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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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Jiang S, Bennett DL, Rosner BA, Colditz GA. Longitudinal Analysis of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk. JAMA Oncol 2023; 9:808-814. [PMID: 37103922 PMCID: PMC10141289 DOI: 10.1001/jamaoncol.2023.0434] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 01/27/2023] [Indexed: 04/28/2023]
Abstract
Importance Although breast density is an established risk factor for breast cancer, longitudinal changes in breast density have not been extensively studied to determine whether this factor is associated with breast cancer risk. Objective To prospectively evaluate the association between change in mammographic density in each breast over time and risk of subsequent breast cancer. Design, Setting, and Participants This nested case-control cohort study was sampled from the Joanne Knight Breast Health Cohort of 10 481 women free from cancer at entry and observed from November 3, 2008, to October 31, 2020, with routine screening mammograms every 1 to 2 years, providing a measure of breast density. Breast cancer screening was provided for a diverse population of women in the St Louis region. A total of 289 case patients with pathology-confirmed breast cancer were identified, and approximately 2 control participants were sampled for each case according to age at entry and year of enrollment, yielding 658 controls with a total number of 8710 craniocaudal-view mammograms for analysis. Exposures Exposures included screening mammograms with volumetric percentage of density, change in volumetric breast density over time, and breast biopsy pathology-confirmed cancer. Breast cancer risk factors were collected via questionnaire at enrollment. Main Outcomes and Measures Longitudinal changes over time in each woman's volumetric breast density by case and control status. Results The mean (SD) age of the 947 participants was 56.67 (8.71) years at entry; 141 were Black (14.9%), 763 were White (80.6%), 20 were of other race or ethnicity (2.1%), and 23 did not report this information (2.4%). The mean (SD) interval was 2.0 (1.5) years from last mammogram to date of subsequent breast cancer diagnosis (10th percentile, 1.0 year; 90th percentile, 3.9 years). Breast density decreased over time in both cases and controls. However, there was a significantly slower decrease in rate of decline in density in the breast that developed breast cancer compared with the decline in controls (estimate = 0.027; 95% CI, 0.001-0.053; P = .04). Conclusions and Relevance This study found that the rate of change in breast density was associated with the risk of subsequent breast cancer. Incorporation of longitudinal changes into existing models could optimize risk stratification and guide more personalized risk management.
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Affiliation(s)
- Shu Jiang
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Debbie L. Bennett
- Department of Radiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Bernard A. Rosner
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Graham A. Colditz
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine in St Louis, St Louis, Missouri
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Ward SV, Burton A, Tamimi RM, Pereira A, Garmendia ML, Pollan M, Boyd N, Dos-Santos-Silva I, Maskarinec G, Perez-Gomez B, Vachon C, Miao H, Lajous M, López-Ridaura R, Bertrand K, Kwong A, Ursin G, Lee E, Ma H, Vinnicombe S, Moss S, Allen S, Ndumia R, Vinayak S, Teo SH, Mariapun S, Peplonska B, Bukowska-Damska A, Nagata C, Hopper J, Giles G, Ozmen V, Aribal ME, Schüz J, Van Gils CH, Wanders JOP, Sirous R, Sirous M, Hipwell J, Kim J, Lee JW, Dickens C, Hartman M, Chia KS, Scott C, Chiarelli AM, Linton L, Flugelman AA, Salem D, Kamal R, McCormack V, Stone J. The association of age at menarche and adult height with mammographic density in the International Consortium of Mammographic Density. Breast Cancer Res 2022; 24:49. [PMID: 35836268 PMCID: PMC9284807 DOI: 10.1186/s13058-022-01545-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 06/29/2022] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Early age at menarche and tall stature are associated with increased breast cancer risk. We examined whether these associations were also positively associated with mammographic density, a strong marker of breast cancer risk. METHODS Participants were 10,681 breast-cancer-free women from 22 countries in the International Consortium of Mammographic Density, each with centrally assessed mammographic density and a common set of epidemiologic data. Study periods for the 27 studies ranged from 1987 to 2014. Multi-level linear regression models estimated changes in square-root per cent density (√PD) and dense area (√DA) associated with age at menarche and adult height in pooled analyses and population-specific meta-analyses. Models were adjusted for age at mammogram, body mass index, menopausal status, hormone therapy use, mammography view and type, mammographic density assessor, parity and height/age at menarche. RESULTS In pooled analyses, later age at menarche was associated with higher per cent density (β√PD = 0.023 SE = 0.008, P = 0.003) and larger dense area (β√DA = 0.032 SE = 0.010, P = 0.002). Taller women had larger dense area (β√DA = 0.069 SE = 0.028, P = 0.012) and higher per cent density (β√PD = 0.044, SE = 0.023, P = 0.054), although the observed effect on per cent density depended upon the adjustment used for body size. Similar overall effect estimates were observed in meta-analyses across population groups. CONCLUSIONS In one of the largest international studies to date, later age at menarche was positively associated with mammographic density. This is in contrast to its association with breast cancer risk, providing little evidence of mediation. Increased height was also positively associated with mammographic density, particularly dense area. These results suggest a complex relationship between growth and development, mammographic density and breast cancer risk. Future studies should evaluate the potential mediation of the breast cancer effects of taller stature through absolute breast density.
