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Kusumaningtyas N, Supit NISH, Murtala B, Muis M, Chandra M, Sanjaya E, Octavius GS. A systematic review and meta-analysis of correlation of automated breast density measurement. Radiography (Lond) 2024:S1078-8174(24)00200-1. [PMID: 39164186 DOI: 10.1016/j.radi.2024.08.003] [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: 06/20/2024] [Revised: 07/22/2024] [Accepted: 08/05/2024] [Indexed: 08/22/2024]
Abstract
INTRODUCTION Breast cancer is the most common cancer in women and a leading cause of mortality. This systematic review and meta-analysis aims to evaluate the correlation between breast density measurements obtained from various software and visual assessments by radiologists using full-field digital mammography (FFDM). METHODS Following the PRISMA 2020 guidelines, five databases (Pubmed, Google Scholar, Science Direct, Cochrane Library, and MEDLINE) were searched for studies correlating volumetric breast density with breast cancer risk. The Newcastle-Ottawa Scale and the Joanna Briggs Institute Checklist were used to assess the quality of the included studies. Meta-analysis of correlation was applied to aggregate correlation coefficients using a random-effects model using MedCalc Statistical Software version 19.2.6. RESULTS The review included 22 studies with a total of 58,491 women. The pooled correlation coefficient for volumetric breast density amongst Volpara™ and Quantra™ was found to be 0.755 (95% CI 0.496-0.890, p < 0.001), indicating a high positive correlation, albeit with a significant heterogeneity (I2 = 99.89%, p < 0.0001). Subgroup analyses based on study origin, quality, and methodology were performed but did not reveal the heterogeneity cause. Egger's and Begg's tests showed no significant publication bias. CONCLUSION Volumetric breast density is strongly correlated with breast cancer risk, underscoring the importance of accurate breast density assessment in screening programs. Automated volumetric measurement tools like Volpara™ and Quantra™ provide reliable assessments, potentially improving breast cancer risk prediction and management. IMPLICATIONS FOR PRACTICE Implementing fully automated breast density assessment tools could enhance consistency in clinical practice, minimizing observer variability and improving screening accuracy. These tools should be further validated against standardized criteria to ensure reliability in diverse clinical settings.
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Affiliation(s)
- N Kusumaningtyas
- Woman Imaging Division, Department of Radiology, Faculty of Universitas Pelita Harapan, Tangerang, Indonesia; Department of Radiology of Siloam Hospital MRCCC, Jakarta, Indonesia.
| | - N I S H Supit
- Woman Imaging Division, Department of Radiology, Faculty of Universitas Pelita Harapan, Tangerang, Indonesia; Department of Radiology of Siloam Hospital MRCCC, Jakarta, Indonesia
| | - B Murtala
- Department of Radiology of Universitas Hasanuddin, South Sulawesi, Makassar, Indonesia
| | - M Muis
- Department of Radiology of Universitas Hasanuddin, South Sulawesi, Makassar, Indonesia
| | - M Chandra
- Radiology Resident, Faculty of Universitas Pelita Harapan, Tangerang, Indonesia
| | - E Sanjaya
- Radiology Resident, Faculty of Universitas Pelita Harapan, Tangerang, Indonesia
| | - G S Octavius
- Radiology Resident, Faculty of Universitas Pelita Harapan, Tangerang, Indonesia
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Haas CB, Chen H, Harrison T, Fan S, Gago-Dominguez M, Castelao JE, Bolla MK, Wang Q, Dennis J, Michailidou K, Dunning AM, Easton DF, Antoniou AC, Hall P, Czene K, Andrulis IL, Mulligan AM, Milne RL, Fasching PA, Haeberle L, Garcia-Closas M, Ahearn T, Gierach GL, Haiman C, Maskarinec G, Couch FJ, Olson JE, John EM, Chenevix-Trench G, Berrington de Gonzalez A, Jones M, Stone J, Murphy R, Aronson KJ, Wernli KJ, Hsu L, Vachon C, Tamimi RM, Lindström S. Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization. Breast Cancer Res Treat 2024; 206:295-305. [PMID: 38653906 DOI: 10.1007/s10549-024-07306-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: 10/19/2023] [Accepted: 02/28/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE Mammographic density phenotypes, adjusted for age and body mass index (BMI), are strong predictors of breast cancer risk. BMI is associated with mammographic density measures, but the role of circulating sex hormone concentrations is less clear. We investigated the relationship between BMI, circulating sex hormone concentrations, and mammographic density phenotypes using Mendelian randomization (MR). METHODS We applied two-sample MR approaches to assess the association between genetically predicted circulating concentrations of sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG)], BMI, and mammographic density phenotypes (dense and non-dense area). We created instrumental variables from large European ancestry-based genome-wide association studies and applied estimates to mammographic density phenotypes in up to 14,000 women of European ancestry. We performed analyses overall and by menopausal status. RESULTS Genetically predicted BMI was positively associated with non-dense area (IVW: β = 1.79; 95% CI = 1.58, 2.00; p = 9.57 × 10-63) and inversely associated with dense area (IVW: β = - 0.37; 95% CI = - 0.51,- 0.23; p = 4.7 × 10-7). We observed weak evidence for an association of circulating sex hormone concentrations with mammographic density phenotypes, specifically inverse associations between genetically predicted testosterone concentration and dense area (β = - 0.22; 95% CI = - 0.38, - 0.053; p = 0.009) and between genetically predicted estradiol concentration and non-dense area (β = - 3.32; 95% CI = - 5.83, - 0.82; p = 0.009), although results were not consistent across a range of MR approaches. CONCLUSION Our findings support a positive causal association between BMI and mammographic non-dense area and an inverse association between BMI and dense area. Evidence was weaker and inconsistent for a causal effect of circulating sex hormone concentrations on mammographic density phenotypes. Based on our findings, associations between circulating sex hormone concentrations and mammographic density phenotypes are weak at best.
