus, mammographic breast features have recently gained attention as CVD predictors. OBJECTIVE This study assessed the association of mammographic features, including benign calcification, microcalcification, and breast density, with cardiovascular diseases. METHODS This study comprised 6,878,686 women aged ≥40 who underwent mammographic screening between 2009 and 2012 with follow-up until 2020. The mammographic features included benign calcification, microcalcification, and breast density. The cardiovascular diseases associated with the mammographic features were assessed using logistic regression. RESULTS The prevalence of benign calcification, microcalcification, and dense breasts were 9.6 %, 0.9 % and 47.3 % at baseline, respectively. Over a median follow-up of 10 years, benign calcification and microcalcification were positively associated with an increased risk of chronic ischaemic heart disease whereas breast density was inversely associated with it; the corresponding aOR (95 % CI) was 1.14 (1.10-1.17), 1.19 (1.03-1.15), and 0.88 (0.85-0.90), respectively. A significantly increased risk of chronic ischaemic heart disease (IHD) was observed among women with benign calcifications (aHR, 1.14; 95 % CI 1.10-1.17) and microcalcifications (aOR, 1.19; 95 % CI 1.06-1.33). Women with microcalcifications had a 1.16-fold (95 % CI 1.03-1.30) increased risk of heart failure. CONCLUSIONS Mammographic calcifications were associated with an increased risk of chronic ischaemic heart diseases, whereas dense breast was associated with a decreased risk of cardiovascular disease. Thus, the mammographic features identified on breast cancer screening may provide an opportunity for cardiovascular disease risk identification and prevention.
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Affiliation(s)
- Thi Xuan Mai Tran
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea; Institute for Health and Society, Hanyang University, Seoul, South Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Seungho Ryu
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea; Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Boyoung Park
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea; Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, Republic of Korea.
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Kim E, Lewin AA. Breast Density: Where Are We Now? Radiol Clin North Am 2024; 62:593-605. [PMID: 38777536 DOI: 10.1016/j.rcl.2023.12.007] [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: 05/25/2024]
Abstract
Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.
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Affiliation(s)
- Eric Kim
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Alana A Lewin
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA; New York University Grossman School of Medicine, New York University Langone Health, Laura and Isaac Perlmutter Cancer Center, 160 East 34th Street 3rd Floor, New York, NY 10016, USA.
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3
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Barnard ME, DuPré NC, Heine JJ, Fowler EE, Murthy DJ, Nelleke RL, Chan A, Warner ET, Tamimi RM. Reproductive risk factors for breast cancer and association with novel breast density measurements among Hispanic, Black, and White women. Breast Cancer Res Treat 2024; 204:309-325. [PMID: 38095811 PMCID: PMC10948301 DOI: 10.1007/s10549-023-07174-w] [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: 06/14/2023] [Accepted: 11/02/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE There are differences in the distributions of breast cancer incidence and risk factors by race and ethnicity. Given the strong association between breast density and breast cancer, it is of interest describe racial and ethnic variation in the determinants of breast density. METHODS We characterized racial and ethnic variation in reproductive history and several measures of breast density for Hispanic (n = 286), non-Hispanic Black (n = 255), and non-Hispanic White (n = 1694) women imaged at a single hospital. We quantified associations between reproductive factors and percent volumetric density (PVD), dense volume (DV), non-dense volume (NDV), and a novel measure of pixel intensity variation (V) using multivariable-adjusted linear regression, and tested for statistical heterogeneity by race and ethnicity. RESULTS Reproductive factors most strongly associated with breast density were age at menarche, parity, and oral contraceptive use. Variation by race and ethnicity was most evident for the associations between reproductive factors and NDV (minimum p-heterogeneity:0.008) and V (minimum p-heterogeneity:0.004) and least evident for PVD (minimum p-heterogeneity:0.042) and DV (minimum p-heterogeneity:0.041). CONCLUSION Reproductive choices, particularly those related to childbearing and oral contraceptive use, may contribute to racial and ethnic variation in breast density.
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Affiliation(s)
- Mollie E Barnard
- Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA.
- University of Utah Intermountain Healthcare Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
| | - Natalie C DuPré
- Department of Epidemiology and Population Health, School of Public Health and Information Sciences, University of Louisville, Louisville, KY, USA
| | - John J Heine
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Erin E Fowler
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Divya J Murthy
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rebecca L Nelleke
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Ariane Chan
- Volpara Health Technologies Ltd., Wellington, New Zealand
| | - Erica T Warner
- Clinical Translational Epidemiology Unit, Department of Medicine, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medical, New York, NY, USA
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4
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Ye Z, Dite GS, Nguyen TL, MacInnis RJ, Schmidt DF, Makalic E, Al-Qershi OM, Nguyen-Dumont T, Goudey B, Stone J, Dowty JG, Giles GG, Southey MC, Hopper JL, Li S. Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures. Cancer Epidemiol Biomarkers Prev 2024; 33:306-313. [PMID: 38059829 DOI: 10.1158/1055-9965.epi-23-1012] [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: 08/27/2023] [Revised: 10/24/2023] [Accepted: 12/05/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus. METHODS We measured Cirrus for 3,195 breast cancer-free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1,599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The SNP-based heritability was estimated for a subset of 2,356 participants. RESULTS There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52 (SE, 0.03) for MZ pairs and 0.16(SE, 0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32% (SE = 5%) of the variance, consistent with the SNP-based heritability of 36% (SE = 16%). CONCLUSION Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to nongenetic factors operating in utero or in early life that are shared by MZ twins. IMPACT Early-life factors, shared more by MZ pairs than DZ/non-twin sister pairs, could play a role in the variation in Cirrus, consistent with early life being recognized as a critical window of vulnerability to breast carcinogens.
