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Adachi M, Ishiba T, Maruya S, Hayashi K, Kumaki Y, Oda G, Aruga T. Relationship between Volpara Density Grade and Compressed Breast Thickness in Japanese Patients with Breast Cancer. Diagnostics (Basel) 2024; 14:1651. [PMID: 39125527 PMCID: PMC11312128 DOI: 10.3390/diagnostics14151651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/09/2024] [Accepted: 07/26/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND High breast density found using mammographs (MGs) reduces positivity rates and is considered a risk factor for breast cancer. Research on the relationship between Volpara density grade (VDG) and compressed breast thickness (CBT) in the Japanese population is still lacking. Moreover, little attention has been paid to pseudo-dense breasts with CBT < 30 mm among high-density breasts. We investigated VDG, CBT, and apparent high breast density in patients with breast cancer. METHODS Women who underwent MG and breast cancer surgery at our institution were included. VDG and CBT were measured. VDG was divided into a non-dense group (NDG) and a dense group (DG). RESULTS This study included 419 patients. VDG was negatively correlated with CBT. The DG included younger patients with lower body mass index (BMI) and thinner CBT. In the DG, patients with CBT < 30 mm had lower BMI and higher VDG; however, no significant difference was noted in the positivity rate of the two groups. CONCLUSIONS Younger women tend to have higher breast density, resulting in thinner CBT, which may pose challenges in detecting breast cancer on MGs. However, there was no significant difference in the breast cancer detection rate between CBT < 30 mm and CBT ≥ 30 mm.
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Affiliation(s)
- Mio Adachi
- Department of Surgery (Breast), Tokyo Metropolitan Cancer and Infectious Disease Center Komagome Hospital, Tokyo 113-8677, Japan; (M.A.); (T.A.)
- Department of Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (S.M.); (K.H.); (Y.K.); (G.O.)
| | - Toshiyuki Ishiba
- Department of Surgery (Breast), Tokyo Metropolitan Cancer and Infectious Disease Center Komagome Hospital, Tokyo 113-8677, Japan; (M.A.); (T.A.)
- Department of Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (S.M.); (K.H.); (Y.K.); (G.O.)
| | - Sakiko Maruya
- Department of Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (S.M.); (K.H.); (Y.K.); (G.O.)
| | - Kumiko Hayashi
- Department of Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (S.M.); (K.H.); (Y.K.); (G.O.)
| | - Yuichi Kumaki
- Department of Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (S.M.); (K.H.); (Y.K.); (G.O.)
| | - Goshi Oda
- Department of Breast Surgery, Tokyo Medical and Dental University, Tokyo 113-8510, Japan; (S.M.); (K.H.); (Y.K.); (G.O.)
| | - Tomoyuki Aruga
- Department of Surgery (Breast), Tokyo Metropolitan Cancer and Infectious Disease Center Komagome Hospital, Tokyo 113-8677, Japan; (M.A.); (T.A.)
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Lloyd R, Pirikahu S, Walter J, Cadby G, Warrington N, Perera D, Hickey M, Saunders C, Hackmann M, Sampson DD, Shepherd J, Lilge L, Stone J. The Prospective Association between Early Life Growth and Breast Density in Young Adult Women. Cancers (Basel) 2024; 16:2418. [PMID: 39001479 PMCID: PMC11240569 DOI: 10.3390/cancers16132418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/24/2024] [Accepted: 06/26/2024] [Indexed: 07/16/2024] Open
Abstract
Breast density is a strong intermediate endpoint to investigate the association between early-life exposures and breast cancer risk. This study investigates the association between early-life growth and breast density in young adult women measured using Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA). OBS measurements were obtained for 536 female Raine Cohort Study participants at ages 27-28, with 268 completing DXA measurements. Participants with three or more height and weight measurements from ages 8 to 22 were used to generate linear growth curves for height, weight and body mass index (BMI) using SITAR modelling. Three growth parameters (size, velocity and timing) were examined for association with breast density measures, adjusting for potential confounders. Women who reached their peak height rapidly (velocity) and later in adolescence (timing) had lower OBS-breast density. Overall, women who were taller (size) had higher OBS-breast density. For weight, women who grew quickly (velocity) and later in adolescence (timing) had higher absolute DXA-breast density. Overall, weight (size) was also inversely associated with absolute DXA-breast density, as was BMI. These findings provide new evidence that adolescent growth is associated with breast density measures in young adult women, suggesting potential mediation pathways for breast cancer risk in later life.
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Affiliation(s)
- Rachel Lloyd
- Genetic Epidemiology Group, School of Population and Global Health, The University of Western Australia, Perth, WA 6009, Australia
| | - Sarah Pirikahu
- Genetic Epidemiology Group, School of Population and Global Health, The University of Western Australia, Perth, WA 6009, Australia
| | - Jane Walter
- University Health Network, Toronto, ON M5G 2C4, Canada
| | - Gemma Cadby
- Genetic Epidemiology Group, School of Population and Global Health, The University of Western Australia, Perth, WA 6009, Australia
| | - Nicole Warrington
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4067, Australia
- The Frazer Institute, The University of Queensland, Woolloongabba, QLD 4102, Australia
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic Epidemiology, Norwegian University of Science and Technology, NO-7491 Trondheim, Norway
| | - Dilukshi Perera
- Genetic Epidemiology Group, School of Population and Global Health, The University of Western Australia, Perth, WA 6009, Australia
| | - Martha Hickey
- Department of Obstetrics and Gynaecology, University of Melbourne and the Royal Women's Hospital, Melbourne, VIC 3052, Australia
| | - Christobel Saunders
- Department of Surgery, The University of Melbourne, Parkville, VIC 3052, Australia
| | - Michael Hackmann
- School of Human Sciences, The University of Western Australia, Crawley, WA 6009, Australia
| | - David D Sampson
- School of Computer Science and Electronic Engineering, The University of Surrey, Guildford, Surrey GU2 7XH, UK
| | - John Shepherd
- Epidemiology and Population Sciences in the Pacific Program, University of Hawaii Cancer Center, Honolulu, HI 96813, USA
| | - Lothar Lilge
- University Health Network, Toronto, ON M5G 2C4, Canada
- Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, The University of Western Australia, Perth, WA 6009, Australia
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Bergan MB, Larsen M, Moshina N, Bartsch H, Koch HW, Aase HS, Satybaldinov Z, Haldorsen IHS, Lee CI, Hofvind S. AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway. Eur Radiol 2024:10.1007/s00330-024-10681-z. [PMID: 38528136 DOI: 10.1007/s00330-024-10681-z] [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: 09/25/2023] [Revised: 01/19/2024] [Accepted: 02/10/2024] [Indexed: 03/27/2024]
Abstract
OBJECTIVE To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. MATERIALS AND METHOD We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG. RESULTS We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4. CONCLUSION The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. CLINICAL RELEVANCE STATEMENT Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. KEY POINTS • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.
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Affiliation(s)
- Marie Burns Bergan
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Nataliia Moshina
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway
| | - Hauke Bartsch
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
| | - Henrik Wethe Koch
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
| | | | - Zhanbolat Satybaldinov
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
| | - Ingfrid Helene Salvesen Haldorsen
- Department of Radiology, Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Bergen, Norway
- Section for Radiology, Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Christoph I Lee
- Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, WA, USA
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Norwegian Institute of Public Health, P.O. Box 5313, 0304, Oslo, Norway.
- Department of Health and Care Sciences, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway.
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Koch HW, Larsen M, Bartsch H, Kurz KD, Hofvind S. Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases. Eur Radiol 2023; 33:3735-3743. [PMID: 36917260 PMCID: PMC10121532 DOI: 10.1007/s00330-023-09461-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 12/13/2022] [Accepted: 01/24/2023] [Indexed: 03/16/2023]
Abstract
OBJECTIVES To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists. METHODS In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratified by mammographic density. RESULTS A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round. CONCLUSIONS The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading. KEY POINTS • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading.
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Affiliation(s)
- Henrik Wethe Koch
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
| | - Marthe Larsen
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
| | - Hauke Bartsch
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kathinka Dæhli Kurz
- Department of Radiology, Stavanger University Hospital, Stavanger, Norway
- Department of Electrical Engineering and Computer Science, Faculty of Science and Technology, The University of Stavanger, Stavanger, Norway
| | - Solveig Hofvind
- Section for Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway.
- Department of Health and Care Sciences, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway.