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Affiliation(s)
- Sarah V Ward
- School of Population and Global Health, The University of Western Australia, Perth, Australia
| | - Anya Burton
- Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, 69372, Lyon Cedex 08, France
- Translation Health Sciences, University of Bristol, Bristol, UK
| | - Rulla M Tamimi
- Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, USA
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | | | - Marina Pollan
- Cancer and Environmental Epidemiology Unit, Instituto de Salud Carlos III, Madrid, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Norman Boyd
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Beatriz Perez-Gomez
- Cancer and Environmental Epidemiology Unit, Instituto de Salud Carlos III, Madrid, Spain
- Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Hui Miao
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore City, Singapore
| | - Martín Lajous
- Instituto Nacional de Salud Pública, Cuernavaca, Mexico
| | | | | | - Ava Kwong
- Division of Breast Surgery, Faculty of Medicine, University of Hong Kong, Pok Fu Lam, Hong Kong, China
- Department of Surgery and Cancer Genetics Center, Hong Kong Sanatorium and Hospital, Pok Fu Lam, Hong Kong, China
- Hong Kong Hereditary Breast Cancer Family Registry, Pok Fu Lam, Hong Kong, China
| | - 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
| | - Eunjung Lee
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, USA
| | - Huiyan Ma
- Department of Population Sciences, City of Hope National Medical Center, Duarte, CA, USA
| | - Sarah Vinnicombe
- Division of Cancer Research, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, UK
| | - Sue Moss
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Steve Allen
- Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK
| | - Rose Ndumia
- Aga Khan University Hospital, Nairobi, Kenya
| | | | - Soo-Hwang Teo
- Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia
- Cancer Research Malaysia, Subang Jaya, Malaysia
| | | | - Beata Peplonska
- Department of Environmental Epidemiology, Nofer Institute of Occupational Medicine, Łódź, Poland
| | - Agnieszka Bukowska-Damska
- Department of Physiology, Pathophysiology and Clinical Immunology,, Medical University of Lodz., Łódź, Poland
| | - Chisato Nagata
- Department of Epidemiology and Preventive Medicine, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Graham Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Vahit Ozmen
- Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Mustafa Erkin Aribal
- Department of Radiology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Joachim Schüz
- School of Population and Global Health, The University of Western Australia, Perth, Australia
| | - 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
| | - Reza Sirous
- Radiology Department, George Washington University Hospital, Washington, DC, USA
| | - Mehri Sirous
- Radiology Department, Isfahan University of Medical Sciences, Isfahan, Iran
| | - John Hipwell
- Centre for Medical Image Computing, University College London, London, UK
| | - Jisun Kim
- Asan Medical Center, Seoul, Republic of Korea
| | | | - Caroline Dickens
- Department of Internal Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Mikael Hartman
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore City, Singapore
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore
| | - Kee-Seng Chia
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Anna M Chiarelli
- Ontario Breast Screening Program, Cancer Care Ontario, Toronto, ON, Canada
| | - Linda Linton
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Anath Arzee Flugelman
- National Cancer Control Center, Lady Davis Carmel Medical Center, Faculty of Medicine, Technion-Israel Institute Technology, Haifa, Israel
| | - Dorria Salem
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Rasha Kamal
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Valerie McCormack
- Environment and Lifestyle Epidemiology Branch, International Agency for Research on Cancer, 150 Cours Albert Thomas, 69372, Lyon Cedex 08, France.
| | - Jennifer Stone
- School of Population and Global Health, The University of Western Australia, Perth, Australia
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Flugelman AA, Burton A, Keinan-Boker L, Stein N, Kutner D, Shemesh L, Boyd N. Correlation between cumulative mammographic density and age-specific incidence of breast cancer: A biethnic study in Israel. Int J Cancer 2022; 150:1968-1977. [PMID: 35128649 DOI: 10.1002/ijc.33957] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 01/08/2022] [Accepted: 01/18/2022] [Indexed: 06/28/2024]
Abstract
Women with the most extensive breast density, have a 4- to 6-fold higher cancer risk than women with the lowest density. This cross-sectional study evaluated associations of cumulative mammographic density in two distinct ethnic groups with the respective age-specific breast cancer incidences in the population. The study compared four cohorts of 200 women each aged 35 to 49 and 50 to 74, representing Jewish and Arab ethnicity. Breast density measures were calculated from screening mammograms, using a thresholding software (Cumulus). Breast cancer specific incidence values were obtained from the National Cancer Registry. The percent mammographic density was lower for women aged 50 to 74 than 35 to 49 years, both for Jews: 11.7 vs 23.1 and for Arabs: 11.6 vs 18.3. In contrast, the cumulative density increased with age, from 37.30 to 181.24 in Jews, compared to 21.26 to 108.03 in Arabs. Similar trends in breast cancer incidence rates per 100 000 in the Israeli population were apparent, with an increase from 92.95 to 381.91 in Jews, compared to 48.6 to 244.44 in Arabs. Comparing cumulative density of the cohort with respective age-specific breast cancer incidence in the population yielded a highly significant correlation: Jews; r = .97, P < .0001 and Arabs: r = .86, P = .007. A strong association was found between the log of cumulative density and the log of cancer incidence, as well. Our study identified correlations between cumulative mammographic density and breast cancer incidence in two distinct populations. The findings should prompt research to enhance our understanding of the pathogenesis of breast cancer, and lead to novel insights into measures of prevention.