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Affiliation(s)
- Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Genetic Technologies Limited, Fitzroy, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Osamah M Al-Qershi
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Tu Nguyen-Dumont
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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Bae SJ, Kim HJ, Kim HA, Ryu JM, Park S, Lee EG, Im SA, Jung Y, Park MH, Park KH, Kang SH, Park E, Kim SY, Lee MH, Kim LS, Lee A, Noh WC, Gwark S, Kim S, Jeong J. Breast density reduction as a predictor for prognosis in premenopausal women with estrogen receptor-positive breast cancer: an exploratory analysis of the updated ASTRRA study. Int J Surg 2024; 110:934-942. [PMID: 38000057 PMCID: PMC10871609 DOI: 10.1097/js9.0000000000000907] [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: 07/05/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND While the relationship between mammographic breast density reduction (MDR) and endocrine therapy efficacy has been reported in estrogen receptor (ER)-positive breast cancer, it is still unclear in premenopausal women, especially in the case of adding ovarian function suppression (OFS) to antihormone therapy. The authors investigated the impact of MDR on prognosis stratified by treatment based on the updated results of the ASTRRA trial. MATERIALS AND METHODS The ASTRRA trial, a randomized phase III study, showed that adding OFS to tamoxifen (TAM) improved survival in premenopausal women with estrogen receptor-positive breast cancer after chemotherapy. The authors updated survival outcomes and assessed mammography before treatment and the annual follow-up mammography for up to 5 years after treatment initiation. Mammographic density (MD) was classified into four categories based on the Breast Imaging-Reporting and Data System. MDR-positivity was defined as a downgrade in MD grade on follow-up mammography up to 2 years after randomization, with pretreatment MD grade as a reference. RESULTS The authors evaluated MDR in 944 of the 1282 patients from the trial, and 813 (86.2%) had grade III or IV MD. There was no difference in the MDR-positivity rate between the two treatment groups [TAM-only group (106/476 (22.3%)) vs. TAM+OFS group (89/468 (19.0%)); P =0.217). MDR-positivity was significantly associated with better disease-free survival (DFS) in the TAM+OFS group (estimated 8-year DFS: 93.1% in MDR-positive vs. 82.0% in MDR-negative patients; HR: 0.37; 95% CI: 0.16-0.85; P =0.019), but not in the TAM-only group ( Pinteraction =0.039). MDR-positive patients who received TAM+OFS had a favorable DFS compared to MDR-negative patients who received only TAM (HR: 0.30; 95% CI: 0.13-0.70; P =0.005). CONCLUSION Although the proportion of MDR-positive patients was comparable between both treatment groups, MDR-positivity was independently associated with favorable outcomes only in the TAM+OFS group.
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Affiliation(s)
- Soong June Bae
- Department of Surgery, Gangnam Severance Hospital
- Institute for Breast Cancer Precision Medicine, Yonsei University College of Medicine
| | - Hee Jeong Kim
- Division of Breast, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine
| | - Hyun-Ah Kim
- Department of Surgery, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences
| | - Jai Min Ryu
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine
| | - Seho Park
- Division of Breast Surgery, Department of Surgery, Yonsei Cancer Center, Yonsei University College of Medicine
| | - Eun-Gyeong Lee
- Center for Breast Cancer, Research Institute and Hospital, National Cancer Center, Goyang, South Korea
| | - Seock-Ah Im
- Seoul National University Hospital, Cancer Research Institute, Seoul National University, College of Medicine
| | - Yongsik Jung
- Department of Surgery, Ajou University, School of Medicine, Suwon
| | - Min Ho Park
- Department of Surgery, Chonnam National University Medical School and Chonnam National University Hwasun Hospital, Gwangju
| | - Kyong Hwa Park
- Korea University Anam Hospital, Department of internal medicine, Division of Medical Oncology/Hematology
| | | | - Eunhwa Park
- Department of Surgery, Dong-A University Hospital, Dong-A University College of Medicine, Busan
| | - Sung Yong Kim
- Department of Surgery, Soonchunhyang University Cheonan Hospital, Cheonan
| | - Min Hyuk Lee
- Department of Surgery, Soonchunhyang University Hospital, Seoul
| | - Lee Su Kim
- Department of Surgery, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong
| | - Anbok Lee
- Department of Surgery, Chung-Ang University Gwangmyeong Hospital, Gwangmyeong
| | - Woo Chul Noh
- Department of Surgery, Konkuk Universitiy Medical Center
| | - Sungchan Gwark
- Department of Surgery, Ewha Womans University College of Medicine, Ewha Womans University Mokdong Hospital
| | - Seonok Kim
- Department of Clinical Epidemiology and Biostatistics, Asan Medical Center
| | - Joon Jeong
- Department of Surgery, Gangnam Severance Hospital
- Institute for Breast Cancer Precision Medicine, Yonsei University College of Medicine
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Matthew KA, Getz KR, Jeon MS, Luo C, Luo J, Toriola AT. Associations of Vitamins and Related Cofactor Metabolites with Mammographic Breast Density in Premenopausal Women. J Nutr 2024; 154:424-434. [PMID: 38122846 PMCID: PMC10900193 DOI: 10.1016/j.tjnut.2023.12.023] [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: 09/18/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Identifying biological drivers of mammographic breast density (MBD), a strong risk factor for breast cancer, could provide insight into breast cancer etiology and prevention. Studies on dietary factors and MBD have yielded conflicting results. There are, however, very limited data on the associations of dietary biomarkers and MBD. OBJECTIVE We aimed to investigate the associations of vitamins and related cofactor metabolites with MBD in premenopausal women. METHODS We measured 37 vitamins and related cofactor metabolites in fasting plasma samples of 705 premenopausal women recruited during their annual screening mammogram at the Washington University School of Medicine, St. Louis, MO. Volpara was used to assess volumetric percent density (VPD), dense volume (DV), and nondense volume (NDV). We estimated the least square means of VPD, DV, and NDV across quartiles of each metabolite, as well as the regression coefficient of a metabolite in continuous scale from multiple covariate-adjusted linear regression. We corrected for multiple testing using the Benjamini-Hochberg procedure to control the false discover rate (FDR) at a 5% level. RESULTS Participants' mean VPD was 10.5%. Two vitamin A metabolites (β-cryptoxanthin and carotene diol 2) were positively associated, and one vitamin E metabolite (γ-tocopherol) was inversely associated with VPD. The mean VPD increased across quartiles of β-cryptoxanthin (Q1 = 7.2%, Q2 = 7.7%, Q3 = 8.4%%, Q4 = 9.2%; P-trend = 1.77E-05, FDR P value = 1.18E-03). There was a decrease in the mean VPD across quartiles of γ-tocopherol (Q1 = 9.4%, Q2 = 8.1%, Q3 = 8.0%, Q4 = 7.8%; P -trend = 4.01E-03, FDR P value = 0.04). Seven metabolites were associated with NDV: 3 vitamin E (γ-CEHC glucuronide, δ-CEHC, and γ-tocopherol) and 1 vitamin C (gulonate) were positively associated, whereas 2 vitamin A (carotene diol 2 and β-cryptoxanthin) and 1 vitamin C (threonate) were inversely associated with NDV. No metabolite was significantly associated with DV. CONCLUSION We report novel associations of vitamins and related cofactor metabolites with MBD in premenopausal women.