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Hofvind S, Moshina N, Holen ÅS, Danielsen AS, Lee CI, Houssami N, Aase HS, Akslen LA, Haldorsen IS. Interval and Subsequent Round Breast Cancer in a Randomized Controlled Trial Comparing Digital Breast Tomosynthesis and Digital Mammography Screening. Radiology 2021; 300:66-76. [PMID: 33973840 DOI: 10.1148/radiol.2021203936] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Prevalent digital breast tomosynthesis (DBT) has shown higher cancer detection rates and lower recall rates compared with those of digital mammography (DM). However, data are limited on rates and histopathologic tumor characteristics of interval and subsequent round screen-detected cancers for DBT. Purpose To follow women randomized to screening with DBT or DM and to investigate rates and tumor characteristics of interval and subsequent round screen-detected cancers. Materials and Methods To-Be is a randomized controlled trial comparing the outcome of DBT and DM in organized breast cancer screening. The trial included 28 749 women, with 22 306 women returning for subsequent DBT screening 2 years later (11 201 and 11 105 originally screened with DBT and DM, respectively). Differences in rates, means, and distribution of histopathologic tumor characteristics between women prevalently screened with DBT versus DM were evaluated with Z tests, t tests, and χ2 tests. Relative risk (RR) with 95% CIs was calculated for the cancer rates. Results Interval cancer rates were 1.4 per 1000 screens (20 of 14 380; 95% CI: 0.9, 2.1) for DBT versus 2.0 per 1000 screens (29 of 14 369; 95% CI: 1.4, 2.9; P = .20) for DM. The rates of subsequent round screen-detected cancer were 8.1 per 1000 (95% CI: 6.6, 10.0) for women originally screened with DBT and 9.1 per 1000 (95% CI: 7.4, 11.0; P = .43) for women screened with DM. The distribution of tumor characteristics did not differ between groups for either interval or subsequent screen-detected cancer. The RR of interval cancer was 0.69 (95% CI: 0.39, 1.22; P = .20) for DBT versus DM, whereas RR of subsequent screen-detected cancer for women prevalently screened with DBT versus DM was 0.89 (95% CI: 0.67, 1.19; P = .43). Conclusion Rates of interval or subsequent round screen-detected cancers and their tumor characteristics did not differ between women originally screened with digital breast tomosynthesis (DBT) versus digital mammography. The analysis suggests that the benefits of prevalent DBT screening did not come at the expense of worse downstream screening performance measures in a population-based screening program. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Taourel in this issue.
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Affiliation(s)
- Solveig Hofvind
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Nataliia Moshina
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Åsne S Holen
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Anders S Danielsen
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Christoph I Lee
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Nehmat Houssami
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Hildegunn S Aase
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Lars A Akslen
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
| | - Ingfrid S Haldorsen
- From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway
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Moshina N, Larsen M, Holen ÅS, Waade GG, Aase HS, Hofvind S. Digital breast tomosynthesis in a population based mammographic screening program: Breast compression and early performance measures. Eur J Radiol 2021; 139:109665. [PMID: 33823373 DOI: 10.1016/j.ejrad.2021.109665] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 03/11/2021] [Accepted: 03/14/2021] [Indexed: 11/24/2022]
Abstract
PURPOSE We aimed to determine if compression force or pressure could be associated with early performance measures for women screened with digital breast tomosynthesis (DBT) in BreastScreen Norway. Early performance measures included rates of consensus, recall, and screen-detected breast cancer. METHOD Data on compression force and pressure, compressed breast thickness and breast characteristics were extracted from an automated software for density assessment of DBT screening examinations for 25,286 women. For descriptive analyses, force (Newton, N) and pressure (kilopascal, kPa) were categorized into quartiles. Analyses were stratified by mammographic view, craniocaudal (CC) and mediolateral oblique (MLO). Logistic regression with restricted cubic splines was used to investigate the association between force and pressure as continuous exposures and early performance measures adjusted for age, compressed breast thickness and fibroglandular volume. RESULTS Mean age of the screened women was 60.7 (SD = 5.2) years. Mean compression force was 90.8 (SD = 14.2) N for CC and 106.3 (SD = 20.6) N for MLO, and pressure was 11.3 (SD = 3.6) kPa for CC and 8.7 (SD = 2.0) kPa for MLO. The highest rates of screen-detected cancer were observed for low force (1.04 % for <82.5 N for CC and 1.07 % for <92.0 N for MLO) and low pressure (1.07 % for <7.2 kPa for MLO). No association was found between force or pressure as continuous exposures and early performance measures in adjusted regression analyses. CONCLUSIONS We found the highest rates of screen-detected cancer for low force and pressure, but no significant association between continuous values of force or pressure and early performance measures in DBT. The findings might indicate that the levels of force and pressure in DBT are of lower significance for screening performance than reported in standard digital mammography.
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Affiliation(s)
| | | | | | - Gunvor G Waade
- Cancer Registry of Norway, Oslo, Norway; Faculty of Health Sciences Oslo Metropolitan University, Oslo, Norway.
| | - Hildegunn S Aase
- Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Solveig Hofvind
- Cancer Registry of Norway, Oslo, Norway; Faculty of Health Sciences Oslo Metropolitan University, Oslo, Norway.
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7
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Pubertal mammary gland development is a key determinant of adult mammographic density. Semin Cell Dev Biol 2020; 114:143-158. [PMID: 33309487 DOI: 10.1016/j.semcdb.2020.11.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 11/25/2020] [Accepted: 11/28/2020] [Indexed: 01/04/2023]
Abstract
Mammographic density refers to the radiological appearance of fibroglandular and adipose tissue on a mammogram of the breast. Women with relatively high mammographic density for their age and body mass index are at significantly higher risk for breast cancer. The association between mammographic density and breast cancer risk is well-established, however the molecular and cellular events that lead to the development of high mammographic density are yet to be elucidated. Puberty is a critical time for breast development, where endocrine and paracrine signalling drive development of the mammary gland epithelium, stroma, and adipose tissue. As the relative abundance of these cell types determines the radiological appearance of the adult breast, puberty should be considered as a key developmental stage in the establishment of mammographic density. Epidemiological studies have pointed to the significance of pubertal adipose tissue deposition, as well as timing of menarche and thelarche, on adult mammographic density and breast cancer risk. Activation of hypothalamic-pituitary axes during puberty combined with genetic and epigenetic molecular determinants, together with stromal fibroblasts, extracellular matrix, and immune signalling factors in the mammary gland, act in concert to drive breast development and the relative abundance of different cell types in the adult breast. Here, we discuss the key cellular and molecular mechanisms through which pubertal mammary gland development may affect adult mammographic density and cancer risk.
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8
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Moshina N, Aase HS, Danielsen AS, Haldorsen IS, Lee CI, Zackrisson S, Hofvind S. Comparing Screening Outcomes for Digital Breast Tomosynthesis and Digital Mammography by Automated Breast Density in a Randomized Controlled Trial: Results from the To-Be Trial. Radiology 2020; 297:522-531. [DOI: 10.1148/radiol.2020201150] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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9
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Vilmun BM, Vejborg I, Lynge E, Lillholm M, Nielsen M, Nielsen MB, Carlsen JF. Impact of adding breast density to breast cancer risk models: A systematic review. Eur J Radiol 2020; 127:109019. [DOI: 10.1016/j.ejrad.2020.109019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 04/10/2020] [Accepted: 04/13/2020] [Indexed: 01/19/2023]
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Yaghjyan L, Wijayabahu A, Eliassen AH, Colditz G, Rosner B, Tamimi RM. Associations of aspirin and other anti-inflammatory medications with mammographic breast density and breast cancer risk. Cancer Causes Control 2020; 31:827-837. [PMID: 32476101 DOI: 10.1007/s10552-020-01321-0] [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: 03/06/2020] [Accepted: 05/26/2020] [Indexed: 11/24/2022]
Abstract
PURPOSE We investigated the associations of aspirin and other non-steroid anti-inflammatory drugs with mammographic breast density (MBD) and their interactions in relation to breast cancer risk. METHODS This study included 3,675 cancer-free women within the Nurses' Health Study (NHS) and Nurses' Health Study II (NHSII) cohorts. Percent breast density (PD), absolute dense area (DA), and non-dense area (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root-transformed. Information on medication use was collected in 1980 (NHS) and 1989 (NHSII) and updated biennially. Medication use was defined as none, past or current; average cumulative dose and frequency were calculated for all past or current users from all bi-annual questionnaires preceding the mammogram date. We used generalized linear regression to quantify associations of medications with MBD. Two-way interactions were examined in logistic regression models. RESULTS In multivariate analysis, none of the anti-inflammatory medications were associated with PD, DA, and NDA. We found no interactions of any of the medications with PD with respect to breast cancer risk (all p-interactions > 0.05). However, some of the aspirin variables appeared to have positive associations with breast cancer risk limited only to women with PD 10-24% (past aspirin OR 1.56, 95% CI 1.03-2.35; current aspirin with < 5 years of use OR 1.82, 95% CI 1.01-3.28; current aspirin with ≥ 5 years of use OR 1.89, 95% CI 1.26-2.82). CONCLUSIONS Aspirin and NSAIDs are not associated with breast density measures. We found no interactions of aspirin with MBD in relation to breast cancer risk.