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Affiliation(s)
- Cameron B Haas
- Department of Epidemiology, University of Washington, Seattle, WA, USA.
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Hongjie Chen
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Tabitha Harrison
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shaoqi Fan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Manuela Gago-Dominguez
- Health Research Institute of Santiago de Compostela Foundation (FIDIS), SERGAS, Cancer Genetics and Epidemiology Group, Santiago, Spain
| | - Jose E Castelao
- Unidad de Oncología Genética, Instituto de Investigación Sanitaria, Galicia Sur, Vigo, Spain
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, Nicosia, Cyprus
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Antonis C Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, 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
| | - Irene L Andrulis
- Fred A. Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
| | - Anna Marie Mulligan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Laboratory Medicine Program, University Health Network, Toronto, Canada
| | - Roger L Milne
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
- Prevision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Lothar Haeberle
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Montserrat Garcia-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Gretchen L Gierach
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Gertraud Maskarinec
- Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, Honolulu, HI, USA
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Janet E Olson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Esther M John
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Division of Oncology, Department of Medicine, Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Geogia Chenevix-Trench
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | | | - Michael Jones
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, WA, Australia
- Division of Cancer Medicine, Peter MacCallum Cancer Centre, The University of Melbourne, Melbourne, VIC, Australia
| | - Rachel Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
- Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada
| | - Kristan J Aronson
- Division of Cancer Care and Epidemiology, Department of Community Health and Epidemiology, Queen's University, Kingston, ON, K7L3N6, Canada
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Li Hsu
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Celine Vachon
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Sara Lindström
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
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Chen S, Tamimi RM, Colditz GA, Jiang S. Association and Prediction Utilizing Craniocaudal and Mediolateral Oblique View Digital Mammography and Long-Term Breast Cancer Risk. Cancer Prev Res (Phila) 2023; 16:531-537. [PMID: 37428020 PMCID: PMC10472097 DOI: 10.1158/1940-6207.capr-22-0499] [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: 11/13/2022] [Revised: 04/19/2023] [Accepted: 06/30/2023] [Indexed: 07/11/2023]
Abstract
Mammographic percentage of volumetric density is an important risk factor for breast cancer. Epidemiology studies historically used film images often limited to craniocaudal (CC) views to estimate area-based breast density. More recent studies using digital mammography images typically use the averaged density between craniocaudal (CC) and mediolateral oblique (MLO) view mammography for 5- and 10-year risk prediction. The performance in using either and both mammogram views has not been well-investigated. We use 3,804 full-field digital mammograms from the Joanne Knight Breast Health Cohort (294 incident cases and 657 controls), to quantity the association between volumetric percentage of density extracted from either and both mammography views and to assess the 5 and 10-year breast cancer risk prediction performance. Our results show that the association between percent volumetric density from CC, MLO, and the average between the two, retain essentially the same association with breast cancer risk. The 5- and 10-year risk prediction also shows similar prediction accuracy. Thus, one view is sufficient to assess association and predict future risk of breast cancer over a 5 or 10-year interval. PREVENTION RELEVANCE Expanding use of digital mammography and repeated screening provides opportunities for risk assessment. To use these images for risk estimates and guide risk management in real time requires efficient processing. Evaluating the contribution of different views to prediction performance can guide future applications for risk management in routine care.