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Affiliation(s)
- Kayode A Matthew
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Kayla R Getz
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Myung Sik Jeon
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States; Siteman Cancer Center Biostatistics and Qualitative Research Shared Resource, Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Chongliang Luo
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States; Siteman Cancer Center Biostatistics and Qualitative Research Shared Resource, Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Jingqin Luo
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States; Siteman Cancer Center Biostatistics and Qualitative Research Shared Resource, Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States
| | - Adetunji T Toriola
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, United States; Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, United States.
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Mao X, He W, Eriksson M, Lindström LS, Holowko N, Bajalica-Lagercrantz S, Hammarström M, Grassmann F, Humphreys K, Easton D, Hall P, Czene K. Prediction of breast cancer risk for sisters of women attending screening. J Natl Cancer Inst 2023; 115:1310-1317. [PMID: 37243694 PMCID: PMC10637039 DOI: 10.1093/jnci/djad101] [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: 01/09/2023] [Revised: 04/17/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023] Open
Abstract
BACKGROUND Risk assessment is important for breast cancer prevention and early detection. We aimed to examine whether common risk factors, mammographic features, and breast cancer risk prediction scores of a woman were associated with breast cancer risk for her sisters. METHODS We included 53 051 women from the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) study. Established risk factors were derived using self-reported questionnaires, mammograms, and single nucleotide polymorphism genotyping. Using the Swedish Multi-Generation Register, we identified 32 198 sisters of the KARMA women (including 5352 KARMA participants and 26 846 nonparticipants). Cox models were used to estimate the hazard ratios of breast cancer for both women and their sisters, respectively. RESULTS A higher breast cancer polygenic risk score, a history of benign breast disease, and higher breast density in women were associated with an increased risk of breast cancer for both women and their sisters. No statistically significant association was observed between breast microcalcifications and masses in women and breast cancer risk for their sisters. Furthermore, higher breast cancer risk scores in women were associated with an increased risk of breast cancer for their sisters. Specifically, the hazard ratios for breast cancer per 1 standard deviation increase in age-adjusted KARMA, Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA), and Tyrer-Cuzick risk scores were 1.16 (95% confidence interval [CI] = 1.07 to 1.27), 1.23 (95% CI = 1.12 to 1.35), and 1.21 (95% CI = 1.11 to 1.32), respectively. CONCLUSION A woman's breast cancer risk factors are associated with her sister's breast cancer risk. However, the clinical utility of these findings requires further investigation.
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Affiliation(s)
- Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Chronic Disease Research Institute, The Children’s Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Linda S Lindström
- Department of Oncology-Pathology, Karolinska Institutet and Hereditary Cancer Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Natalie Holowko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
| | - Svetlana Bajalica-Lagercrantz
- Department of Oncology-Pathology, Karolinska Institutet and Hereditary Cancer Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Mattias Hammarström
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Institute for Clinical Research and Systems Medicine, Health and Medical University, Potsdam, Germany
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Douglas Easton
- Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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8
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Ye Z, Nguyen TL, Dite GS, MacInnis RJ, Schmidt DF, Makalic E, Al-Qershi OM, Bui M, Esser VFC, Dowty JG, Trinh HN, Evans CF, Tan M, Sung J, Jenkins MA, Giles GG, Southey MC, Hopper JL, Li S. Causal relationships between breast cancer risk factors based on mammographic features. Breast Cancer Res 2023; 25:127. [PMID: 37880807 PMCID: PMC10598934 DOI: 10.1186/s13058-023-01733-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/17/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology. METHODS We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method. RESULTS The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P < 0.005). We estimated that 28-92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively). CONCLUSIONS In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways.
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Affiliation(s)
- Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
- Genetic Technologies Limited, Fitzroy, VIC, 3065, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Osamah M Al-Qershi
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Ho N Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500, Sunway City, Malaysia
- School of Electrical and Computer Engineering, The University of Oklahoma, Norman, OK, 73019, USA
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, 08826, Korea
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia.
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, CB1 8RN, UK.
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, 3051, Australia.
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9
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Mariapun S, Ho WK, Eriksson M, Tai MC, Mohd Taib NA, Yip CH, Rahmat K, Li J, Hartman M, Hall P, Easton DF, Lindstrom S, Teo SH. Evaluation of SNPs associated with mammographic density in European women with mammographic density in Asian women from South-East Asia. Breast Cancer Res Treat 2023; 201:237-245. [PMID: 37338730 PMCID: PMC10865780 DOI: 10.1007/s10549-023-06984-2] [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: 11/19/2022] [Accepted: 05/24/2023] [Indexed: 06/21/2023]
Abstract
PURPOSE Mammographic density (MD), after accounting for age and body mass index (BMI), is a strong heritable risk factor for breast cancer. Genome-wide association studies (GWAS) have identified 64 SNPs in 55 independent loci associated with MD in women of European ancestry. Their associations with MD in Asian women, however, are largely unknown. METHOD Using linear regression adjusting for age, BMI, and ancestry-informative principal components, we evaluated the associations of previously reported MD-associated SNPs with MD in a multi-ethnic cohort of Asian ancestry. Area and volumetric mammographic densities were determined using STRATUS (N = 2450) and Volpara™ (N = 2257). We also assessed the associations of these SNPs with breast cancer risk in an Asian population of 14,570 cases and 80,870 controls. RESULTS Of the 61 SNPs available in our data, 21 were associated with MD at a nominal threshold of P value < 0.05, all in consistent directions with those reported in European ancestry populations. Of the remaining 40 variants with a P-value of association > 0.05, 29 variants showed consistent directions of association as those previously reported. We found that nine of the 21 MD-associated SNPs in this study were also associated with breast cancer risk in Asian women (P < 0.05), seven of which showed a direction of associations that was consistent with that reported for MD. CONCLUSION Our study confirms the associations of 21 SNPs (19/55 or 34.5% out of all known MD loci identified in women of European ancestry) with area and/or volumetric densities in Asian women, and further supports the evidence of a shared genetic basis through common genetic variants for MD and breast cancer risk.
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Affiliation(s)
- Shivaani Mariapun
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Semenyih, Selangor, Malaysia
| | - Weang Kee Ho
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham Malaysia, Semenyih, Selangor, Malaysia
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mei Chee Tai
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
| | - Nur Aishah Mohd Taib
- Faculty of Medicine, University Malaya Cancer Research Institute, University Malaya, Kuala Lumpur, Malaysia
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Cheng Har Yip
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Subang Jaya Medical Centre, Subang Jaya, Malaysia
| | - Kartini Rahmat
- Faculty of Medicine, University Malaya Cancer Research Institute, University Malaya, Kuala Lumpur, Malaysia
- Biomedical Imaging Department, Faculty of Medicine, Universiti Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia
| | - Jingmei Li
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Human Genetics, Genome Institute of Singapore, Singapore, Singapore
| | - Mikael Hartman
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Surgery, National University Hospital and National University Health System, Singapore, Singapore
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK
| | - Sara Lindstrom
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Soo-Hwang Teo
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia.