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Affiliation(s)
- Lusine Yaghjyan
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, USA.
| | - Akemi Wijayabahu
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Rd, Gainesville, FL, 32610, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Graham Colditz
- Department of Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.,Institute for Public Health, Washington University in St. Louis, St. Louis, MO, USA
| | - Bernard Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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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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12
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Engmann NJ, Scott CG, Jensen MR, Winham S, Miglioretti DL, Ma L, Brandt K, Mahmoudzadeh A, Whaley DH, Hruska C, Wu F, Norman AD, Hiatt RA, Heine J, Shepherd J, Pankratz VS, Vachon CM, Kerlikowske K. Combined effect of volumetric breast density and body mass index on breast cancer risk. Breast Cancer Res Treat 2019; 177:165-173. [PMID: 31129803 DOI: 10.1007/s10549-019-05283-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 05/16/2019] [Indexed: 01/16/2023]
Abstract
BACKGROUND Breast density and body mass index (BMI) are used for breast cancer risk stratification. We evaluate whether the positive association between volumetric breast density and breast cancer risk is strengthened with increasing BMI. METHODS The San Francisco Mammography Registry and Mayo Clinic Rochester identified 781 premenopausal and 1850 postmenopausal women with breast cancer diagnosed between 2007 and 2015 that had a screening digital mammogram at least 6 months prior to diagnosis. Up to three controls (N = 3535) were matched per case on age, race, date, mammography machine, and state. Volumetric percent density (VPD) and dense volume (DV) were measured with Volpara™. Breast cancer risk was assessed with logistic regression stratified by menopause status. Multiplicative interaction tests assessed whether the association of density measures was differential by BMI categories. RESULTS The increased risk of breast cancer associated with VPD was strengthened with higher BMI for both premenopausal (pinteraction = 0.01) and postmenopausal (pinteraction = 0.0003) women. For BMI < 25, 25-30, and ≥ 30 kg/m2, ORs for breast cancer for a 1 SD increase in VPD were 1.24, 1.65, and 1.97 for premenopausal, and 1.20, 1.55, and 2.25 for postmenopausal women, respectively. ORs for breast cancer for a 1 SD increase in DV were 1.39, 1.33, and 1.51 for premenopausal (pinteraction = 0.58), and 1.31, 1.34, and 1.65 (pinteraction = 0.03) for postmenopausal women for BMI < 25, 25-30 and ≥ 30 kg/m2, respectively. CONCLUSIONS The effect of volumetric percent density on breast cancer risk is strongest in overweight and obese women. These associations have clinical relevance for informing prevention strategies.
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Affiliation(s)
| | | | | | | | - Diana L Miglioretti
- University of California, Davis, USA.,Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Lin Ma
- Kaiser Permanente Division of Research, Oakland, CA, USA
| | | | | | | | | | | | | | - Robert A Hiatt
- Department of Medicine and Epidemiology & Biostatistics, University of California, San Francisco, USA
| | | | | | - V Shane Pankratz
- University of New Mexico Health Sciences Center, Albuquerque, USA
| | | | - Karla Kerlikowske
- Department of Medicine and Epidemiology & Biostatistics, University of California, San Francisco, USA.
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13
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A new automated method to evaluate 2D mammographic breast density according to BI-RADS® Atlas Fifth Edition recommendations. Eur Radiol 2019; 29:3830-3838. [DOI: 10.1007/s00330-019-06016-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 12/13/2018] [Accepted: 01/17/2019] [Indexed: 10/27/2022]
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14
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15
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Aase HS, Holen ÅS, Pedersen K, Houssami N, Haldorsen IS, Sebuødegård S, Hanestad B, Hofvind S. A randomized controlled trial of digital breast tomosynthesis versus digital mammography in population-based screening in Bergen: interim analysis of performance indicators from the To-Be trial. Eur Radiol 2018; 29:1175-1186. [PMID: 30159620 PMCID: PMC6510877 DOI: 10.1007/s00330-018-5690-x] [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: 05/01/2018] [Revised: 07/19/2018] [Accepted: 07/31/2018] [Indexed: 12/21/2022]
Abstract
Objectives To describe a randomized controlled trial (RCT) of digital breast tomosynthesis including synthesized two-dimensional mammograms (DBT) versus digital mammography (DM) in a population-based screening program for breast cancer and to compare selected secondary screening outcomes for the two techniques. Methods This RCT, performed in Bergen as part of BreastScreen Norway, was approved by the Regional Committees for Medical Health Research Ethics. All screening attendees in Bergen were invited to participate, of which 89% (14,274/15,976) concented during the first year, and were randomized to DBT (n = 7155) or DM (n = 7119). Secondary screening outcomes were stratified by mammographic density and compared using two-sample t-tests, chi-square tests, ANOVA, negative binomial regression and tests of proportions (z tests). Results Mean reading time was 1 min 11 s for DBT and 41 s for DM (p < 0.01). Mean time spent at consensus was 3 min 12 s for DBT and 2 min 12 s for DM (p < 0.01), while the rate of cases discussed at consensus was 6.4% and 7.4%, respectively for DBT and DM (p = 0.03). The recall rate was 3.0% for DBT and 3.6% for DM (p = 0.03). For women with non-dense breasts, recall rate was 2.2% for DBT versus 3.4% for DM (p = 0.04). The rate did not differ for women with dense breasts (3.6% for both). Mean glandular dose per examination was 2.96 mGy for DBT and 2.95 mGy for DM (p = 0.433). Conclusions Interim analysis of a screening RCT showed that DBT took longer to read than DM, but had significantly lower recall rate than DM. We found no differences in radiation dose between the two techniques. Key Points • In this RCT, DBT was associated with longer interpretation time than DM • Recall rates were lower for DBT than for DM • Mean glandular radiation dose did not differ between DBT and DM
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Affiliation(s)
- Hildegunn S Aase
- Department of Radiology, Haukeland University Hospital, 5021, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, 5020, Bergen, Norway
| | - Åsne S Holen
- Cancer Registry of Norway, P.O. 5313, 0304, Majorstuen, Oslo, Norway
| | - Kristin Pedersen
- Cancer Registry of Norway, P.O. 5313, 0304, Majorstuen, Oslo, Norway
| | - Nehmat Houssami
- Sydney School of Public Health, Sydney Medical School, University of Sydney, Camperdown, Australia
| | - Ingfrid S Haldorsen
- Department of Radiology, Haukeland University Hospital, 5021, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, 5020, Bergen, Norway
| | - Sofie Sebuødegård
- Cancer Registry of Norway, P.O. 5313, 0304, Majorstuen, Oslo, Norway
| | - Berit Hanestad
- Department of Radiology, Haukeland University Hospital, 5021, Bergen, Norway
| | - Solveig Hofvind
- Cancer Registry of Norway, P.O. 5313, 0304, Majorstuen, Oslo, Norway. .,Oslo Metropolitan University, Oslo, Norway.
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16
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Moshina N, Sebuødegård S, Lee CI, Akslen LA, Tsuruda KM, Elmore JG, Hofvind S. Automated Volumetric Analysis of Mammographic Density in a Screening Setting: Worse Outcomes for Women with Dense Breasts. Radiology 2018; 288:343-352. [DOI: 10.1148/radiol.2018172972] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Nataliia Moshina
- From the Cancer Registry of Norway, Oslo, Norway (N.M., S.S., K.M.T., S.H.); Departments of Radiology (C.I.L.) and Medicine (J.G.E.), University of Washington School of Medicine, Seattle, Wash; Department of Clinical Medicine, Section for Pathology, Centre for Cancer Biomarkers (CCBIO), Bergen, Norway (L.A.A.); Department of Pathology, Haukeland University Hospital, Bergen, Norway (L.A.A.); and Oslo Metropolitan University, Faculty of Health Science, Oslo, Norway (S.H.)
| | - Sofie Sebuødegård
- From the Cancer Registry of Norway, Oslo, Norway (N.M., S.S., K.M.T., S.H.); Departments of Radiology (C.I.L.) and Medicine (J.G.E.), University of Washington School of Medicine, Seattle, Wash; Department of Clinical Medicine, Section for Pathology, Centre for Cancer Biomarkers (CCBIO), Bergen, Norway (L.A.A.); Department of Pathology, Haukeland University Hospital, Bergen, Norway (L.A.A.); and Oslo Metropolitan University, Faculty of Health Science, Oslo, Norway (S.H.)