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Affiliation(s)
- Simin Chen
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Rulla M. Tamimi
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York
| | - Graham A. Colditz
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri
- Alvin J. Siteman Cancer Center, Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri
| | - Shu Jiang
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, Missouri
- Alvin J. Siteman Cancer Center, Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri
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Habel LA, Alexeeff SE, Achacoso N, Arasu VA, Gastounioti A, Gerstley L, Klein RJ, Liang RY, Lipson JA, Mankowski W, Margolies LR, Rothstein JH, Rubin DL, Shen L, Sistig A, Song X, Villaseñor MA, Westley M, Whittemore AS, Yaffe MJ, Wang P, Kontos D, Sieh W. Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women. Breast Cancer Res 2023; 25:92. [PMID: 37544983 PMCID: PMC10405373 DOI: 10.1186/s13058-023-01685-6] [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: 04/05/2023] [Accepted: 07/09/2023] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Breast density is strongly associated with breast cancer risk. Fully automated quantitative density assessment methods have recently been developed that could facilitate large-scale studies, although data on associations with long-term breast cancer risk are limited. We examined LIBRA assessments and breast cancer risk and compared results to prior assessments using Cumulus, an established computer-assisted method requiring manual thresholding. METHODS We conducted a cohort study among 21,150 non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were 40-74 years at enrollment, followed for up to 10 years, and had archived processed screening mammograms acquired on Hologic or General Electric full-field digital mammography (FFDM) machines and prior Cumulus density assessments available for analysis. Dense area (DA), non-dense area (NDA), and percent density (PD) were assessed using LIBRA software. Cox regression was used to estimate hazard ratios (HRs) for breast cancer associated with DA, NDA and PD modeled continuously in standard deviation (SD) increments, adjusting for age, mammogram year, body mass index, parity, first-degree family history of breast cancer, and menopausal hormone use. We also examined differences by machine type and breast view. RESULTS The adjusted HRs for breast cancer associated with each SD increment of DA, NDA and PD were 1.36 (95% confidence interval, 1.18-1.57), 0.85 (0.77-0.93) and 1.44 (1.26-1.66) for LIBRA and 1.44 (1.33-1.55), 0.81 (0.74-0.89) and 1.54 (1.34-1.77) for Cumulus, respectively. LIBRA results were generally similar by machine type and breast view, although associations were strongest for Hologic machines and mediolateral oblique views. Results were also similar during the first 2 years, 2-5 years and 5-10 years after the baseline mammogram. CONCLUSION Associations with breast cancer risk were generally similar for LIBRA and Cumulus density measures and were sustained for up to 10 years. These findings support the suitability of fully automated LIBRA assessments on processed FFDM images for large-scale research on breast density and cancer risk.
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Affiliation(s)
- Laurel A Habel
- Division of Research, Kaiser Permanente Northern California, CA, Oakland, USA.
| | - Stacey E Alexeeff
- Division of Research, Kaiser Permanente Northern California, CA, Oakland, USA
| | - Ninah Achacoso
- Division of Research, Kaiser Permanente Northern California, CA, Oakland, USA
| | - Vignesh A Arasu
- Division of Research, Kaiser Permanente Northern California, CA, Oakland, USA
- Department of Radiology, Kaiser Permanente Northern California, Vallejo, CA, USA
| | - Aimilia Gastounioti
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Lawrence Gerstley
- Division of Research, Kaiser Permanente Northern California, CA, Oakland, USA
| | - Robert J Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rhea Y Liang
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jafi A Lipson
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Walter Mankowski
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Laurie R Margolies
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph H Rothstein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Daniel L Rubin
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Li Shen
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, NY, New York, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adriana Sistig
- Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, NY, New York, USA
| | - Xiaoyu Song
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Mark Westley
- Division of Research, Kaiser Permanente Northern California, CA, Oakland, USA
| | - Alice S Whittemore
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Martin J Yaffe
- Sunnybrook Research Institute and Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Weiva Sieh
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Epidemiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Gastounioti A, Cohen EA, Pantalone L, Ehsan S, Vasudevan S, Kurudi A, Conant EF, Chen J, Kontos D, McCarthy AM. Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening. Breast Cancer Res Treat 2023; 198:535-544. [PMID: 36800118 DOI: 10.1007/s10549-023-06879-2] [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: 06/18/2022] [Accepted: 02/01/2023] [Indexed: 02/18/2023]
Abstract
PURPOSE Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort. MATERIALS AND METHODS We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7 years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman. RESULTS PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical. CONCLUSIONS Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk.