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Affiliation(s)
- Anath A Flugelman
- Rambam Health Care Campus, Technion-Israel Institute of Technology, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Anya Burton
- National Cancer Registration and Analysis Service, Bristol, UK
| | - Lital Keinan-Boker
- National Cancer Registry, Center for Disease Control, Ministry of Health, Ramat Gan
- School of Public Health, University of Haifa, Haifa, Israel
| | - Nili Stein
- The Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Dafna Kutner
- The Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Lior Shemesh
- The Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Clalit National Cancer Control Center, Haifa, Israel
| | - Norman Boyd
- Princess Margaret Cancer Center, Toronto, Ontario, Canada
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5
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Sneider A, Kiemen A, Kim JH, Wu PH, Habibi M, White M, Phillip JM, Gu L, Wirtz D. Deep learning identification of stiffness markers in breast cancer. Biomaterials 2022; 285:121540. [PMID: 35537336 PMCID: PMC9873266 DOI: 10.1016/j.biomaterials.2022.121540] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/12/2022] [Accepted: 04/21/2022] [Indexed: 02/07/2023]
Abstract
While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo, is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.
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Affiliation(s)
- Alexandra Sneider
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Ashley Kiemen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Joo Ho Kim
- Department of Materials Science and Engineering, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Mehran Habibi
- Johns Hopkins Breast Center, Johns Hopkins Bayview Medical Center, 4940 Eastern Ave, Baltimore, MD, 21224, USA
| | - Marissa White
- Department of Pathology, Johns Hopkins School of Medicine, 401 N Broadway, Baltimore, MD, 21231, USA
| | - Jude M. Phillip
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA,Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Luo Gu
- Department of Materials Science and Engineering, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA
| | - Denis Wirtz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA,Department of Pathology, Johns Hopkins School of Medicine, 401 N Broadway, Baltimore, MD, 21231, USA,Department of Oncology, Johns Hopkins School of Medicine, 1800 Orleans St, Baltimore, MD, 21205, USA,Corresponding author. Department of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, and Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA., (D. Wirtz)
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Watt GP, Knight JA, Lin C, Lynch CF, Malone KE, John EM, Bernstein L, Brooks JD, Reiner AS, Liang X, Woods M, Nguyen TL, Hopper JL, Pike MC, Bernstein JL. Mammographic texture features associated with contralateral breast cancer in the WECARE Study. NPJ Breast Cancer 2021; 7:146. [PMID: 34845211 PMCID: PMC8630158 DOI: 10.1038/s41523-021-00354-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 11/01/2021] [Indexed: 01/12/2023] Open
Abstract
To evaluate whether mammographic texture features were associated with second primary contralateral breast cancer (CBC) risk, we created a "texture risk score" using pre-treatment mammograms in a case-control study of 212 women with CBC and 223 controls with unilateral breast cancer. The texture risk score was associated with CBC (odds per adjusted standard deviation = 1.25, 95% CI 1.01-1.56) after adjustment for mammographic percent density and confounders. These results support the potential of texture features for CBC risk assessment of breast cancer survivors.
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Affiliation(s)
- Gordon P. Watt
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Julia A. Knight
- grid.250674.20000 0004 0626 6184Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON Canada ,grid.17063.330000 0001 2157 2938Division of Epidemiology, Dalla Lana School of Public Health, Toronto, ON Canada
| | - Christine Lin
- grid.240473.60000 0004 0543 9901Penn State College of Medicine, Hershey, PA USA
| | - Charles F. Lynch
- grid.214572.70000 0004 1936 8294 Department of Epidemiology, University of Iowa, Iowa City, IA USA
| | - Kathleen E. Malone
- grid.270240.30000 0001 2180 1622Fred Hutchinson Cancer Research Center, Seattle, WA 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, Stanford University School of Medicine, Stanford, CA USA
| | - Leslie Bernstein
- grid.410425.60000 0004 0421 8357Beckman Research Institute, City of Hope National Medical Center, Duarte, CA USA
| | - Jennifer D. Brooks
- grid.17063.330000 0001 2157 2938Division of Epidemiology, Dalla Lana School of Public Health, Toronto, ON Canada
| | - Anne S. Reiner
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Xiaolin Liang
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Meghan Woods
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Tuong L. Nguyen
- grid.1008.90000 0001 2179 088XMelbourne School of Population and Global Health, University of Melbourne, Parkville, VIC Australia
| | - John L. Hopper
- grid.1008.90000 0001 2179 088XMelbourne School of Population and Global Health, University of Melbourne, Parkville, VIC Australia
| | - Malcolm C. Pike
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Jonine L. Bernstein
- grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA
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7