| | - Christoph I. Lee
- From the Cancer Registry of Norway, Oslo, Norway (N.M., S.S., K.M.T., S.H.); Departments of Radiology (C.I.L.) and Medicine (J.G.E.), University of Washington School of Medicine, Seattle, Wash; Department of Clinical Medicine, Section for Pathology, Centre for Cancer Biomarkers (CCBIO), Bergen, Norway (L.A.A.); Department of Pathology, Haukeland University Hospital, Bergen, Norway (L.A.A.); and Oslo Metropolitan University, Faculty of Health Science, Oslo, Norway (S.H.)
| | - Lars A. Akslen
- From the Cancer Registry of Norway, Oslo, Norway (N.M., S.S., K.M.T., S.H.); Departments of Radiology (C.I.L.) and Medicine (J.G.E.), University of Washington School of Medicine, Seattle, Wash; Department of Clinical Medicine, Section for Pathology, Centre for Cancer Biomarkers (CCBIO), Bergen, Norway (L.A.A.); Department of Pathology, Haukeland University Hospital, Bergen, Norway (L.A.A.); and Oslo Metropolitan University, Faculty of Health Science, Oslo, Norway (S.H.)
| | - Kaitlyn M. Tsuruda
- From the Cancer Registry of Norway, Oslo, Norway (N.M., S.S., K.M.T., S.H.); Departments of Radiology (C.I.L.) and Medicine (J.G.E.), University of Washington School of Medicine, Seattle, Wash; Department of Clinical Medicine, Section for Pathology, Centre for Cancer Biomarkers (CCBIO), Bergen, Norway (L.A.A.); Department of Pathology, Haukeland University Hospital, Bergen, Norway (L.A.A.); and Oslo Metropolitan University, Faculty of Health Science, Oslo, Norway (S.H.)
| | - Joann G. Elmore
- From the Cancer Registry of Norway, Oslo, Norway (N.M., S.S., K.M.T., S.H.); Departments of Radiology (C.I.L.) and Medicine (J.G.E.), University of Washington School of Medicine, Seattle, Wash; Department of Clinical Medicine, Section for Pathology, Centre for Cancer Biomarkers (CCBIO), Bergen, Norway (L.A.A.); Department of Pathology, Haukeland University Hospital, Bergen, Norway (L.A.A.); and Oslo Metropolitan University, Faculty of Health Science, Oslo, Norway (S.H.)
| | - Solveig Hofvind
- From the Cancer Registry of Norway, Oslo, Norway (N.M., S.S., K.M.T., S.H.); Departments of Radiology (C.I.L.) and Medicine (J.G.E.), University of Washington School of Medicine, Seattle, Wash; Department of Clinical Medicine, Section for Pathology, Centre for Cancer Biomarkers (CCBIO), Bergen, Norway (L.A.A.); Department of Pathology, Haukeland University Hospital, Bergen, Norway (L.A.A.); and Oslo Metropolitan University, Faculty of Health Science, Oslo, Norway (S.H.)
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17
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Physical activity and mammographic density in an Asian multi-ethnic cohort. Cancer Causes Control 2018; 29:883-894. [DOI: 10.1007/s10552-018-1064-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2017] [Accepted: 07/25/2018] [Indexed: 01/14/2023]
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18
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Yaghjyan L, Colditz G, Eliassen H, Rosner B, Gasparova A, Tamimi RM. Interactions of alcohol and postmenopausal hormone use in regards to mammographic breast density. Cancer Causes Control 2018; 29:751-758. [PMID: 29938357 DOI: 10.1007/s10552-018-1053-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2018] [Accepted: 06/20/2018] [Indexed: 12/12/2022]
Abstract
PURPOSE We investigated the association of alcohol intake with mammographic breast density in postmenopausal women by their hormone therapy (HT) status. METHODS This study included 2,100 cancer-free postmenopausal women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were measured from digitized film mammograms using a computer-assisted thresholding technique; all measures were square root transformed. Alcohol consumption was assessed with a food frequency questionnaire (0, < 5, and ≥ 5 g/day). Information regarding breast cancer risk factors was obtained from baseline or biennial questionnaires closest to the mammogram date. We used generalized linear regression to examine associations between alcohol and breast density measures in women with no HT history, current, and past HT users. RESULTS In multivariable analyses, we found no associations of alcohol consumption with PD (p trend = 0.32) and DA (p trend = 0.53) and an inverse association with NDA (β = - 0.41, 95% CI - 0.73, - 0.09 for ≥ 5 g/day, p trend < 0.01). In the stratified analysis by HT status, alcohol was not associated with PD in any of the strata. We found a significant inverse association of alcohol with NDA among past HT users (β = - 0.79, 95% CI - 1.51, - 0.07 for ≥ 5 g/day, p trend = 0.02). There were no significant interactions between alcohol and HT in relation to PD, DA, and NDA (p interaction = 0.19, 0.42, and 0.43, respectively). CONCLUSIONS Our findings suggest that associations of alcohol with breast density do not vary by HT status.
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Affiliation(s)
- Lusine Yaghjyan
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Rd., Gainesville, FL, 32610, USA.
| | - Graham Colditz
- Department of Surgery, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.,Institute for Public Health, Washington University in St. Louis, St. Louis, MO, USA
| | - Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Bernard Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Aleksandra Gasparova
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Rd., Gainesville, FL, 32610, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Hjerkind KV, Ellingjord-Dale M, Johansson AL, Aase HS, Hoff SR, Hofvind S, Fagerheim S, dos-Santos-Silva I, Ursin G. Volumetric Mammographic Density, Age-Related Decline, and Breast Cancer Risk Factors in a National Breast Cancer Screening Program. Cancer Epidemiol Biomarkers Prev 2018; 27:1065-1074. [DOI: 10.1158/1055-9965.epi-18-0151] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 04/25/2018] [Accepted: 06/15/2018] [Indexed: 11/16/2022] Open
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20
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Moshina N, Sebuødegård S, Holen ÅS, Waade GG, Tsuruda K, Hofvind S. The impact of compression force and pressure at prevalent screening on subsequent re-attendance in a national screening program. Prev Med 2018; 108:129-136. [PMID: 29337068 DOI: 10.1016/j.ypmed.2018.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 12/15/2017] [Accepted: 01/08/2018] [Indexed: 11/29/2022]
Abstract
Adherence to screening may indirectly help assess whether a prior screening examination deters women from returning for a subsequent examination. We investigated whether compression force and pressure in mammography were associated with re-attendance among prevalently screened women in the organized breast cancer screening program in Norway. Data on compression force (kg) and pressure (kPa) from women's first screening examination in the program (prevalent screening) and subsequent re-attendance were available for 31,225 women aged 50-68, screened during 2007-2013. Crude re-attendance rates and log-binomial regression models estimating the prevalence ratio of re-attendance were used to identify the association between compression force or pressure and re-attendance two-years later. Age and year at prevalent screening, county of residence, screening result (negative or false positive), breast volume, and breast density were included in analyses. Overall, 27,197 (87.1%) women re-attended the program. Re-attendance was highest for women who received a compression force of 10.0-13.9 kg (87.5%) or pressure of 9.0-17.9 kPa (87.8%) and lowest for those who received a compression force of <10.0 kg (85.0%) or pressure of <9.0 kPa (84.7%). The adjusted prevalence of re-attendance was 3% lower for women who received low compression force (<10.0 kg) and 2% lower for women who received low compression pressure (<9.0 kPa) relative to the reference groups (10.0-13.9 kg and 9.0-17.9 kPa, respectively). Future research related to re-attendance should also include information about women's experience of pain, anxiety and stress, as well as image quality.
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Affiliation(s)
- Nataliia Moshina
- Cancer Registry of Norway, P.O. 5313, Majorstuen, 0304 Oslo, Norway.
| | - Sofie Sebuødegård
- Cancer Registry of Norway, P.O. 5313, Majorstuen, 0304 Oslo, Norway.
| | - Åsne S Holen
- Cancer Registry of Norway, P.O. 5313, Majorstuen, 0304 Oslo, Norway.
| | - Gunvor G Waade
- Oslo and Akershus University College of Applied Sciences, P.O. 4, St. Olavs plass, 0130 Oslo, Norway.
| | - Kaitlyn Tsuruda
- Cancer Registry of Norway, P.O. 5313, Majorstuen, 0304 Oslo, Norway.
| | - Solveig Hofvind
- Cancer Registry of Norway, P.O. 5313, Majorstuen, 0304 Oslo, Norway; Oslo and Akershus University College of Applied Sciences, P.O. 4, St. Olavs plass, 0130 Oslo, Norway.