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Affiliation(s)
- Aimilia Gastounioti
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric A Cohen
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren Pantalone
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Ehsan
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sanjana Vasudevan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Avinash Kurudi
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne Marie McCarthy
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Gastounioti A, Pantalone L, Scott CG, Cohen EA, Wu FF, Winham SJ, Jensen MR, Maidment ADA, Vachon CM, Conant EF, Kontos D. Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis. Radiology 2021; 301:561-568. [PMID: 34519572 PMCID: PMC8608738 DOI: 10.1148/radiol.2021210190] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.
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Affiliation(s)
- Aimilia Gastounioti
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Lauren Pantalone
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Christopher G Scott
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Eric A Cohen
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Fang F Wu
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Stacey J Winham
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Matthew R Jensen
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Andrew D A Maidment
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Celine M Vachon
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Emily F Conant
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
| | - Despina Kontos
- From the Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Richards Bldg, Room D702, Philadelphia, PA 19104 (A.G., L.P., E.A.C., A.D.A.M., E.F.C., D.K.); and the Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minn (C.G.S., F.F.W., S.J.W., M.R.J., C.M.V.)
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Kanbayti IH, Rae WID, McEntee MF, Al-Foheidi M, Ashour S, Turson SA, Ekpo EU. Is mammographic density a marker of breast cancer phenotypes? Cancer Causes Control 2020; 31:749-765. [PMID: 32410205 DOI: 10.1007/s10552-020-01316-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 05/05/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE To investigate the association between mammographic density (MD) phenotypes and both clinicopathologic features of breast cancer (BC) and tumor location. METHODS MD was measured for 297 BC-affected females using qualitative (visual method) and quantitative (fully automated area-based method) approaches. Radiologists' description, visible external markers, and surgical scar were used to establish the location of tumors. Binary logistic regression models were used to assess the association between MD phenotypes and BC clinicopathologic features. RESULTS Categorical and numerical MD measures showed no association with clinicopathologic features of BC (p > 0.05). Participants with higher BI-RADS scores [(51-75% glandular) and (> 75% glandular)] (p < 0.001), and percent density (PD) categories [PD (21-49%) and PD ≥ 50%] (p = 0.01) were more likely to have tumors emanating from dense areas. Additionally, tumors were commonly found in dense regions of the breast among patients with higher medians of PD (p = 0.001), dense area (DA) (p = 0.02), and lower medians of non-dense area (NDA) (p < 0.001). Adjusted logistic regression models showed that high BI-RADS density (> 75% glandular) has an almost fivefold increased odds of tumors developing within dense areas (OR 4.99, 95% CI 0.93-25.9; p = 0.05. PD (OR 1.02, 95% CI 1-1.03, p = 0.002) and NDA (OR 0.99, 95% CI 0.991-0.997, p < 0.001) had very small effect on tumor location. Compared to tumors within non-dense areas, tumors in dense areas tended to exhibit human epidermal growth factor receptor 2 positive (p = 0.05) and carcinoma in situ (p = 0.01) characteristics. CONCLUSION MD shows no significant association with clinicopathologic features of BC. However, BC was more likely to originate from dense tissue, with tumors in dense regions having human epidermal growth receptor 2 positive and carcinoma in situ characteristics.
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Affiliation(s)
- Ibrahem H Kanbayti
- Diagnostic Radiography Technology Department, Faculty of Applied Medical Sciences, King Abdul-Aziz University, Jeddah, Saudi Arabia. .,Medical Image Optimisation and Perception Group (MIOPeG), Faculty of Medicine and Health, The University of Sydney, Sydney, Australia. .,Faculty of Health Science, University of Sydney, Cumberland Campus C42
- 75 East Street, Lidcombe, NSW, 2141, Australia.