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Shu H, Chiang T, Wei P, Do KA, Lesslie MD, Cohen EO, Srinivasan A, Moseley TW, Chang Sen LQ, Leung JWT, Dennison JB, Hanash SM, Weaver OO. A Deep Learning Approach to Re-create Raw Full-Field Digital Mammograms for Breast Density and Texture Analysis. Radiol Artif Intell 2021; 3:e200097. [PMID: 34350403 DOI: 10.1148/ryai.2021200097] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 03/18/2021] [Accepted: 03/30/2021] [Indexed: 11/11/2022]
Abstract
Purpose To develop a computational approach to re-create rarely stored for-processing (raw) digital mammograms from routinely stored for-presentation (processed) mammograms. Materials and Methods In this retrospective study, pairs of raw and processed mammograms collected in 884 women (mean age, 57 years ± 10 [standard deviation]; 3713 mammograms) from October 5, 2017, to August 1, 2018, were examined. Mammograms were split 3088 for training and 625 for testing. A deep learning approach based on a U-Net convolutional network and kernel regression was developed to estimate the raw images. The estimated raw images were compared with the originals by four image error and similarity metrics, breast density calculations, and 29 widely used texture features. Results In the testing dataset, the estimated raw images had small normalized mean absolute error (0.022 ± 0.015), scaled mean absolute error (0.134 ± 0.078) and mean absolute percentage error (0.115 ± 0.059), and a high structural similarity index (0.986 ± 0.007) for the breast portion compared with the original raw images. The estimated and original raw images had a strong correlation in breast density percentage (Pearson r = 0.946) and a strong agreement in breast density grade (Cohen κ = 0.875). The estimated images had satisfactory correlations with the originals in 23 texture features (Pearson r ≥ 0.503 or Spearman ρ ≥ 0.705) and were well complemented by processed images for the other six features. Conclusion This deep learning approach performed well in re-creating raw mammograms with strong agreement in four image evaluation metrics, breast density, and the majority of 29 widely used texture features.Keywords: Mammography, Breast, Supervised Learning, Convolutional Neural Network (CNN), Deep learning algorithms, Machine Learning AlgorithmsSee also the commentary by Chan in this issue.Supplemental material is available for this article.©RSNA, 2021.
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Affiliation(s)
- Hai Shu
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Tingyu Chiang
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Peng Wei
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Kim-Anh Do
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Michele D Lesslie
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Ethan O Cohen
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Ashmitha Srinivasan
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Tanya W Moseley
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Lauren Q Chang Sen
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Jessica W T Leung
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Jennifer B Dennison
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Sam M Hanash
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
| | - Olena O Weaver
- Departments of Biostatistics (H.S., P.W., K.A.D.), Diagnostic Radiology (T.C., M.D.L., E.O.C., A.S., T.W.M., L.Q.C.S., J.W.T.L., O.O.W.), and Clinical Cancer Prevention (J.B.D., S.M.H.), The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030; and Department of Biostatistics, School of Global Public Health, New York University, New York, NY (H.S.)
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Darcey E, Lloyd R, Cadby G, Pilkington L, Redfern A, Thompson SC, Saunders C, Wylie E, Stone J. The association between mammographic density and breast cancer risk in Western Australian Aboriginal women. Breast Cancer Res Treat 2019; 176:235-242. [PMID: 30977028 DOI: 10.1007/s10549-019-05225-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 04/03/2019] [Indexed: 11/25/2022]
Abstract
PURPOSE Mammographic density is an established breast cancer risk factor within many ethnically different populations. The distribution of mammographic density has been shown to be significantly lower in Western Australian Aboriginal women compared to age- and screening location-matched non-Aboriginal women. Whether mammographic density is a predictor of breast cancer risk in Aboriginal women is unknown. METHODS We measured mammographic density from 103 Aboriginal breast cancer cases and 327 Aboriginal controls, 341 non-Aboriginal cases, and 333 non-Aboriginal controls selected from the BreastScreen Western Australia database using the Cumulus software program. Logistic regression was used to examine the associations of percentage dense area and absolute dense area with breast cancer risk for Aboriginal and non-Aboriginal women separately, adjusting for covariates. RESULTS Both percentage density and absolute dense area were strongly predictive of risk in Aboriginal women with odds per adjusted standard deviation (OPERAS) of 1.36 (95% CI 1.09, 1.69) and 1.36 (95% CI 1.08, 1.71), respectively. For non-Aboriginal women, the OPERAS were 1.22 (95% CI 1.03, 1.46) and 1.26 (95% CI 1.05, 1.50), respectively. CONCLUSIONS Whilst mean mammographic density for Aboriginal women is lower than non-Aboriginal women, density measures are still higher in Aboriginal women with breast cancer compared to Aboriginal women without breast cancer. Thus, mammographic density strongly predicts breast cancer risk in Aboriginal women. Future efforts to predict breast cancer risk using mammographic density or standardize risk-associated mammographic density measures should take into account Aboriginal status when applicable.