- Faculty of Medicine, University Malaya Cancer Research Institute, University Malaya, Kuala Lumpur, Malaysia.
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10
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Verma SS, Guare L, Ehsan S, Gastounioti A, Scales G, Ritchie MD, Kontos D, McCarthy AM. Genome-Wide Association Study of Breast Density among Women of African Ancestry. Cancers (Basel) 2023; 15:2776. [PMID: 37345113 DOI: 10.3390/cancers15102776] [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: 03/14/2023] [Revised: 05/03/2023] [Accepted: 05/11/2023] [Indexed: 06/23/2023] Open
Abstract
Breast density, the amount of fibroglandular versus fatty tissue in the breast, is a strong breast cancer risk factor. Understanding genetic factors associated with breast density may help in clarifying mechanisms by which breast density increases cancer risk. To date, 50 genetic loci have been associated with breast density, however, these studies were performed among predominantly European ancestry populations. We utilized a cohort of women aged 40-85 years who underwent screening mammography and had genetic information available from the Penn Medicine BioBank to conduct a Genome-Wide Association Study (GWAS) of breast density among 1323 women of African ancestry. For each mammogram, the publicly available "LIBRA" software was used to quantify dense area and area percent density. We identified 34 significant loci associated with dense area and area percent density, with the strongest signals in GACAT3, CTNNA3, HSD17B6, UGDH, TAAR8, ARHGAP10, BOD1L2, and NR3C2. There was significant overlap between previously identified breast cancer SNPs and SNPs identified as associated with breast density. Our results highlight the importance of breast density GWAS among diverse populations, including African ancestry populations. They may provide novel insights into genetic factors associated with breast density and help in elucidating mechanisms by which density increases breast cancer risk.
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Affiliation(s)
- Shefali Setia Verma
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lindsay Guare
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah Ehsan
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aimilia Gastounioti
- Washington University School of Medicine in St. Louis, St. Louis, MO 63130, USA
| | | | - Marylyn D Ritchie
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Despina Kontos
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anne Marie McCarthy
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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11
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Tran TXM, Chang Y, Kim S, Song H, Ryu S, Park B. Association of Breast Cancer Family History With Breast Density Over Time in Korean Women. JAMA Netw Open 2023; 6:e232420. [PMID: 36897591 DOI: 10.1001/jamanetworkopen.2023.2420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
IMPORTANCE Evidence suggests that women with a family history of breast cancer (FHBC) in first-degree relatives have a higher level of breast density; however, studies of premenopausal women remain limited. OBJECTIVE To investigate the association between FHBC and mammographic breast density and breast density changes among premenopausal women. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study used population-based data obtained from the National Health Insurance Service-National Health Information Database of Korea. We included premenopausal women aged 40 to 55 years who underwent mammography for breast cancer screening once between January 1, 2015, and December 31, 2016 (n = 1 174 214), and women who underwent mammography twice (first in 2015-2016 and again between January 1, 2017 and December 31, 2018) (n = 838 855). EXPOSURES Family history of breast cancer was assessed using a self-reported questionnaire, which included information on FHBC in the mother and/or sister. MAIN OUTCOMES AND MEASURES Breast density, based on the Breast Imaging Reporting and Data System, was categorized as dense (heterogeneously or extremely dense) and nondense (almost entirely fat or scattered fibroglandular areas). Multivariate logistic regression was used to assess the association among FHBC, breast density, and changes in breast density from the first to second screening. Data analysis was performed from June 1 to September 31, 2022. RESULTS Of the 1 174 214 premenopausal women, 34 003 (2.4%; mean [SD] age, 46.3 [3.2] years) reported having FHBC among their first-degree relatives, and 1 140 211 (97.1%; mean [SD] age, 46.3 [3.2] years) reported no FHBC. Odds of having dense breasts was 22% higher (adjusted odds ratio [aOR], 1.22; 95% CI, 1.19-1.26) in women with FHBC than in women without FHBC, and the association varied by affected relatives: mother alone (aOR, 1.15; 95% CI, 1.10-1.21), sister alone (aOR, 1.26; 95% CI, 1.22-1.31), and both mother and sister (aOR, 1.64; 95% CI, 1.20-2.25). Among women with fatty breasts at baseline, the odds of developing dense breasts was higher in women with FHBC than in those without FHBC (aOR, 1.19; 95% CI, 1.11-1.26), whereas among women with dense breasts, higher odds of having persistently dense breasts were observed in women with FHBC (aOR, 1.11; 95% CI, 1.05-1.16) than in those without FHBC. CONCLUSIONS AND RELEVANCE In this cohort study of premenopausal Korean women, FHBC was positively associated with an increased incidence of having increased or persistently dense breasts over time. These findings suggest the need for a tailored breast cancer risk assessment for women with FHBC.
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Affiliation(s)
- Thi Xuan Mai Tran
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Soyeoun Kim
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Huiyeon Song
- Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea
| | - Seungho Ryu
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Boyoung Park
- Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
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12
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Mao X, He W, Humphreys K, Eriksson M, Holowko N, Strand F, Hall P, Czene K. Factors Associated With False-Positive Recalls in Mammography Screening. J Natl Compr Canc Netw 2023; 21:143-152.e4. [PMID: 36791753 DOI: 10.6004/jnccn.2022.7081] [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: 05/28/2022] [Accepted: 09/27/2022] [Indexed: 02/17/2023]
Abstract
BACKGROUND We aimed to identify factors associated with false-positive recalls in mammography screening compared with women who were not recalled and those who received true-positive recalls. METHODS We included 29,129 women, aged 40 to 74 years, who participated in the Karolinska Mammography Project for Risk Prediction of Breast Cancer (KARMA) between 2011 and 2013 with follow-up until the end of 2017. Nonmammographic factors were collected from questionnaires, mammographic factors were generated from mammograms, and genotypes were determined using the OncoArray or an Illumina custom array. By the use of conditional and regular logistic regression models, we investigated the association between breast cancer risk factors and risk models and false-positive recalls. RESULTS Women with a history of benign breast disease, high breast density, masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have mammography recalls, including both false-positive and true-positive recalls. Further analyses restricted to women who were recalled found that women with a history of benign breast disease and dense breasts had a similar risk of having false-positive and true-positive recalls, whereas women with masses, microcalcifications, high Tyrer-Cuzick 10-year risk scores, KARMA 2-year risk scores, and polygenic risk scores were more likely to have true-positive recalls than false-positive recalls. CONCLUSIONS We found that risk factors associated with false-positive recalls were also likely, or even more likely, to be associated with true-positive recalls in mammography screening.