| | - William I D Rae
- Medical Image Optimisation and Perception Group (MIOPeG), Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - Mark F McEntee
- Medical Image Optimisation and Perception Group (MIOPeG), Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.,Department of Medicine Roinn na Sláinte, UG 12 Áras Watson
- Brookfield Health Sciences, Cork, T12 AK54, Ireland
| | - Meteb Al-Foheidi
- King Saud Bin Abdulaziz University for Health Science-National Guard Health Affairs, Jeddah, Saudi Arabia
| | - Sawsan Ashour
- Radiology Department, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
| | - Smeera A Turson
- Radiology Department, King Abdulaziz University Hospital, Jeddah, Saudi Arabia
| | - Ernest U Ekpo
- Medical Image Optimisation and Perception Group (MIOPeG), Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.,Orange Radiology, Laboratories and Research Centre, Calabar, Nigeria
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8
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Gastounioti A, Kasi CD, Scott CG, Brandt KR, Jensen MR, Hruska CB, Wu FF, Norman AD, Conant EF, Winham SJ, Kerlikowske K, Kontos D, Vachon CM. Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction. Radiology 2020; 296:24-31. [PMID: 32396041 DOI: 10.1148/radiol.2020192509] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD (r = 0.77-0.84) and Volpara VPD (r = 0.85-0.90) (P < .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; P = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; P = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; P = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Aimilia Gastounioti
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Christine Damases Kasi
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Christopher G Scott
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Kathleen R Brandt
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Matthew R Jensen
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Carrie B Hruska
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Fang F Wu
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Aaron D Norman
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Emily F Conant
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Stacey J Winham
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Karla Kerlikowske
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Despina Kontos
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Celine M Vachon
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
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9
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Alexeeff SE, Odo NU, McBride R, McGuire V, Achacoso N, Rothstein JH, Lipson JA, Liang RY, Acton L, Yaffe MJ, Whittemore AS, Rubin DL, Sieh W, Habel LA. Reproductive Factors and Mammographic Density: Associations Among 24,840 Women and Comparison of Studies Using Digitized Film-Screen Mammography and Full-Field Digital Mammography. Am J Epidemiol 2019; 188:1144-1154. [PMID: 30865217 DOI: 10.1093/aje/kwz033] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 01/29/2019] [Accepted: 02/04/2019] [Indexed: 11/14/2022] Open
Abstract
Breast density is a modifiable factor that is strongly associated with breast cancer risk. We sought to understand the influence of newer technologies of full-field digital mammography (FFDM) on breast density research and to determine whether results are comparable across studies using FFDM and previous studies using traditional film-screen mammography. We studied 24,840 screening-age (40-74 years) non-Hispanic white women who were participants in the Research Program on Genes, Environment and Health of Kaiser Permanente Northern California and underwent screening mammography with either Hologic (Hologic, Inc., Marlborough, Massachusetts) or General Electric (General Electric Company, Boston, Massachusetts) FFDM machines between 2003 and 2013. We estimated the associations of parity, age at first birth, age at menarche, and menopausal status with percent density and dense area as measured by a single radiological technologist using Cumulus software (Canto Software, Inc., San Francisco, California). We found that associations between reproductive factors and mammographic density measured using processed FFDM images were generally similar in magnitude and direction to those from prior studies using film mammography. Estimated associations for both types of FFDM machines were in the same direction. There was some evidence of heterogeneity in the magnitude of the effect sizes by machine type, which we accounted for using random-effects meta-analysis when combining results. Our findings demonstrate the robustness of quantitative mammographic density measurements across FFDM and film mammography platforms.