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Affiliation(s)
- Ellie Darcey
- Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, 35 Stirling Highway, M409, Perth, WA, 6009, Australia
| | - Rachel Lloyd
- Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, 35 Stirling Highway, M409, Perth, WA, 6009, Australia
| | - Gemma Cadby
- Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, 35 Stirling Highway, M409, Perth, WA, 6009, Australia
| | - Leanne Pilkington
- BreastScreen Western Australia, Women and Newborn Health Service, 9th Floor, Eastpoint Plaza, 233 Adelaide Terrace, Perth, WA, 6000, Australia
| | - Andrew Redfern
- School of Medicine, The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia.,Fiona Stanley Hospital, Robin Warren Drive, Murdoch, WA, Australia
| | - Sandra C Thompson
- School of Population and Global Health, Western Australian Centre for Rural Health, The University of Western Australia, 167 Fitzgerald St, Geraldton, WA, 6531, Australia
| | - Christobel Saunders
- School of Medicine, The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia.,Fiona Stanley Hospital, Robin Warren Drive, Murdoch, WA, Australia
| | - Elizabeth Wylie
- BreastScreen Western Australia, Women and Newborn Health Service, 9th Floor, Eastpoint Plaza, 233 Adelaide Terrace, Perth, WA, 6000, Australia.,School of Medicine, The University of Western Australia, 35 Stirling Highway, Perth, WA, 6009, Australia
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, 35 Stirling Highway, M409, Perth, WA, 6009, Australia. .,The RPH Research Foundation, Royal Perth Hospital, 50 Murray Street, Perth, WA, 6000, Australia.
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9
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McLean K, Darcey E, Cadby G, Lund H, Pilkington L, Redfern A, Thompson S, Saunders C, Wylie E, Stone J. The distribution and determinants of mammographic density measures in Western Australian aboriginal women. Breast Cancer Res 2019; 21:33. [PMID: 30819215 PMCID: PMC6393976 DOI: 10.1186/s13058-019-1113-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 02/01/2019] [Indexed: 11/27/2022] Open
Abstract
Background Mammographic density (MD) is an established risk factor for breast cancer. There are significant ethnic differences in MD measures which are consistent with those for corresponding breast cancer risk. This is the first study investigating the distribution and determinants of MD measures within Aboriginal women of Western Australia (WA). Methods Epidemiological data and mammographic images were obtained from 628 Aboriginal women and 624 age-, year of screen-, and screening location-matched non-Aboriginal women randomly selected from the BreastScreen Western Australia database. Women were cancer free at the time of their mammogram between 1989 and 2014. MD was measured using the Cumulus software. Kolmogorov-Smirnov tests were used to compare distributions of absolute dense area (DA), precent dense area (PDA), non-dense area (NDA) and total breast area between Aboriginal and non-Aboriginal women. General linear regression was used to estimate the determinants of MD, adjusting for age, NDA, hormone therapy use, family history, measures of socio-economic status and remoteness of residence for Aboriginal and non-Aboriginal women separately. Results Aboriginal women were found to have lower DA and PDA and higher NDA than non-Aboriginal women. Age (p < 0.001) was negatively associated and several socio-economic indices (p < 0.001) were positively associated with DA and PDA in Aboriginal and non-Aboriginal women. Remoteness of residence was associated with both mammographic measures but for non-Aboriginal women only. Conclusions Aboriginal women have, on average, less MD than non-Aboriginal women but the factors associated with MD are similar for both sample populations. Since reduced MD is associated with improved sensitivity of mammography, this study suggests that mammographic screening is a particularly good test for Australian Indigenous women, a population that suffers from high breast cancer mortality. Electronic supplementary material The online version of this article (10.1186/s13058-019-1113-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kirsty McLean
- Centre for Genetic Origins of Health and Disease, School of Biomedical Science, Curtin University and The University of Western Australia, Perth, Western Australia, Australia
| | - Ellie Darcey
- Centre for Genetic Origins of Health and Disease, School of Biomedical Science, Curtin University and The University of Western Australia, Perth, Western Australia, Australia
| | - Gemma Cadby
- Centre for Genetic Origins of Health and Disease, School of Biomedical Science, Curtin University and The University of Western Australia, Perth, Western Australia, Australia
| | - Helen Lund
- BreastScreen Western Australia, Women and Newborn Health Service, Perth, Western Australia, Australia
| | - Leanne Pilkington
- BreastScreen Western Australia, Women and Newborn Health Service, Perth, Western Australia, Australia.,WA Country Health Service, Government of Western Australia, Perth, Western Australia, Australia
| | - Andrew Redfern
- School of Medicine, The University of Western Australia, Perth, Western Australia, Australia.,Fiona Stanley Hospital, Robin Warren Drive, Murdoch, Western Australia, Australia
| | - Sandra Thompson
- Western Australian Centre for Rural Health, School of Population and Global Health, The University of Western Australia, Geraldton, Western Australia, Australia
| | - Christobel Saunders
- School of Medicine, The University of Western Australia, Perth, Western Australia, Australia.,Fiona Stanley Hospital, Robin Warren Drive, Murdoch, Western Australia, Australia
| | - Elizabeth Wylie
- BreastScreen Western Australia, Women and Newborn Health Service, Perth, Western Australia, Australia.,School of Medicine, The University of Western Australia, Perth, Western Australia, Australia
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, School of Biomedical Science, Curtin University and The University of Western Australia, Perth, Western Australia, Australia. .,The Medical Research Foundation, Royal Perth Hospital, Perth, Western Australia, Australia. .,Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, 35 Stirling Highway M409, Crawley, Western Australia, 6009, Australia.