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21
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Association of multiple genetic variants with breast cancer susceptibility in the Han Chinese population. Oncotarget 2018; 7:85483-85491. [PMID: 27863437 PMCID: PMC5356751 DOI: 10.18632/oncotarget.13402] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2016] [Accepted: 10/19/2016] [Indexed: 12/22/2022] Open
Abstract
We selected 13 tag single nucleotide polymorphisms (tSNPs) to investigate whether they were associated with breast cancer risk in the Chinese Han population. Upon statistical analyses of clinical data from 551 patients and 577 controls, we found that six of the 13 SNPs were associated with breast cancer; namely, rs4973768(Odds ratio (OR) = 1.30, 95% confidence interval (CI) =1.01-1.67), rs981782(OR =1.30, 95% CI=1.01-1.66), rs1432679(OR =0.84, 95% CI=0.70-0.99), rs10759243(OR=1.30, 95%CI=1.09-1.55), rs10822013(OR =1.18, 95% CI=1.00-1.39) and rs704010(OR =1.63, 95% CI=1.04-2.56). When stratified based on breast cancer subtype, our analyses revealed that three SNPs (rs981782, rs10759243 and rs704010) correlated with ER+ breast cancer, while another three (rs4973768, rs1432679 and rs10822013) correlated with ER- breast cancer. We obtained similar results while investigating the correlation of SNPs with PR status or clinical stage. Our results suggest that associations identified between SNPs and breast cancer through genome-wide association studies (GWAS) may not always be generalizable across races.
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22
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McLean KE, Stone J. Role of breast density measurement in screening for breast cancer. Climacteric 2018; 21:214-220. [DOI: 10.1080/13697137.2018.1424816] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- K. E. McLean
- Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, Perth, WA, Australia
| | - J. Stone
- Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, Perth, WA, Australia
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23
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Byström S, Eklund M, Hong MG, Fredolini C, Eriksson M, Czene K, Hall P, Schwenk JM, Gabrielson M. Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density. Breast Cancer Res 2018; 20:14. [PMID: 29444691 PMCID: PMC5813412 DOI: 10.1186/s13058-018-0940-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 01/29/2018] [Indexed: 02/08/2023] Open
Abstract
Background Mammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density. Methods Plasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI). Results Plasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD. Conclusions Screening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women. Electronic supplementary material The online version of this article (10.1186/s13058-018-0940-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sanna Byström
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden
| | - Mun-Gwan Hong
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Claudia Fredolini
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden.,Department of Oncology, South General Hospital, Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Marike Gabrielson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12A, -171 77, Stockholm, SE, Sweden.
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Li S, Wei J, Chan HP, Helvie MA, Roubidoux MA, Lu Y, Zhou C, Hadjiiski LM, Samala RK. Computer-aided assessment of breast density: comparison of supervised deep learning and feature-based statistical learning. Phys Med Biol 2018; 63:025005. [PMID: 29210358 DOI: 10.1088/1361-6560/aa9f87] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Breast density is one of the most significant factors that is associated with cancer risk. In this study, our purpose was to develop a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammograms (DMs). The input 'for processing' DMs was first log-transformed, enhanced by a multi-resolution preprocessing scheme, and subsampled to a pixel size of 800 µm × 800 µm from 100 µm × 100 µm. A deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD) by using a domain adaptation resampling method. The PD was estimated as the ratio of the dense area to the breast area based on the PMD. The DCNN approach was compared to a feature-based statistical learning approach. Gray level, texture and morphological features were extracted and a least absolute shrinkage and selection operator was used to combine the features into a feature-based PMD. With approval of the Institutional Review Board, we retrospectively collected a training set of 478 DMs and an independent test set of 183 DMs from patient files in our institution. Two experienced mammography quality standards act radiologists interactively segmented PD as the reference standard. Ten-fold cross-validation was used for model selection and evaluation with the training set. With cross-validation, DCNN obtained a Dice's coefficient (DC) of 0.79 ± 0.13 and Pearson's correlation (r) of 0.97, whereas feature-based learning obtained DC = 0.72 ± 0.18 and r = 0.85. For the independent test set, DCNN achieved DC = 0.76 ± 0.09 and r = 0.94, while feature-based learning achieved DC = 0.62 ± 0.21 and r = 0.75. Our DCNN approach was significantly better and more robust than the feature-based learning approach for automated PD estimation on DMs, demonstrating its potential use for automated density reporting as well as for model-based risk prediction.
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Affiliation(s)
- Songfeng Li
- School of Mathematics, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
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25
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Rahbar K, Gubern-Merida A, Patrie JT, Harvey JA. Automated Volumetric Mammographic Breast Density Measurements May Underestimate Percent Breast Density for High-density Breasts. Acad Radiol 2017; 24:1561-1569. [PMID: 28754209 DOI: 10.1016/j.acra.2017.06.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 06/20/2017] [Accepted: 06/20/2017] [Indexed: 01/22/2023]
Abstract
RATIONALE AND OBJECTIVES The purpose of this study was to evaluate discrepancy in breast composition measurements obtained from mammograms using two commercially available software methods for systematic trends in overestimation or underestimation compared to magnetic resonance-derived measurements. MATERIALS AND METHODS An institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study was performed to calculate percent breast density (PBD) by quantifying fibroglandular volume and total breast volume derived from magnetic resonance imaging (MRI) segmentation and mammograms using two commercially available software programs (Volpara and Quantra). Consecutive screening MRI exams from a 6-month period with negative or benign findings were used. The most recent mammogram within 9 months was used to derive mean density values from "for processing" images at the per breast level. Bland-Altman statistical analyses were performed to determine the mean discrepancy and the limits of agreement. RESULTS A total of 110 women with 220 breasts met the study criteria. Overall, PBD was not different between MRI (mean 10%, range 1%-41%) and Volpara (mean 10%, range 3%-29%); a small but significant difference was present in the discrepancy between MRI and Quantra (4.0%, 95% CI: 2.9 to 5.0, P < 0.001). Discrepancy was highest at higher breast densities, with Volpara slightly underestimating and Quantra slightly overestimating PBD compared to MRI. The mean discrepancy for both Volpara and Quantra for total breast volume was not significantly different from MRI (p = 0.89, 0.35, respectively). Volpara tended to underestimate, whereas Quantra tended to overestimate fibroglandular volume, with the highest discrepancy at higher breast volumes. CONCLUSIONS Both Volpara and Quantra tend to underestimate PBD, which is most pronounced at higher densities. PBD can be accurately measured using automated volumetric software programs, but values should not be used interchangeably between vendors.
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Affiliation(s)
- Kareem Rahbar
- Roper Radiologists, P.A., Charleston, South Carolina
| | - Albert Gubern-Merida
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - James T Patrie
- Department of Biostatistics, University of Virginia Health System, Charlottesville, Virginia
| | - Jennifer A Harvey
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA 22908.
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26
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Abstract
Purpose Obesity and breast density are both associated with an increased risk of breast cancer and are potentially modifiable. Weight loss surgery (WLS) causes a significant reduction in the amount of body fat and a decrease in breast cancer risk. The effect of WLS on breast density and its components has not been documented. Here, we analyze the impact of WLS on volumetric breast density (VBD) and on each of its components (fibroglandular volume and breast volume) by using three-dimensional methods. Materials and Methods Fibroglandular volume, breast volume, and their ratio, the VBD, were calculated from mammograms before and after WLS by using Volpara™ automated software. Results For the 80 women included, average body mass index decreased from 46.0 ± 7.22 to 33.7 ± 7.06 kg/m2. Mammograms were performed on average 11.6 ± 9.4 months before and 10.1 ± 7 months after WLS. There was a significant reduction in average breast volume (39.4 % decrease) and average fibroglandular volume (15.5 % decrease), and thus, the average VBD increased from 5.15 to 7.87 % (p < 1 × 10−9) after WLS. When stratified by menopausal status and diabetic status, VBD increased significantly in all groups but only perimenopausal and postmenopausal women and non-diabetics experienced a significant reduction in fibroglandular volume. Conclusions Breast volume and fibroglandular volume decreased, and VBD increased following WLS, with the most significant change observed in postmenopausal women and non-diabetics. Further studies are warranted to determine how physical and biological alterations in breast density components after WLS may impact breast cancer risk.