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Affiliation(s)
- Stacey E Alexeeff
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | | | - Russell McBride
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Valerie McGuire
- Department of Health Research and Policy, Division of Epidemiology, School of Medicine, Stanford University, Stanford, California
| | - Ninah Achacoso
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Joseph H Rothstein
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Jafi A Lipson
- Department of Radiology, School of Medicine, Stanford University, Stanford, California
| | - Rhea Y Liang
- Department of Radiology, School of Medicine, Stanford University, Stanford, California
| | - Luana Acton
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Martin J Yaffe
- Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Alice S Whittemore
- Department of Health Research and Policy, Division of Epidemiology, School of Medicine, Stanford University, Stanford, California
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California
| | - Daniel L Rubin
- Department of Radiology, School of Medicine, Stanford University, Stanford, California
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California
| | - Weiva Sieh
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Laurel A Habel
- Division of Research, Kaiser Permanente Northern California, Oakland, California
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10
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Albeshan SM, Hossain SZ, Mackey MG, Peat JK, Al Tahan FM, Brennan PC. Preliminary investigation of mammographic density among women in Riyadh: association with breast cancer risk factors and implications for screening practices. Clin Imaging 2019; 54:138-147. [PMID: 30639525 DOI: 10.1016/j.clinimag.2019.01.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 12/10/2018] [Accepted: 01/04/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE Improved mammographic sensitivity is associated with reduced mammographic density. This study aims to: provide a preliminary report on mammographic density among women in Riyadh; identify risk factors associated with mammographic density; and consider the potential implications for screening practices. METHODS Based on a cross-sectional design, we examined a total of 792 women using an area-based mammographic density method (LIBRA). Spearman's correlation, Mann-Whitney U, Kruskal-Wallis and binary logistic regression were used for analyses. RESULTS The study population had a mean age of 49.6 years and a high proportion of participants were overweight or obese (90%). A large number of women had low mammographic density, with a mean dense breast area of 19.1 cm2 and percent density of 10.3 cm2. Slightly more than half of the variations in the dense breast area and percent density models were explained by BMI. In the adjusted analyses, BMI, menopausal status, age at menarche and number of children remained statistically significant predictors. CONCLUSION Given the high proportion of women with low mammographic density, our findings suggest that women living in Riyadh may not require additional imaging strategies beyond mammography to detect breast cancers. The high proportion of obese women reported here and across Saudi Arabia suggests that mammographic density is less likely to have an adverse impact on mammographic sensitivity. Thus and to improve clinical outcomes among Saudi women, annual mammography and commencing screening at a younger age are suggested. Additional studies are required to shed further light on our findings.
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Affiliation(s)
- Salman M Albeshan
- Medical Radiation Sciences, Medical Image Optimization and Perception Group (MIOPeG), Australia; Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University (KSU), Saudi Arabia.
| | - Syeda Z Hossain
- Discipline of Behavioral and Social Sciences in Health, Australia
| | | | - Jennifer K Peat
- Medical Radiation Sciences, Medical Image Optimization and Perception Group (MIOPeG), Australia
| | | | - Patrick C Brennan
- Medical Radiation Sciences, Medical Image Optimization and Perception Group (MIOPeG), Australia
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11
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Knight JA, Blackmore KM, Fan J, Malone KE, John EM, Lynch CF, Vachon CM, Bernstein L, Brooks JD, Reiner AS, Liang X, Woods M, Bernstein JL. The association of mammographic density with risk of contralateral breast cancer and change in density with treatment in the WECARE study. Breast Cancer Res 2018; 20:23. [PMID: 29566728 PMCID: PMC5863854 DOI: 10.1186/s13058-018-0948-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Accepted: 02/26/2018] [Indexed: 12/25/2022] Open
Abstract
Background Mammographic density (MD) is an established predictor of risk of a first breast cancer, but the relationship of MD to contralateral breast cancer (CBC) risk is not clear, including the roles of age, mammogram timing, and change with treatment. Multivariable prediction models for CBC risk are needed and MD could contribute to these. Methods We conducted a case-control study of MD and CBC risk in phase II of the WECARE study where cases had a CBC diagnosed ≥ 2 years after first diagnosis at age <55 years and controls had unilateral breast cancer (UBC) with similar follow-up time. We retrieved film mammograms of the unaffected breast from two time points, prior to/at the time of the first diagnosis (253 CBC cases, 269 UBC controls) and ≥ 6 months up to 48 months following the first diagnosis (333 CBC cases, 377 UBC controls). Mammograms were digitized and