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Affiliation(s)
- Xinhe Mao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Wei He
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Chronic Disease Research Institute, the Children's Hospital, and National Clinical Research Center for Child Health, School of Public Health, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Nutrition and Food Hygiene, School of Public Health, Zhejiang University, Hangzhou, Zhejiang, China
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Natalie Holowko
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Medicine Solna, Clinical Epidemiology Division, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Strand
- Department of Radiology, Karolinska University Hospital, Stockholm, Sweden.,Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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13
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Edmonds CE, O'Brien SR, Conant EF. Mammographic Breast Density: Current Assessment Methods, Clinical Implications, and Future Directions. Semin Ultrasound CT MR 2023; 44:35-45. [PMID: 36792272 DOI: 10.1053/j.sult.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.
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Affiliation(s)
- Christine E Edmonds
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA.
| | - Sophia R O'Brien
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - Emily F Conant
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
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14
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Le Cam S, Badachi Y, Ayadi S, Lucidarme O. Performance of ultrasound guidance for vacuum-assisted biopsy of breast microcalcifications without associated mass. RESEARCH IN DIAGNOSTIC AND INTERVENTIONAL IMAGING 2022; 3:100012. [PMID: 39077379 PMCID: PMC11265181 DOI: 10.1016/j.redii.2022.100012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 07/26/2022] [Indexed: 07/31/2024]
Abstract
•The accuracy of ultrasound (US)-guided biopsies of suspicious microcalcifications without mass is similar, or even better than results published in literature with stereotactic-guided biopsies.•When the microcalcifications are visible on ultrasound, there is no loss of chance in performing the biopsy under ultrasound guidance rather than under stereotactic guidance.•US guidance allows access to difficult areas for stereotactic, increases the comfort of the patient, avoids compression and irradiation.
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Affiliation(s)
- S. Le Cam
- Paris University, APHP, Bicêtre hospital, 94270 Le Kremlin-Bicêtre, France
| | - Y. Badachi
- Department of General Radiology, GH La Pitié-Salpêtrière Charles-Foix, Paris, France
| | - S. Ayadi
- Department of General Radiology, GH La Pitié-Salpêtrière Charles-Foix, Paris, France
| | - O. Lucidarme
- Sorbonne University, CNRS, INSERM, Laboratoire d'Imagerie Biomédicale, APHP, Pitié-Salpêtrière Hospital, 75013 Paris, France
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15
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Chalfant JS, Hoyt AC. Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications. JOURNAL OF BREAST IMAGING 2022; 4:357-370. [PMID: 38416979 DOI: 10.1093/jbi/wbac028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Indexed: 03/01/2024]
Abstract
Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.
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Affiliation(s)
- James S Chalfant
- David Geffen School of Medicine at University of California, Los Angeles, Department of Radiological Sciences, Santa Monica, CA, USA
| | - Anne C Hoyt
- David Geffen School of Medicine at University of California, Los Angeles, Department of Radiological Sciences, Santa Monica, CA, USA
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16
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Chen H, Fan S, Stone J, Thompson DJ, Douglas J, Li S, Scott C, Bolla MK, Wang Q, Dennis J, Michailidou K, Li C, Peters U, Hopper JL, Southey MC, Nguyen-Dumont T, Nguyen TL, Fasching PA, Behrens A, Cadby G, Murphy RA, Aronson K, Howell A, Astley S, Couch F, Olson J, Milne RL, Giles GG, Haiman CA, Maskarinec G, Winham S, John EM, Kurian A, Eliassen H, Andrulis I, Evans DG, Newman WG, Hall P, Czene K, Swerdlow A, Jones M, Pollan M, Fernandez-Navarro P, McConnell DS, Kristensen VN, Rothstein JH, Wang P, Habel LA, Sieh W, Dunning AM, Pharoah PDP, Easton DF, Gierach GL, Tamimi RM, Vachon CM, Lindström S. Genome-wide and transcriptome-wide association studies of mammographic density phenotypes reveal novel loci. Breast Cancer Res 2022; 24:27. [PMID: 35414113 PMCID: PMC9006574 DOI: 10.1186/s13058-022-01524-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/02/2022] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Mammographic density (MD) phenotypes, including percent density (PMD), area of dense tissue (DA), and area of non-dense tissue (NDA), are associated with breast cancer risk. Twin studies suggest that MD phenotypes are highly heritable. However, only a small proportion of their variance is explained by identified genetic variants. METHODS We conducted a genome-wide association study, as well as a transcriptome-wide association study (TWAS), of age- and BMI-adjusted DA, NDA, and PMD in up to 27,900 European-ancestry women from the MODE/BCAC consortia. RESULTS We identified 28 genome-wide significant loci for MD phenotypes, including nine novel signals (5q11.2, 5q14.1, 5q31.1, 5q33.3, 5q35.1, 7p11.2, 8q24.13, 12p11.2, 16q12.2). Further, 45% of all known breast cancer SNPs were associated with at least one MD phenotype at p < 0.05. TWAS further identified two novel genes (SHOX2 and CRISPLD2) whose genetically predicted expression was significantly associated with MD phenotypes. CONCLUSIONS Our findings provided novel insight into the genetic background of MD phenotypes, and further demonstrated their shared genetic basis with breast cancer.