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27
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Ali MA, Czene K, Eriksson L, Hall P, Humphreys K. Breast Tissue Organisation and its Association with Breast Cancer Risk. Breast Cancer Res 2017; 19:103. [PMID: 28877713 PMCID: PMC5586066 DOI: 10.1186/s13058-017-0894-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 08/07/2017] [Indexed: 11/13/2022] Open
Abstract
Background Mammographic percentage density is an established and important risk factor for breast cancer. In this paper, we investigate the role of the spatial organisation of (dense vs. fatty) regions of the breast defined from mammographic images in terms of breast cancer risk. Methods We present a novel approach that provides a thorough description of the spatial organisation of different types of tissue in the breast. Each mammogram is first segmented into four regions (fatty, semi-fatty, semi-dense and dense tissue). The spatial relations between each pair of regions is described using so-called forces histograms (FHs) and summarised using functional principal component analysis. In our main analysis, association with case–control status is assessed using a Swedish population-based case–control study (1,170 cases and 1283 controls), for which digitised mammograms were available. We also carried out a small validation study based on digital images. Results For our main analysis, we obtained a global p value of 2×10−7 indicating a significant association between the spatial relations of the four segmented regions and breast cancer status after adjustment for percentage density and other important breast cancer risk factors. Our (spatial relations) score had a per standard deviation odds ratio 1.29, after accounting for overfitting (percentage density had a per standard deviation odds ratio of 1.34). The spatial relations between the fatty and semi-fatty tissue and the spatial relations between the fatty and dense tissue were the most significant. The spatial relations between the fatty and semi-fatty tissue were associated with parity and age at first birth (p=6×10−4). Using digital images, we were able to verify that the same characteristics of tissue organisation can be identified and we validated the association for the spatial relations between the fatty and semi-fatty tissue. Conclusions Our findings are consistent with the notion that fibroglandular and adipose tissue plays a role in breast cancer risk and, more specifically, they suggest that fatty tissue in the lower quadrants and the absence of density in the retromammary space, as shown in mediolateral oblique images, are protective against breast cancer.
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Affiliation(s)
- Maya Alsheh Ali
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Swedish eScience Research Centre (SeRC), Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Louise Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology and Pathology, Cancer Centre Karolinska, Karolinska Institutet and University Hospital, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. .,Swedish eScience Research Centre (SeRC), Karolinska Institutet, Stockholm, Sweden.
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28
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Yaghjyan L, Tamimi RM, Bertrand KA, Scott CG, Jensen MR, Pankratz VS, Brandt K, Visscher D, Norman A, Couch F, Shepherd J, Fan B, Chen YY, Ma L, Beck AH, Cummings SR, Kerlikowske K, Vachon CM. Interaction of mammographic breast density with menopausal status and postmenopausal hormone use in relation to the risk of aggressive breast cancer subtypes. Breast Cancer Res Treat 2017; 165:421-431. [PMID: 28624977 PMCID: PMC5773252 DOI: 10.1007/s10549-017-4341-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 06/13/2017] [Indexed: 12/12/2022]
Abstract
PURPOSE We examined the associations of mammographic breast density with breast cancer risk by tumor aggressiveness and by menopausal status and current postmenopausal hormone therapy. METHODS This study included 2596 invasive breast cancer cases and 4059 controls selected from participants of four nested case-control studies within four established cohorts: the Mayo Mammography Health Study, the Nurses' Health Study, Nurses' Health Study II, and San Francisco Mammography Registry. Percent breast density (PD), absolute dense (DA), and non-dense areas (NDA) were assessed from digitized film-screen mammograms using a computer-assisted threshold technique and standardized across studies. We used polytomous logistic regression to quantify the associations of breast density with breast cancer risk by tumor aggressiveness (defined as presence of at least two of the following tumor characteristics: size ≥2 cm, grade 2/3, ER-negative status, or positive nodes), stratified by menopausal status and current hormone therapy. RESULTS Overall, the positive association of PD and borderline inverse association of NDA with breast cancer risk was stronger in aggressive vs. non-aggressive tumors (≥51 vs. 11-25% OR 2.50, 95% CI 1.94-3.22 vs. OR 2.03, 95% CI 1.70-2.43, p-heterogeneity = 0.03; NDA 4th vs. 2nd quartile OR 0.54, 95% CI 0.41-0.70 vs. OR 0.71, 95% CI 0.59-0.85, p-heterogeneity = 0.07). However, there were no differences in the association of DA with breast cancer by aggressive status. In the stratified analysis, there was also evidence of a stronger association of PD and NDA with aggressive tumors among postmenopausal women and, in particular, current estrogen+progesterone users (≥51 vs. 11-25% OR 3.24, 95% CI 1.75-6.00 vs. OR 1.93, 95% CI 1.25-2.98, p-heterogeneity = 0.01; NDA 4th vs. 2nd quartile OR 0.43, 95% CI 0.21-0.85 vs. OR 0.56, 95% CI 0.35-0.89, p-heterogeneity = 0.01), even though the interaction was not significant. CONCLUSION Our findings suggest that associations of mammographic density with breast cancer risk differ by tumor aggressiveness. While there was no strong evidence that these associations differed by menopausal status or hormone therapy, they did appear more prominent among current estrogen+progesterone users.
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Affiliation(s)
- Lusine Yaghjyan
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, 2004 Mowry Road, Gainesville, FL, 32610, USA.
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA, 02115, USA
- Department of Epidemiology, Harvard T.H Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | | | - Christopher G Scott
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
| | - Matthew R Jensen
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
| | - V Shane Pankratz
- Division of Biomedical Statistics and Informatics, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
| | - Kathy Brandt
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Daniel Visscher
- Department of Anatomic Pathology, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
| | - Aaron Norman
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
| | - Fergus Couch
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
- Division of Experimental Pathology, Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
| | - John Shepherd
- Department of Radiology, University of California, 1 Irving Street, AC109, San Francisco, CA, 94143, USA
| | - Bo Fan
- Department of Pathology, University of California, 505 Parnassus AvenueRoom M559, Box 0102, San Francisco, CA, 94143, USA
| | - Yunn-Yi Chen
- Department of Pathology, University of California, 505 Parnassus AvenueRoom M559, Box 0102, San Francisco, CA, 94143, USA
| | - Lin Ma
- Department of Medicine, University of California, 1635 Divisadero St. Suite 600, Box 1793, San Francisco, CA, USA
| | - Andrew H Beck
- Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, USA
| | - Steven R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, 475 Brannan Street, Suite 220, San Francisco, CA, 94107, USA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, 4150 Clement Street, Mailing Code 111A1, San Francisco, CA, 94121, USA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, 4150 Clement Street, Mailing Code 111A1, San Francisco, CA, 94121, USA
| | - Celine M Vachon
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA
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O'Flynn EA, Fromageau J, Ledger AE, Messa A, D'Aquino A, Schoemaker MJ, Schmidt M, Duric N, Swerdlow AJ, Bamber JC. Ultrasound Tomography Evaluation of Breast Density: A Comparison With Noncontrast Magnetic Resonance Imaging. Invest Radiol 2017; 52:343-348. [PMID: 28121639 PMCID: PMC5417582 DOI: 10.1097/rli.0000000000000347] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2016] [Indexed: 01/17/2023]
Abstract
OBJECTIVES Ultrasound tomography (UST) is an emerging whole-breast 3-dimensional imaging technique that obtains quantitative tomograms of speed of sound of the entire breast. The imaged parameter is the speed of sound which is used as a surrogate measure of density at each voxel and holds promise as a method to evaluate breast density without ionizing radiation. This study evaluated the technique of UST and compared whole-breast volume averaged speed of sound (VASS) with MR percent water content from noncontrast magnetic resonance imaging (MRI). MATERIALS AND METHODS Forty-three healthy female volunteers (median age, 40 years; range, 29-59 years) underwent bilateral breast UST and MRI using a 2-point Dixon technique. Reproducibility of VASS was evaluated using Bland-Altman analysis. Volume averaged speed of sound and MR percent water were evaluated and compared using Pearson correlation coefficient. RESULTS The mean ± standard deviation VASS measurement was 1463 ± 29 m s (range, 1434-1542 m s). There was high similarity between right (1464 ± 30 m s) and left (1462 ± 28 m s) breasts (P = 0.113) (intraclass correlation coefficient, 0.98). Mean MR percent water content was 35.7% ± 14.7% (range, 13.2%-75.3%), with small but significant differences between right and left breasts (36.3% ± 14.9% and 35.1% ± 14.7%, respectively; P = 0.004). There was a very strong correlation between VASS and MR percent water density (r = 0.96, P < 0.0001). CONCLUSIONS Ultrasound tomography holds promise as a reliable and reproducible 3-dimensional technique to provide a surrogate measure of breast density and correlates strongly with MR percent water content.