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10
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Lee J, Nishikawa RM. Automated mammographic breast density estimation using a fully convolutional network. Med Phys 2018; 45:1178-1190. [PMID: 29363774 DOI: 10.1002/mp.12763] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Revised: 12/06/2017] [Accepted: 12/29/2017] [Indexed: 11/11/2022] Open
Abstract
PURPOSE The purpose of this study was to develop a fully automated algorithm for mammographic breast density estimation using deep learning. METHOD Our algorithm used a fully convolutional network, which is a deep learning framework for image segmentation, to segment both the breast and the dense fibroglandular areas on mammographic images. Using the segmented breast and dense areas, our algorithm computed the breast percent density (PD), which is the faction of dense area in a breast. Our dataset included full-field digital screening mammograms of 604 women, which included 1208 mediolateral oblique (MLO) and 1208 craniocaudal (CC) views. We allocated 455, 58, and 91 of 604 women and their exams into training, testing, and validation datasets, respectively. We established ground truth for the breast and the dense fibroglandular areas via manual segmentation and segmentation using a simple thresholding based on BI-RADS density assessments by radiologists, respectively. Using the mammograms and ground truth, we fine-tuned a pretrained deep learning network to train the network to segment both the breast and the fibroglandular areas. Using the validation dataset, we evaluated the performance of the proposed algorithm against radiologists' BI-RADS density assessments. Specifically, we conducted a correlation analysis between a BI-RADS density assessment of a given breast and its corresponding PD estimate by the proposed algorithm. In addition, we evaluated our algorithm in terms of its ability to classify the BI-RADS density using PD estimates, and its ability to provide consistent PD estimates for the left and the right breast and the MLO and CC views of the same women. To show the effectiveness of our algorithm, we compared the performance of our algorithm against a state of the art algorithm, laboratory for individualized breast radiodensity assessment (LIBRA). RESULT The PD estimated by our algorithm correlated well with BI-RADS density ratings by radiologists. Pearson's rho values of our algorithm for CC view, MLO view, and CC-MLO-averaged were 0.81, 0.79, and 0.85, respectively, while those of LIBRA were 0.58, 0.71, and 0.69, respectively. For CC view and CC-MLO averaged cases, the difference in rho values between the proposed algorithm and LIBRA showed statistical significance (P < 0.006). In addition, our algorithm provided reliable PD estimates for the left and the right breast (Pearson's ρ > 0.87) and for the MLO and CC views (Pearson's ρ = 0.76). However, LIBRA showed a lower Pearson's rho value (0.66) for both the left and right breasts for the CC view. In addition, our algorithm showed an excellent ability to separate each sub BI-RADS breast density class (statistically significant, p-values = 0.0001 or less); only one comparison pair, density 1 and density 2 in the CC view, was not statistically significant (P = 0.54). However, LIBRA failed to separate breasts in density 1 and 2 for both the CC and MLO views (P > 0.64). CONCLUSION We have developed a new deep learning based algorithm for breast density segmentation and estimation. We showed that the proposed algorithm correlated well with BI-RADS density assessments by radiologists and outperformed an existing state of the art algorithm.
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Affiliation(s)
- Juhun Lee
- Department of Radiology, University of Pittsburgh, 3362 Fifth Ave.,, Pittsburgh, PA, 15213, USA
| | - Robert M Nishikawa
- Department of Radiology, University of Pittsburgh, 3362 Fifth Ave.,, Pittsburgh, PA, 15213, USA
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11
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A comprehensive tool for measuring mammographic density changes over time. Breast Cancer Res Treat 2018; 169:371-379. [PMID: 29392583 PMCID: PMC5945741 DOI: 10.1007/s10549-018-4690-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Accepted: 01/21/2018] [Indexed: 11/14/2022]
Abstract
Background Mammographic density is a marker of breast cancer risk and diagnostics accuracy. Density change over time is a strong proxy for response to endocrine treatment and potentially a stronger predictor of breast cancer incidence. We developed STRATUS to analyse digital and analogue images and enable automated measurements of density changes over time. Method Raw and processed images from the same mammogram were randomly sampled from 41,353 healthy women. Measurements from raw images (using FDA approved software iCAD) were used as templates for STRATUS to measure density on processed images through machine learning. A similar two-step design was used to train density measures in analogue images. Relative risks of breast cancer were estimated in three unique datasets. An alignment protocol was developed using images from 11,409 women to reduce non-biological variability in density change. The protocol was evaluated in 55,073 women having two regular mammography screens. Differences and variances in densities were compared before and after image alignment. Results The average relative risk of breast cancer in the three datasets was 1.6 [95% confidence interval (CI) 1.3–1.8] per standard deviation of percent mammographic density. The discrimination was AUC 0.62 (CI 0.60–0.64). The type of image did not significantly influence the risk associations. Alignment decreased the non-biological variability in density change and re-estimated the yearly overall percent density decrease from 1.5 to 0.9%, p < 0.001. Conclusions The quality of STRATUS density measures was not influenced by mammogram type. The alignment protocol reduced the non-biological variability between images over time. STRATUS has the potential to become a useful tool for epidemiological studies and clinical follow-up. Electronic supplementary material The online version of this article (10.1007/s10549-018-4690-5) contains supplementary material, which is available to authorized users.