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Affiliation(s)
- Hongjie Chen
- grid.34477.330000000122986657Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Ave NE, Box 351619, Seattle, WA 98195 USA
| | - Shaoqi Fan
- grid.48336.3a0000 0004 1936 8075Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Jennifer Stone
- grid.1012.20000 0004 1936 7910School of Population and Global Health, University of Western Australia, Crawley, Australia
| | - Deborah J. Thompson
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Julie Douglas
- grid.214458.e0000000086837370Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI USA ,grid.60094.3b0000 0001 2270 6467Department of Mathematics and Statistics, Skidmore College, Saratoga Springs, NY USA
| | - Shuai Li
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK ,grid.1008.90000 0001 2179 088XCentre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC Australia ,grid.1002.30000 0004 1936 7857Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC Australia
| | - Christopher Scott
- grid.66875.3a0000 0004 0459 167XDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Manjeet K. Bolla
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Qin Wang
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- grid.417705.00000 0004 0609 0940Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus ,grid.417705.00000 0004 0609 0940Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - Christopher Li
- grid.34477.330000000122986657Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Ave NE, Box 351619, Seattle, WA 98195 USA ,grid.270240.30000 0001 2180 1622Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - Ulrike Peters
- grid.34477.330000000122986657Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Ave NE, Box 351619, Seattle, WA 98195 USA ,grid.270240.30000 0001 2180 1622Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA USA
| | - John L. Hopper
- grid.1008.90000 0001 2179 088XCentre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC Australia
| | - Melissa C. Southey
- grid.1002.30000 0004 1936 7857Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC Australia
| | - Tu Nguyen-Dumont
- grid.1002.30000 0004 1936 7857Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC Australia
| | - Tuong L. Nguyen
- grid.1008.90000 0001 2179 088XCentre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC Australia
| | - Peter A. Fasching
- grid.411668.c0000 0000 9935 6525Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Annika Behrens
- grid.411668.c0000 0000 9935 6525Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Gemma Cadby
- grid.1012.20000 0004 1936 7910School of Population and Global Health, University of Western Australia, Crawley, Australia
| | - Rachel A. Murphy
- grid.17091.3e0000 0001 2288 9830Cancer Control Research, BC Cancer and School of Population and Public Health, University of British Columbia, Vancouver, Canada
| | - Kristan Aronson
- grid.410356.50000 0004 1936 8331Public Health Sciences, Queen’s University, Kingston, Canada
| | - Anthony Howell
- grid.5379.80000000121662407Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Susan Astley
- grid.5379.80000000121662407Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
| | - Fergus Couch
- grid.66875.3a0000 0004 0459 167XDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Janet Olson
- grid.66875.3a0000 0004 0459 167XDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Roger L. Milne
- grid.1008.90000 0001 2179 088XCentre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC Australia ,grid.1002.30000 0004 1936 7857Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC Australia ,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC Australia
| | - Graham G. Giles
- grid.1008.90000 0001 2179 088XCentre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC Australia ,grid.5379.80000000121662407Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK ,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC Australia
| | - Christopher A. Haiman
- grid.42505.360000 0001 2156 6853Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA USA
| | - Gertraud Maskarinec
- grid.410445.00000 0001 2188 0957Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI USA
| | - Stacey Winham
- grid.66875.3a0000 0004 0459 167XDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Esther M. John
- grid.168010.e0000000419368956Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Allison Kurian
- grid.168010.e0000000419368956Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Department of Medicine (Oncology), Stanford University School of Medicine, Stanford, CA USA ,grid.168010.e0000000419368956Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA USA
| | - Heather Eliassen
- grid.38142.3c000000041936754XDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA ,grid.62560.370000 0004 0378 8294Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
| | - Irene Andrulis
- grid.250674.20000 0004 0626 6184Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Canada ,grid.17063.330000 0001 2157 2938Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - D. Gareth Evans
- grid.5379.80000000121662407Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK ,grid.462482.e0000 0004 0417 0074Genomic Medicine, St Mary’s Hospital, Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK ,grid.498924.a0000 0004 0430 9101NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - William G. Newman
- grid.5379.80000000121662407Division of Evolution and Genomic Medicine, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - Per Hall
- grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Anthony Swerdlow
- grid.18886.3fDivision of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Michael Jones
- grid.18886.3fDivision of Genetics and Epidemiology, The Institute of Cancer Research, London, UK
| | - Marina Pollan
- grid.413448.e0000 0000 9314 1427Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, Spain
| | - Pablo Fernandez-Navarro
- grid.413448.e0000 0000 9314 1427Cancer and Environmental Epidemiology Unit, National Center for Epidemiology, Carlos III Institute of Health, Madrid, Spain
| | - Daniel S. McConnell
- grid.214458.e0000000086837370Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI USA
| | - Vessela N. Kristensen
- grid.55325.340000 0004 0389 8485Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway
| | | | - Joseph H. Rothstein
- grid.59734.3c0000 0001 0670 2351Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Pei Wang
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Laurel A. Habel
- grid.280062.e0000 0000 9957 7758Division of Research, Kaiser Permanente Northern California, Oakland, CA USA
| | - Weiva Sieh
- grid.59734.3c0000 0001 0670 2351Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Alison M. Dunning
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul D. P. Pharoah
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Douglas F. Easton
- grid.5335.00000000121885934Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Gretchen L. Gierach
- grid.48336.3a0000 0004 1936 8075Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD USA
| | - Rulla M. Tamimi
- grid.5386.8000000041936877XDivision of Epidemiology, Population Health Science, Weill Cornell Medicine, New York, NY USA
| | - Celine M. Vachon
- grid.66875.3a0000 0004 0459 167XDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Sara Lindström
- grid.34477.330000000122986657Department of Epidemiology, School of Public Health, University of Washington, 3980 15th Ave NE, Box 351619, Seattle, WA 98195 USA ,grid.270240.30000 0001 2180 1622Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA USA
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17
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Kim S, Tran TXM, Song H, Park B. Microcalcifications, mammographic breast density, and risk of breast cancer: a cohort study. Breast Cancer Res 2022; 24:96. [PMID: 36544167 PMCID: PMC9773568 DOI: 10.1186/s13058-022-01594-0] [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: 04/13/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Breast density and microcalcifications are strongly associated with the risk of breast cancer. However, few studies have evaluated the combined association between these two factors and breast cancer risk. We investigated the association between breast density, microcalcifications, and risk of breast cancer. METHODS This cohort study included 3,910,815 women aged 40-74 years who were screened for breast cancer between 2009 and 2010 and followed up until 2020. The National Health Insurance Service database includes national health-screening results from the national breast cancer screening program, which is an organized screening program provided every 2 years for all women aged 40 years or older. Breast density was assessed based on the Breast Imaging Reporting and Data System (BI-RADS) 4th edition, mostly through visual assessment by radiologists. The presence or absence of microcalcifications was obtained from the mammographic results. Cox proportional hazard regression for breast cancer risk was used to estimate hazard ratios (aHRs) adjusted for breast cancer risk factors. RESULTS A total of 58,315 women developed breast cancer during a median follow-up of 10.8 years. Women with breast cancer had a higher proportion of microcalcifications than women without breast cancer (0.9% vs. 0.3%). After adjusting for breast density, women with microcalcification had a 3.07-fold (95% confidence interval [CI] 2.82-3.35) increased risk of breast cancer compared to women without microcalcification. The combined association between microcalcification and breast density dramatically increased the risk of breast cancer, corresponding to a higher level of breast density. Among postmenopausal women, the highest risk group was women with BI-RADS 4 and microcalcification. These women had more than a sevenfold higher risk than women with BI-RADS 1 and non-microcalcification (aHR, 7.26; 95% CI 5.01-10.53). CONCLUSION Microcalcification is an independent risk factor for breast cancer, and its risk is elevated when combined with breast density.