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Affiliation(s)
- Elizabeth A.M. O'Flynn
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Jeremie Fromageau
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Araminta E. Ledger
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Alessandro Messa
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Ashley D'Aquino
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Minouk J. Schoemaker
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Maria Schmidt
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Neb Duric
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Anthony J. Swerdlow
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
| | - Jeffrey C. Bamber
- From the *Cancer Research UK Cancer Imaging Centre; †Joint Department of Physics, Institute of Cancer Research and Royal Marsden NHS Foundation Trust; ‡Royal Marsden NHS Foundation Trust; §Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom; ∥Delphinus Medical Technologies, Karmanos Cancer Institute, Wayne State University, Detroit, MI; and ¶Division of Genetics and Epidemiology, and Division of Breast Cancer Research Institute of Cancer Research, London, United Kingdom
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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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Johansson H, von Tiedemann M, Erhard K, Heese H, Ding H, Molloi S, Fredenberg E. Breast-density measurement using photon-counting spectral mammography. Med Phys 2017; 44:3579-3593. [DOI: 10.1002/mp.12279] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2016] [Revised: 03/12/2017] [Accepted: 03/23/2017] [Indexed: 11/09/2022] Open
Affiliation(s)
- Henrik Johansson
- Philips Health Systems; Mammography Solutions; Torshamnsgatan 30A 164 40 Kista Sweden
| | - Miriam von Tiedemann
- Philips Health Systems; Mammography Solutions; Torshamnsgatan 30A 164 40 Kista Sweden
| | - Klaus Erhard
- Philips Research; Röntgenstrasse 24-26 22335 Hamburg Germany
| | - Harald Heese
- Philips Research; Röntgenstrasse 24-26 22335 Hamburg Germany
| | - Huanjun Ding
- Department of Radiological Sciences; University of California; Irvine CA 92697 USA
| | - Sabee Molloi
- Department of Radiological Sciences; University of California; Irvine CA 92697 USA
| | - Erik Fredenberg
- Philips Health Systems; Mammography Solutions; Torshamnsgatan 30A 164 40 Kista Sweden
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Waade GG, Highnam R, Hauge IHR, McEntee MF, Hofvind S, Denton E, Kelly J, Sarwar JJ, Hogg P. Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software. Med Phys 2017; 43:2870-2876. [PMID: 27277035 DOI: 10.1118/1.4948503] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
PURPOSE Mammographic density has been demonstrated to predict breast cancer risk. It has been proposed that it could be used for stratifying screening pathways and recommending additional imaging. Volumetric density tools use the recorded compressed breast thickness (CBT) of the breast measured at the x-ray unit in their calculation; however, the accuracy of the recorded thickness can vary. The aim of this study was to investigate whether inaccuracies in recorded CBT impact upon volumetric density classification and to examine whether the current quality control (QC) standard is sufficient for assessing mammographic density. METHODS Raw data from 52 digital screening mammograms were included in the study. For each image, the clinically recorded CBT was artificially increased and decreased in increments of 1 mm to simulate measurement error, until ±15% from the recorded CBT was reached. New images were created for each 1 mm step in thickness resulting in a total of 974 images which then had volpara density grade (VDG) and volumetric density percentage assigned. RESULTS A change in VDG was observed in 38.5% (n = 20) of mammograms when applying ±15% error to the recorded CBT and 11.5% (n = 6) was within the QC standard prescribed error of ±5 mm. CONCLUSIONS The current QC standard of ±5 mm error in recorded CBT creates the potential for error in mammographic density measurement. This may lead to inaccurate classification of mammographic density. The current QC standard for assessing mammographic density should be reconsidered.
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Affiliation(s)
- Gunvor Gipling Waade
- Department of Life Sciences and Health, Faculty of Health Sciences, Oslo and Akershus University College of Applied Sciences, Box 4, St. Olavs Plass, 0130, Oslo, Norway and School of Health Sciences, University of Salford, Salford M6 6PU, United Kingdom
| | - Ralph Highnam
- Volpara Solutions Limited, P.O. Box 24404, Manners St Central, Wellington 6142, New Zealand
| | - Ingrid H R Hauge
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, 4950 Nydalen, Norway
| | - Mark F McEntee
- Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, M205, Cumberland Campus, 75 East Street, Lidcombe, Sydney, NSW, 2141, Australia
| | - Solveig Hofvind
- Department of Screening, Cancer Registry of Norway, N-0304, Oslo, Norway and Department of Life Sciences and Health, Faculty of Health Sciences, Oslo and Akershus University College of Applied Sciences, Box 4, St. Olavs Plass, 0130, Oslo, Norway
| | - Erika Denton
- Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, United Kingdom
| | - Judith Kelly
- The Countess of Chester Hospitals NHS Foundation Trust, Chester, CH2 1UL, United Kingdom
| | - Jasmine J Sarwar
- School of Health Sciences, University of Salford, Salford M6 6PU, United Kingdom
| | - Peter Hogg
- School of Health Sciences, University of Salford, Salford M6 6PU, United Kingdom and Karolinska Institute, Stockholm, Sweden
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Digital volumetric measurement of mammographic density and the risk of overlooking cancer in Japanese women. Breast Cancer 2017; 24:708-713. [PMID: 28238177 DOI: 10.1007/s12282-017-0763-2] [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: 08/29/2016] [Accepted: 02/13/2017] [Indexed: 10/20/2022]
Abstract
BACKGROUND Breast density often affects cancer detection via mammography (MMG). Because of this, additional tests are recommended for women with dense breasts. This study aimed to reveal trends in breast density among Japanese women and determine whether differences in breast density differentially affected the detection of abnormalities via MMG. METHODS We retrospectively analyzed 397 control women who underwent MMG screening as well as 269 patients who underwent surgery for breast cancer for whom preoperative MMG data were available. VolparaDensity™ (Volpara), a three-dimensional image analysis software with high reproducibility, was used to calculate breast density. Breasts were categorized according to the volumetric density grade (VDG), a measure of the percentage of dense tissue. The associations between age, VDG, and MMG density categories were analyzed. RESULTS In the control group, 78% of women had dense breasts, while in the breast cancer group, 87% of patients had dense breasts. One of 36 patients with non-dense breasts (2.7%) was classified as category 1 or 2 (C-1 or C-2), indicating that abnormal findings could not be detected by MMG. The proportion of patients with breast cancer who had dense breasts and were classified as C-1 or C-2 was as high as 22.3%. CONCLUSIONS The proportions of Japanese women with dense breasts were high. In addition, the false-negative rate for women with dense breasts was also high. Owing to this, Japanese women with dense breasts may need to commonly undergo additional tests to ensure detection of breast cancer in the screening MMG.
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Martínez-Arroyo A, Moreno-Macías H, Scalabrino AP, Garmendia ML. Metabolic Syndrome and Mammographic Density in Premenopausal Chilean Women. Nutr Cancer 2017; 69:254-260. [DOI: 10.1080/01635581.2017.1263348] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Angela Martínez-Arroyo
- School of Nutrition and Dietetics, Faculty of Pharmacy, University of Valparaíso, Valparaíso, Chile
| | - Hortensia Moreno-Macías
- Division of Social Sciences and Humanities, Department of Economics, Metropolitan Autonomous University, Iztapalapa, Mexico
| | | | - Maria Luisa Garmendia
- Institute of Nutrition and Food Technology (INTA), University of Chile, Santiago, Chile
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McCarthy AM, Keller BM, Pantalone LM, Hsieh MK, Synnestvedt M, Conant EF, Armstrong K, Kontos D. Racial Differences in Quantitative Measures of Area and Volumetric Breast Density. J Natl Cancer Inst 2016; 108:djw104. [PMID: 27130893 PMCID: PMC5939658 DOI: 10.1093/jnci/djw104] [Citation(s) in RCA: 60] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Revised: 01/29/2016] [Accepted: 03/09/2016] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Increased breast density is a strong risk factor for breast cancer and also decreases the sensitivity of mammographic screening. The purpose of our study was to compare breast density for black and white women using quantitative measures. METHODS Breast density was assessed among 5282 black and 4216 white women screened using digital mammography. Breast Imaging-Reporting and Data System (BI-RADS) density was obtained from radiologists' reports. Quantitative measures for dense area, area percent density (PD), dense volume, and volume percent density were estimated using validated, automated software. Breast density was categorized as dense or nondense based on BI-RADS categories or based on values above and below the median for quantitative measures. Logistic regression was used to estimate the odds of having dense breasts by race, adjusted for age, body mass index (BMI), age at menarche, menopause status, family history of breast or ovarian cancer, parity and age at first birth, and current hormone replacement therapy (HRT) use. All statistical tests were two-sided. RESULTS There was a statistically significant interaction of race and BMI on breast density. After accounting for age, BMI, and breast cancer risk factors, black women had statistically significantly greater odds of high breast density across all quantitative measures (eg, PD nonobese odds ratio [OR] = 1.18, 95% confidence interval [CI] = 1.02 to 1.37, P = .03, PD obese OR = 1.26, 95% CI = 1.04 to 1.53, P = .02). There was no statistically significant difference in BI-RADS density by race. CONCLUSIONS After accounting for age, BMI, and other risk factors, black women had higher breast density than white women across all quantitative measures previously associated with breast cancer risk. These results may have implications for risk assessment and screening.