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12
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Burton A, Maskarinec G, Perez-Gomez B, Vachon C, Miao H, Lajous M, López-Ridaura R, Rice M, Pereira A, Garmendia ML, Tamimi RM, Bertrand K, Kwong A, Ursin G, Lee E, Qureshi SA, Ma H, Vinnicombe S, Moss S, Allen S, Ndumia R, Vinayak S, Teo SH, Mariapun S, Fadzli F, Peplonska B, Bukowska A, Nagata C, Stone J, Hopper J, Giles G, Ozmen V, Aribal ME, Schüz J, Van Gils CH, Wanders JOP, Sirous R, Sirous M, Hipwell J, Kim J, Lee JW, Dickens C, Hartman M, Chia KS, Scott C, Chiarelli AM, Linton L, Pollan M, Flugelman AA, Salem D, Kamal R, Boyd N, dos-Santos-Silva I, McCormack V. Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide. PLoS Med 2017; 14:e1002335. [PMID: 28666001 PMCID: PMC5493289 DOI: 10.1371/journal.pmed.1002335] [Citation(s) in RCA: 101] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 05/24/2017] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Mammographic density (MD) is one of the strongest breast cancer risk factors. Its age-related characteristics have been studied in women in western countries, but whether these associations apply to women worldwide is not known. METHODS AND FINDINGS We examined cross-sectional differences in MD by age and menopausal status in over 11,000 breast-cancer-free women aged 35-85 years, from 40 ethnicity- and location-specific population groups across 22 countries in the International Consortium on Mammographic Density (ICMD). MD was read centrally using a quantitative method (Cumulus) and its square-root metrics were analysed using meta-analysis of group-level estimates and linear regression models of pooled data, adjusted for body mass index, reproductive factors, mammogram view, image type, and reader. In all, 4,534 women were premenopausal, and 6,481 postmenopausal, at the time of mammography. A large age-adjusted difference in percent MD (PD) between post- and premenopausal women was apparent (-0.46 cm [95% CI: -0.53, -0.39]) and appeared greater in women with lower breast cancer risk profiles; variation across population groups due to heterogeneity (I2) was 16.5%. Among premenopausal women, the √PD difference per 10-year increase in age was -0.24 cm (95% CI: -0.34, -0.14; I2 = 30%), reflecting a compositional change (lower dense area and higher non-dense area, with no difference in breast area). In postmenopausal women, the corresponding difference in √PD (-0.38 cm [95% CI: -0.44, -0.33]; I2 = 30%) was additionally driven by increasing breast area. The study is limited by different mammography systems and its cross-sectional rather than longitudinal nature. CONCLUSIONS Declines in MD with increasing age are present premenopausally, continue postmenopausally, and are most pronounced over the menopausal transition. These effects were highly consistent across diverse groups of women worldwide, suggesting that they result from an intrinsic biological, likely hormonal, mechanism common to women. If cumulative breast density is a key determinant of breast cancer risk, younger ages may be the more critical periods for lifestyle modifications aimed at breast density and breast cancer risk reduction.
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Affiliation(s)
- Anya Burton
- Section of Environment and Radiation, International Agency for Research on Cancer, Lyon, France
| | - Gertraud Maskarinec
- University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | | | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Hui Miao
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Martín Lajous
- Instituto Nacional de Salud Pública, Cuernavaca, Mexico
| | | | - Megan Rice
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Ana Pereira
- Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Maria Luisa Garmendia
- Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Santiago, Chile
| | - Rulla M. Tamimi
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Kimberly Bertrand
- Slone Epidemiology Center, Boston University, Boston, Massachusetts, United States of America
| | - Ava Kwong
- Division of Breast Surgery, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong, China
- Department of Surgery and Cancer Genetics Center, Hong Kong Sanatorium and Hospital, Hong Kong, China
- Hong Kong Hereditary Breast Cancer Family Registry, Hong Kong, China
| | - 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, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
| | - Eunjung Lee
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
| | - Samera A. Qureshi
- Norwegian Centre for Migrant and Minority Health (NAKMI), Oslo, Norway
| | - Huiyan Ma
- Department of Population Sciences, City of Hope National Medical Center, Duarte, California, United States of America
| | - Sarah Vinnicombe
- Division of Cancer Research, Ninewells Hospital and Medical School, Dundee, United Kingdom
| | - Sue Moss
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, United Kingdom
| | - Steve Allen
- Department of Diagnostic Radiology, Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Rose Ndumia
- Aga Khan University Hospital, Nairobi, Kenya
| | | | - Soo-Hwang Teo
- Breast Cancer Research Group, University of Malaya Medical Centre, University of Malaya, Kuala Lumpur, Malaysia
- Cancer Research Malaysia, Subang Jaya, Malaysia
| | | | - Farhana Fadzli
- Breast Cancer Research Unit, Faculty of Medicine, University of Malaya Cancer Research Institute, University of Malaya, Kuala Lumpur, Malaysia
- Biomedical Imaging Department, University of Malaya Medical Centre, University of Malaya, Kuala Lumpur, Malaysia
| | | | | | - Chisato Nagata
- Department of Epidemiology & Preventive Medicine, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Graham Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Vahit Ozmen
- Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Mustafa Erkin Aribal