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Affiliation(s)
- Soyeoun Kim
- grid.49606.3d0000 0001 1364 9317Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Thi Xuan Mai Tran
- grid.49606.3d0000 0001 1364 9317Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Huiyeon Song
- grid.49606.3d0000 0001 1364 9317Department of Epidemiology and Biostatistics, Graduate School of Public Health, Hanyang University, Seoul, Republic of Korea
| | - Boyoung Park
- grid.49606.3d0000 0001 1364 9317Department of Preventive Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
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18
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Grassmann F, Yang H, Eriksson M, Azam S, Hall P, Czene K. Mammographic features are associated with cardiometabolic disease risk and mortality. Eur Heart J 2021; 42:3361-3370. [PMID: 34338750 PMCID: PMC8423470 DOI: 10.1093/eurheartj/ehab502] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/01/2021] [Accepted: 07/15/2021] [Indexed: 01/03/2023] Open
Abstract
Aims In recent years, microcalcifications identified in routine mammograms were found to be associated with cardiometabolic disease in women. Here, we aimed to systematically evaluate the association of microcalcifications and other mammographic features with cardiometabolic disease risk and mortality in a large screening cohort and to understand a potential genetic contribution. Methods and results This study included 57 867 women from a prospective mammographic screening cohort in Sweden (KARMA) and 49 583 sisters. Cardiometabolic disease diagnoses and mortality and medication were extracted by linkage to Swedish population registries with virtually no missing data. In the cardiometabolic phenome-wide association study, we found that a higher number of microcalcifications were associated with increased risk for multiple cardiometabolic diseases, particularly in women with pre-existing cardiometabolic diseases. In contrast, dense breasts were associated with a lower incidence of cardiometabolic diseases. Importantly, we observed similar associations in sisters of KARMA women, indicating a potential genetic overlap between mammographic features and cardiometabolic traits. Finally, we observed that the presence of microcalcifications was associated with increased cardiometabolic mortality in women with pre-existing cardiometabolic diseases (hazard ratio and 95% confidence interval: 1.79 [1.24–2.58], P = 0.002) while we did not find such effects in women without cardiometabolic diseases. Conclusions We found that mammographic features are associated with cardiometabolic risk and mortality. Our results strengthen the notion that a combination of mammographic features and other breast cancer risk factors could be a novel and affordable tool to assess cardiometabolic health in women attending mammographic screening.
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Affiliation(s)
- Felix Grassmann
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Stockholm 171 65, Sweden.,Institute of Medical Sciences, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, UK
| | - Haomin Yang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Stockholm 171 65, Sweden.,Department of Epidemiology and Health Statistics, The School of Public Health, Fujian Medical University, Xuefu North Road 1, University Town, Fuzhou 350122, China
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Stockholm 171 65, Sweden
| | - Shadi Azam
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Stockholm 171 65, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Stockholm 171 65, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, Stockholm 171 65, Sweden
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19
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His M, Lajous M, Gómez-Flores-Ramos L, Monge A, Dossus L, Viallon V, Gicquiau A, Biessy C, Gunter MJ, Rinaldi S. Biomarkers of mammographic density in premenopausal women. Breast Cancer Res 2021; 23:75. [PMID: 34301304 PMCID: PMC8305592 DOI: 10.1186/s13058-021-01454-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 07/12/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND While mammographic density is one of the strongest risk factors for breast cancer, little is known about its determinants, especially in young women. We applied targeted metabolomics to identify circulating metabolites specifically associated with mammographic density in premenopausal women. Then, we aimed to identify potential correlates of these biomarkers to guide future research on potential modifiable determinants of mammographic density. METHODS A total of 132 metabolites (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids, hexose) were measured by tandem liquid chromatography/mass spectrometry in plasma samples from 573 premenopausal participants in the Mexican Teachers' Cohort. Associations between metabolites and percent mammographic density were assessed using linear regression models, adjusting for breast cancer risk factors and accounting for multiple tests. Mean concentrations of metabolites associated with percent mammographic density were estimated across levels of several lifestyle and metabolic factors. RESULTS Sphingomyelin (SM) C16:1 and phosphatidylcholine (PC) ae C30:2 were inversely associated with percent mammographic density after correction for multiple tests. Linear trends with percent mammographic density were observed for SM C16:1 only in women with body mass index (BMI) below the median (27.4) and for PC ae C30:2 in women with a BMI over the median. SM C16:1 and PC ae C30:2 concentrations were positively associated with cholesterol (total and HDL) and inversely associated with number of metabolic syndrome components. CONCLUSIONS We identified new biomarkers associated with mammographic density in young women. The association of these biomarkers with mammographic density and metabolic parameters may provide new perspectives to support future preventive actions for breast cancer.
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Affiliation(s)
- Mathilde His
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, CEDEX 08, 69372, Lyon, France
| | - Martin Lajous
- Center for Research on Population Health, National Institute of Public Health, 62100, Cuernavaca, México.
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA.
| | - Liliana Gómez-Flores-Ramos
- Center for Research on Population Health, National Institute of Public Health, 62100, Cuernavaca, México
- Cátedras-CONACYT, Mexico City, Mexico
| | - Adriana Monge
- Center for Research on Population Health, National Institute of Public Health, 62100, Cuernavaca, México
| | - Laure Dossus
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, CEDEX 08, 69372, Lyon, France
| | - Vivian Viallon
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, CEDEX 08, 69372, Lyon, France
| | - Audrey Gicquiau
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, CEDEX 08, 69372, Lyon, France
| | - Carine Biessy
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, CEDEX 08, 69372, Lyon, France
| | - Marc J Gunter
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, CEDEX 08, 69372, Lyon, France
| | - Sabina Rinaldi
- International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, CEDEX 08, 69372, Lyon, France
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20
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Azam S, Eriksson M, Sjölander A, Gabrielson M, Hellgren R, Czene K, Hall P. Predictors of mammographic microcalcifications. Int J Cancer 2021; 148:1132-1143. [PMID: 32949149 PMCID: PMC7821182 DOI: 10.1002/ijc.33302] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/28/2020] [Accepted: 09/08/2020] [Indexed: 12/31/2022]
Abstract
We examined the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry. A cohort study of 53 273 Swedish women aged 30 to 80 years, with comprehensive information on breast cancer risk factors and mammograms, was conducted. Total number of microcalcification clusters and the average mammographic density area were measured using a Computer Aided Detection system and the STRATUS method, respectively. A polygenic risk score for breast cancer, including 313 single nucleotide polymorphisms, was calculated for those women genotyped (N = 7387). Odds ratios (ORs) and 95% confidence intervals (CIs), with adjustment for potential confounders, were estimated. Age was strongly associated with microcalcification clusters. Both high mammographic density (>40 cm2 ), and high polygenic risk score (80-100 percentile) were associated with microcalcification clusters, OR = 2.08 (95% CI = 1.93-2.25) and OR = 1.22 (95% CI = 1.06-1.48), respectively. Among reproductive risk factors, life-time breastfeeding duration >1 year was associated with microcalcification clusters OR = 1.22 (95% CI = 1.03-1.46). The association was confined to postmenopausal women. Among lifestyle risk factors, women with a body mass index ≥30 kg/m2 had the lowest risk of microcalcification clusters OR = 0.79 (95% CI = 0.73-0.85) and the association was stronger among premenopausal women. Our results suggest that age, mammographic density, genetic predictors of breast cancer, having more than two children, longer duration of breast-feeding are significantly associated with increased risk of microcalcification clusters. However, most lifestyle risk factors for breast cancer seem to protect against presence of microcalcification clusters. More research is needed to study biological mechanisms behind microcalcifications formation.