percent MD (%MD) was measured using the thresholding program Cumulus. Odds ratios (OR) and 95% confidence intervals (CI) for association between %MD and CBC, adjusted for age, treatment, and other factors related to CBC, were estimated using logistic regression. Linear regression was used to estimate the association between treatment modality and change in %MD in 467 women with mammograms at both time points. Results For %MD assessed following diagnosis, there was a statistically significant trend of increasing CBC with increasing %MD (p = 0.03). Lower density (<25%) was associated with reduced risk of CBC compared to 25 to < 50% density (OR 0.69, 95% CI 0.49, 0.98). Similar, but weaker, associations were noted for %MD measurements prior to/at diagnosis. The relationship appeared strongest in women aged < 45 years and non-existent in women aged 50 to 54 years. A decrease of ≥ 10% in %MD between first and second mammogram was associated marginally with reduced risk of CBC (OR 0.63, 95% CI 0.40, 1.01) compared to change of <10%. Both tamoxifen and chemotherapy were associated with statistically significant 3% decreases in %MD (p < 0.01). Conclusions Post-diagnosis measures of %MD may be useful to include in CBC risk prediction models with consideration of age at diagnosis. Chemotherapy is associated with reductions in %MD, similar to tamoxifen. Electronic supplementary material The online version of this article (10.1186/s13058-018-0948-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Julia A Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 60 Murray Street Box 18, Toronto, ON, M6P 2G3, Canada. .,Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
| | | | - Jing Fan
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, 60 Murray Street Box 18, Toronto, ON, M6P 2G3, Canada
| | | | - Esther M John
- Cancer Prevention Institute of California, Fremont, CA, USA.,Department of Health Research and Policy (Epidemiology) and Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Leslie Bernstein
- Beckman Research Institute, City of Hope National Medical Center, Duarte, CA, USA
| | - Jennifer D Brooks
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Anne S Reiner
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Xiaolin Liang
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meghan Woods
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
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12
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Vinnicombe SJ. Breast density: why all the fuss? Clin Radiol 2017; 73:334-357. [PMID: 29273225 DOI: 10.1016/j.crad.2017.11.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 11/17/2017] [Indexed: 01/06/2023]
Abstract
The term "breast density" or mammographic density (MD) denotes those components of breast parenchyma visualised at mammography that are denser than adipose tissue. MD is composed of a mixture of epithelial and stromal components, notably collagen, in variable proportions. MD is most commonly assessed in clinical practice with the time-honoured method of visual estimation of area-based percent density (PMD) on a mammogram, with categorisation into quartiles. The computerised semi-automated thresholding method, Cumulus, also yielding area-based percent density, is widely used for research purposes; however, the advent of fully automated volumetric methods developed as a consequence of the widespread use of digital mammography (DM) and yielding both absolute and percent dense volumes, has resulted in an explosion of interest in MD recently. Broadly, the importance of MD is twofold: firstly, the presence of marked MD significantly reduces mammographic sensitivity for breast cancer, even with state-of-the-art DM. Recognition of this led to the formation of a powerful lobby group ('Are You Dense') in the US, as a consequence of which 32 states have legislated for mandatory disclosure of MD to women undergoing mammography. Secondly, it is now widely accepted that MD is in itself a risk factor for breast cancer, with a four-to sixfold increased relative risk in women with PMD in the highest quintile compared to those with PMD in the lowest quintile. Consequently, major research efforts are underway to assess whether use of MD could provide a major step forward towards risk-adapted, personalised breast cancer prevention, imaging, and treatment.
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Affiliation(s)
- S J Vinnicombe
- Cancer Research, School of Medicine, Level 7, Mailbox 4, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK.
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Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad. Diagnostics (Basel) 2017; 7:diagnostics7020030. [PMID: 28561776 PMCID: PMC5489950 DOI: 10.3390/diagnostics7020030] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 05/22/2017] [Accepted: 05/24/2017] [Indexed: 12/14/2022] Open
Abstract
Mammographic breast density (MBD) has been proven to be an important risk factor for breast cancer and an important determinant of mammographic screening performance. The measurement of density has changed dramatically since its inception. Initial qualitative measurement methods have been found to have limited consistency between readers, and in regards to breast cancer risk. Following the introduction of full-field digital mammography, more sophisticated measurement methodology is now possible. Automated computer-based density measurements can provide consistent, reproducible, and objective results. In this review paper, we describe various methods currently available to assess MBD, and provide a discussion on the clinical utility of such methods for breast cancer screening.