- Department of Radiology, School of Medicine, Marmara University, Istanbul, Turkey
| | - Joachim Schüz
- Section of Environment and Radiation, International Agency for Research on Cancer, Lyon, France
| | - 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
| | - Reza Sirous
- Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mehri Sirous
- Radiology Department, Isfahan University of Medical Sciences, Isfahan, Iran
| | - John Hipwell
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Jisun Kim
- Asan Medical Center, Seoul, Republic of Korea
| | | | - Caroline Dickens
- Department of Internal Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Mikael Hartman
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
- Department of Surgery, Yong Loo Lin School of Medicine, Singapore
| | - Kee-Seng Chia
- Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Anna M. Chiarelli
- Ontario Breast Screening Program, Cancer Care Ontario, Toronto, Ontario, Canada
| | - Linda Linton
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Marina Pollan
- Instituto de Salud Carlos III, Madrid, Spain
- CIBERESP, Madrid, Spain
| | - Anath Arzee Flugelman
- National Cancer Control Center, Lady Davis Carmel Medical Center, Faculty of Medicine, Technion–Israel Institute of Technology, Haifa, Israel
| | - Dorria Salem
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Rasha Kamal
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Norman Boyd
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Isabel dos-Santos-Silva
- Department of Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Valerie McCormack
- Section of Environment and Radiation, International Agency for Research on Cancer, Lyon, France
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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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Rice MS, Rosner BA, Tamimi RM. Percent mammographic density prediction: development of a model in the nurses' health studies. Cancer Causes Control 2017; 28:677-684. [PMID: 28478536 DOI: 10.1007/s10552-017-0898-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Accepted: 04/22/2017] [Indexed: 11/28/2022]
Abstract
PURPOSE To develop a model to predict percent mammographic density (MD) using questionnaire data and mammograms from controls in the Nurses' Health Studies' nested breast cancer case-control studies. Further, we assessed the association between both measured and predicted percent MD and breast cancer risk. METHODS Using data from 2,955 controls, we assessed several variables as potential predictors. We randomly divided our dataset into a training dataset (two-thirds of the dataset) and a testing dataset (one-third of the dataset). We used stepwise linear regression to identify the subset of variables that were most predictive. Next, we examined the correlation between measured and predicted percent MD in the testing dataset and computed the r 2 in the total dataset. We used logistic regression to examine the association between measured and predicted percent MD and breast cancer risk. RESULTS In the training dataset, several variables were selected for inclusion, including age, body mass index, and parity, among others. In the testing dataset, the Spearman correlation coefficient between predicted and measured percent MD was 0.61. As the prediction model performed well in the testing dataset, we developed the final model in the total dataset. The final prediction model explained 41% of the variability in percent MD. Both measured and predicted percent MD were similarly associated with breast cancer risk adjusting for age, menopausal status, and hormone use (OR per five unit increase = 1.09 for both). CONCLUSION These results suggest that predicted percent MD may be useful for research studies in which mammograms are unavailable.
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Affiliation(s)
- Megan S Rice
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, 55 Fruit Street, Bartlett 9, Boston, MA, 02114, USA.
| | - Bernard A Rosner
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02114, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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Byun J, Lee JE, Cha ES, Chung J, Kim JH. Visualization of Breast Microcalcifications on Digital Breast Tomosynthesis and 2-Dimensional Digital Mammography Using Specimens. BREAST CANCER-BASIC AND CLINICAL RESEARCH 2017; 11:1178223417703388. [PMID: 28469438 PMCID: PMC5391988 DOI: 10.1177/1178223417703388] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Accepted: 03/09/2017] [Indexed: 11/23/2022]
Abstract
Purpose: The purpose of this study is to compare the visibility of microcalcifications of digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM) using breast specimens. Materials And Methods: Thirty-one specimens’ DBT and FFDM were retrospectively reviewed by four readers. Results: The image quality of microcalcifications of DBT was rated as superior or equivalent in 71.0% by reader 1, 67.8% by reader 2, 64.5% by reader 3, and 80.6% by reader 4. The Fleiss kappa statistic for agreement among readers was 0.31. Conclusions: We suggest that image quality of DBT appears to be comparable with or better than FFDM in terms of revealing microcalcifications.
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Affiliation(s)
- Jieun Byun
- Department of Radiology, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Jee Eun Lee
- Department of Radiology, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Eun Suk Cha
- Department of Radiology, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Jin Chung
- Department of Radiology, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
| | - Jeoung Hyun Kim
- Department of Radiology, School of Medicine, Ewha Womans University, Seoul, Republic of Korea
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