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Affiliation(s)
- Shadi Azam
- Department of Medical Epidemiology and BiostatisticsKarolinska InstituteStockholmSweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and BiostatisticsKarolinska InstituteStockholmSweden
| | - Arvid Sjölander
- Department of Medical Epidemiology and BiostatisticsKarolinska InstituteStockholmSweden
| | - Marike Gabrielson
- Department of Medical Epidemiology and BiostatisticsKarolinska InstituteStockholmSweden
| | - Roxanna Hellgren
- Department of Medical Epidemiology and BiostatisticsKarolinska InstituteStockholmSweden
- Department of MammographySouth General HospitalStockholmSweden
| | - Kamila Czene
- Department of Medical Epidemiology and BiostatisticsKarolinska InstituteStockholmSweden
| | - Per Hall
- Department of Medical Epidemiology and BiostatisticsKarolinska InstituteStockholmSweden
- Department of OncologySouth General HospitalStockholmSweden
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21
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Nykänen A, Okuma H, Sutela A, Masarwah A, Vanninen R, Sudah M. The mammographic breast density distribution of Finnish women with breast cancer and comparison of breast density reporting using the 4 th and 5 th editions of the Breast Imaging-Reporting and Data System. Eur J Radiol 2021; 137:109585. [PMID: 33607373 DOI: 10.1016/j.ejrad.2021.109585] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/24/2021] [Accepted: 02/03/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE To examine the breast density distribution in patients diagnosed with breast cancer in an eastern Finnish population and to examine the changes in breast density reporting patterns between the 4th and 5th editions of the Breast Imaging-Reporting and Data System (BI-RADS). METHOD 821 women (mean age 62.8 ± 12.2 years, range 28-94 years) with breast cancer were included in this retrospective study and their digital mammographic examinations were assessed semi-automatically and then visually by two radiologists in accordance with the 4th and 5th editions of the BI-RADS. Intraclass correlation coefficients (ICCs) were used to evaluate interobserver reproducibility. Chi-square tests were used to examine the associations between the breast density distribution and age or body mass index (BMI). RESULTS Interobserver reproducibility of the visual assessment was excellent, with an ICCr = 0.93. The majority of breast cancers occurred in fatty breasts (93.8 %) when density was assessed according to the 4th edition of the BI-RADS. The distributions remained constant after correction for age and BMI. Using the 5th edition, there was an overall 50.2 % decrease in almost entirely fatty (p < 0.001), 19.4 % increase in scattered fibroglandular (p < 0.001), 28.7 % increase in heterogeneously dense (p < 0.001), and 2.1 % increase in extremely dense (p < 0.001) categories. CONCLUSIONS Most breast cancers in eastern Finland occur in fatty breasts with an area density of < 50 %. Assessing breast density using the 5th edition of the BI-RADS greatly increased denser assessments.
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Affiliation(s)
- Aki Nykänen
- Kuopio University Hospital, Diagnostic Imaging Centre, Department of Clinical Radiology, Puijonlaaksontie 2, 70210 Kuopio, Finland; University of Eastern Finland, Institute of Clinical Medicine, School of Medicine, Yliopistonranta 1, 70210 Kuopio, Finland.
| | - Hidemi Okuma
- Kuopio University Hospital, Diagnostic Imaging Centre, Department of Clinical Radiology, Puijonlaaksontie 2, 70210 Kuopio, Finland
| | - Anna Sutela
- Kuopio University Hospital, Diagnostic Imaging Centre, Department of Clinical Radiology, Puijonlaaksontie 2, 70210 Kuopio, Finland
| | - Amro Masarwah
- Kuopio University Hospital, Diagnostic Imaging Centre, Department of Clinical Radiology, Puijonlaaksontie 2, 70210 Kuopio, Finland
| | - Ritva Vanninen
- Kuopio University Hospital, Diagnostic Imaging Centre, Department of Clinical Radiology, Puijonlaaksontie 2, 70210 Kuopio, Finland; University of Eastern Finland, Institute of Clinical Medicine, School of Medicine, Yliopistonranta 1, 70210 Kuopio, Finland; University of Eastern Finland, Cancer Center of Eastern Finland, Yliopistonranta 1, 70210 Kuopio, Finland
| | - Mazen Sudah
- Kuopio University Hospital, Diagnostic Imaging Centre, Department of Clinical Radiology, Puijonlaaksontie 2, 70210 Kuopio, Finland
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22
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Evans DG, van Veen EM, Howell A, Astley S. Heritability of mammographic breast density. Quant Imaging Med Surg 2020; 10:2387-2391. [PMID: 33269237 DOI: 10.21037/qims-2020-20] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- D Gareth Evans
- Clinical Genetics Service, Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, UK.,NW Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, UK.,Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.,Prevent Breast Cancer Centre, Wythenshawe Hospital Manchester Universities Foundation Trust, Wythenshawe, Manchester, UK.,Manchester Breast Centre, The Christie Hospital, Manchester, UK
| | - Elke M van Veen
- NW Genomic Laboratory Hub, Manchester Centre for Genomic Medicine, Manchester University Hospitals NHS Foundation Trust, Manchester, UK.,Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Anthony Howell
- Prevent Breast Cancer Centre, Wythenshawe Hospital Manchester Universities Foundation Trust, Wythenshawe, Manchester, UK.,Manchester Breast Centre, The Christie Hospital, Manchester, UK
| | - Susan Astley
- Prevent Breast Cancer Centre, Wythenshawe Hospital Manchester Universities Foundation Trust, Wythenshawe, Manchester, UK.,Manchester Breast Centre, The Christie Hospital, Manchester, UK.,Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
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