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Burton A, Byrnes G, Stone J, Tamimi RM, Heine J, Vachon C, Ozmen V, Pereira A, Garmendia ML, Scott C, Hipwell JH, Dickens C, Schüz J, Aribal ME, Bertrand K, Kwong A, Giles GG, Hopper J, Pérez Gómez B, Pollán M, Teo SH, Mariapun S, Taib NAM, Lajous M, Lopez-Riduara R, Rice M, Romieu I, Flugelman AA, Ursin G, Qureshi S, Ma H, Lee E, Sirous R, Sirous M, Lee JW, Kim J, Salem D, Kamal R, Hartman M, Miao H, Chia KS, Nagata C, Vinayak S, Ndumia R, van Gils CH, Wanders JOP, Peplonska B, Bukowska A, Allen S, Vinnicombe S, Moss S, Chiarelli AM, Linton L, Maskarinec G, Yaffe MJ, Boyd NF, dos-Santos-Silva I, McCormack VA. Mammographic density assessed on paired raw and processed digital images and on paired screen-film and digital images across three mammography systems. Breast Cancer Res 2016; 18:130. [PMID: 27993168 PMCID: PMC5168805 DOI: 10.1186/s13058-016-0787-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 11/23/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Inter-women and intra-women comparisons of mammographic density (MD) are needed in research, clinical and screening applications; however, MD measurements are influenced by mammography modality (screen film/digital) and digital image format (raw/processed). We aimed to examine differences in MD assessed on these image types. METHODS We obtained 1294 pairs of images saved in both raw and processed formats from Hologic and General Electric (GE) direct digital systems and a Fuji computed radiography (CR) system, and 128 screen-film and processed CR-digital pairs from consecutive screening rounds. Four readers performed Cumulus-based MD measurements (n = 3441), with each image pair read by the same reader. Multi-level models of square-root percent MD were fitted, with a random intercept for woman, to estimate processed-raw MD differences. RESULTS Breast area did not differ in processed images compared with that in raw images, but the percent MD was higher, due to a larger dense area (median 28.5 and 25.4 cm2 respectively, mean √dense area difference 0.44 cm (95% CI: 0.36, 0.52)). This difference in √dense area was significant for direct digital systems (Hologic 0.50 cm (95% CI: 0.39, 0.61), GE 0.56 cm (95% CI: 0.42, 0.69)) but not for Fuji CR (0.06 cm (95% CI: -0.10, 0.23)). Additionally, within each system, reader-specific differences varied in magnitude and direction (p < 0.001). Conversion equations revealed differences converged to zero with increasing dense area. MD differences between screen-film and processed digital on the subsequent screening round were consistent with expected time-related MD declines. CONCLUSIONS MD was slightly higher when measured on processed than on raw direct digital mammograms. Comparisons of MD on these image formats should ideally control for this non-constant and reader-specific difference.
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Affiliation(s)
- Anya Burton
- Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France
| | - Graham Byrnes
- Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Australia
| | - Rulla M. Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | | | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Vahit Ozmen
- Department of Surgery, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | | | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - John H. Hipwell
- Centre for Medical Image Computing, University College London, London, UK
| | - Caroline Dickens
- Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Joachim Schüz
- Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France
| | | | | | - Ava Kwong
- Division of Breast Surgery, Department of Surgery, The University of Hong Kong, Hong Kong, People’s Republic of China
- Department of Surgery, Hong Kong Sanatorium and Hospital, Hong Kong, People’s Republic of China
| | - Graham G. Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria Australia
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria Australia
| | - Beatriz Pérez Gómez
- Cancer Epidemiology Unit, Instituto de Salud Carlos III and CIBERESP, Madrid, Spain
| | - Marina Pollán
- Cancer Epidemiology Unit, Instituto de Salud Carlos III and CIBERESP, Madrid, Spain
| | - Soo-Hwang Teo
- Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia
- Cancer Research Malaysia, Subang Jaya, Malaysia
| | | | - Nur Aishah Mohd Taib
- Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia
| | - Martín Lajous
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Center for Research on Population Health, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | - Ruy Lopez-Riduara
- Center for Research on Population Health, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | - Megan Rice
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Isabelle Romieu
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | | | - Giske Ursin
- Cancer Registry of Norway, Oslo, Norway
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA USA
| | - Samera Qureshi
- Norwegian Center for Minority and Migrant Health Research (NAKMI), Oslo, Norway
| | - Huiyan Ma
- Department of Population Sciences, Beckman Research Institute, City of Hope, CA USA
| | - Eunjung Lee
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA USA
| | - Reza Sirous
- Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mehri Sirous
- Isfahan University of Medical Sciences, Isfahan, Iran
| | - Jong Won Lee
- Department of Surgery, Asan Medical Center, Seoul, Republic of Korea
| | - Jisun Kim
- Department of Surgery, Asan Medical Center, Seoul, Republic of Korea
| | | | - Rasha Kamal
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Mikael Hartman
- Department of Surgery, Yong Loo Lin School of Medicine, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Hui Miao
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Kee-Seng Chia
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
| | | | | | - Rose Ndumia
- Aga Khan University Hospital, Nairobi, Kenya
| | - Carla H. van Gils
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Johanna O. P. Wanders
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | | | - Steve Allen
- Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK
| | - Sarah Vinnicombe
- Division of Cancer Research, Ninewells Hospital & Medical School, Dundee, UK
| | - Sue Moss
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Anna M. Chiarelli
- Ontario Breast Screening Program, Cancer Care Ontario, Toronto, Canada
| | - Linda Linton
- Princess Margaret Cancer Centre, Toronto, Canada
| | | | | | | | - Isabel dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Valerie A. McCormack
- Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France
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