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Affiliation(s)
- Anne Marie McCarthy
- Department of Medicine, Massachusetts General Hospital, Boston, MA (AMM, KA); Department of Radiology, University of Pennsylvania, Philadelphia, PA (BMK, LMP, MKH, MS, EFC, DK)
| | - Brad M Keller
- Department of Medicine, Massachusetts General Hospital, Boston, MA (AMM, KA); Department of Radiology, University of Pennsylvania, Philadelphia, PA (BMK, LMP, MKH, MS, EFC, DK)
| | - Lauren M Pantalone
- Department of Medicine, Massachusetts General Hospital, Boston, MA (AMM, KA); Department of Radiology, University of Pennsylvania, Philadelphia, PA (BMK, LMP, MKH, MS, EFC, DK)
| | - Meng-Kang Hsieh
- Department of Medicine, Massachusetts General Hospital, Boston, MA (AMM, KA); Department of Radiology, University of Pennsylvania, Philadelphia, PA (BMK, LMP, MKH, MS, EFC, DK)
| | - Marie Synnestvedt
- Department of Medicine, Massachusetts General Hospital, Boston, MA (AMM, KA); Department of Radiology, University of Pennsylvania, Philadelphia, PA (BMK, LMP, MKH, MS, EFC, DK)
| | - Emily F Conant
- Department of Medicine, Massachusetts General Hospital, Boston, MA (AMM, KA); Department of Radiology, University of Pennsylvania, Philadelphia, PA (BMK, LMP, MKH, MS, EFC, DK)
| | - Katrina Armstrong
- Department of Medicine, Massachusetts General Hospital, Boston, MA (AMM, KA); Department of Radiology, University of Pennsylvania, Philadelphia, PA (BMK, LMP, MKH, MS, EFC, DK)
| | - Despina Kontos
- Department of Medicine, Massachusetts General Hospital, Boston, MA (AMM, KA); Department of Radiology, University of Pennsylvania, Philadelphia, PA (BMK, LMP, MKH, MS, EFC, DK)
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Busana MC, Eng A, Denholm R, Dowsett M, Vinnicombe S, Allen S, Dos-Santos-Silva I. Impact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case-control study. Breast Cancer Res 2016; 18:96. [PMID: 27670914 PMCID: PMC5037867 DOI: 10.1186/s13058-016-0756-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2016] [Accepted: 09/08/2016] [Indexed: 11/22/2022] Open
Abstract
Background Full-field digital mammography, which is gradually being introduced in most clinical and screening settings, produces two types of images: raw and processed. However, the extent to which mammographic density measurements, and their ability to predict breast cancer risk, vary according to type of image is not fully known. Methods We compared the performance of the semi-automated Cumulus method on digital raw, “analogue-like” raw and processed images, and the performance of a recently developed method - Laboratory for Breast Radiodensity Assessment (LIBRA) - on digital raw and processed images, in a case-control study (414 patients (cases) and 684 controls) by evaluating the extent to which their measurements were associated with breast cancer risk factors, and by comparing their ability to predict breast cancer risk. Results Valid Cumulus and LIBRA measurements were obtained from all available images, but the resulting distributions differed according to the method and type of image used. Both Cumulus and LIBRA percent density were inversely associated with age, body mass index (BMI), parity and postmenopausal status, regardless of type of image used. Cumulus percent density was strongly associated with breast cancer risk, but with the magnitude of the association slightly stronger for processed (risk increase per one SD increase in percent density (95 % CI): 1.55 (1.29, 1.85)) and “analogue-like” raw (1.52 (1.28, 1.80)) than for raw (1.35 (1.14, 1.60)) images. LIBRA percent density produced weaker associations with risk, albeit stronger for processed (1.32 (1.08, 1.61)) than raw images (1.17 (0.99, 1.37)). The percent density values yielded by the various density assessment/type of image combinations had similar ability to discriminate between patients and controls (area under the receiving operating curve values for percent density, age, BMI, parity and menopausal status combined ranged from 0.61 and 0.64). Conclusions The findings showed that Cumulus can be used to measure density on all types of digital images. They also indicate that LIBRA may provide a valid fully automated alternative to the more labour-intensive Cumulus. However, the same digital image type and assessment method should be used when examining mammographic density across populations, or longitudinal changes in density within a single population. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0756-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marta Cecilia Busana
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Amanda Eng
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.,Centre for Public Health Research, Massey University, Wellington, New Zealand
| | - Rachel Denholm
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Mitch Dowsett
- Academic Biochemistry, Royal Marsden Hospital, London, UK
| | - Sarah Vinnicombe
- Cancer Research, Ninewells Hospital Medical School, University of Dundee, Dundee, UK
| | - Steve Allen
- Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
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Yaghjyan L, Ghita GL, Rosner B, Farvid M, Bertrand KA, Tamimi RM. Adolescent fiber intake and mammographic breast density in premenopausal women. Breast Cancer Res 2016; 18:85. [PMID: 27520794 PMCID: PMC4983022 DOI: 10.1186/s13058-016-0747-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Accepted: 07/29/2016] [Indexed: 12/12/2022] Open
Abstract
Background To date, there is limited and inconsistent epidemiologic evidence for associations of adolescent diet with mammographic breast density, a strong and consistent predictor of breast cancer. We investigated the association of adolescent fiber intake with mammographic density in premenopausal women. Methods This study included 743 cancer-free premenopausal women (mean age, 44.9 years) within the Nurses’ Health Study II cohort. Percent breast density, absolute dense and non-dense areas were measured from digitized film mammograms using a computer-assisted thresholding technique. Adolescent and adult diet were assessed with a food frequency questionnaire; energy-adjusted nutrient intakes were estimated for each food item. Information regarding breast cancer risk factors was obtained from baseline or biennial questionnaires closest to the mammogram date. We used generalized linear regression to quantify associations between quartiles of adolescent fiber intake and each of the breast density measures, adjusted for potential confounders. Associations were examined separately for total fiber intake; fiber from fruits, vegetables, legumes, and cereal; and food sources of fiber (fruits, vegetables, and nuts). Results In multivariable analyses, total fiber intake during adolescence was not associated with percent breast density (p for trend = 0.64), absolute dense area (p for trend = 0.80), or non-dense area (p for trend = 0.75). Similarly, neither consumption of fiber from fruits, vegetables, legumes, or cereal nor specific sources of fiber intake (fruits, vegetables, or nuts) during adolescence were associated with any of the mammographic density phenotypes. Conclusions Our findings do not support the hypothesis that adolescent fiber intake is associated with premenopausal mammographic breast density.
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Affiliation(s)
- Lusine Yaghjyan
- Department of Epidemiology, University of Florida, College of Public Health and Health Professions and College of Medicine, 2004 Mowry Rd., Gainesville, 32610, FL, USA.
| | - Gabriela L Ghita
- Department of Biostatistics, University of Florida, College of Public Health and Health Professions and College of Medicine, Gainesville, FL, USA
| | - Bernard Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Maryam Farvid
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Harvard/Massachusetts General Hospital Center on Genomics, Vulnerable Populations, and Health Disparities, Mongan Institute for Health Policy, Massachusetts General Hospital, Boston, MA, USA
| | | | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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Reproductive factors related to childbearing and mammographic breast density. Breast Cancer Res Treat 2016; 158:351-9. [PMID: 27351801 DOI: 10.1007/s10549-016-3884-y] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 06/21/2016] [Indexed: 10/21/2022]
Abstract
We investigated the associations of reproductive factors related to childbearing with percent breast density, absolute dense and nondense areas, by menopausal status. This study included 4110 cancer-free women within the Nurses' Health Study and Nurses' Health Study II cohorts. Percent breast density, absolute dense and nondense areas were measured from digitized mammography film images with computerized techniques. All density measures were square root-transformed in all the analyses to improve normality. The data on reproductive variables and other breast cancer risk factors were obtained from biennial questionnaires, at the time of the mammogram date. As compared to nulliparous women, parous postmenopausal women had lower percent density (β = -0.60, 95 % CI -0.84; -0.37), smaller absolute dense area (β = -0.66, 95 % CI -1.03; -0.29), and greater nondense area (β = 0.72, 95 % CI 0.27; 1.16). Among parous women, number of children was inversely associated with percent density in pre- (β per one child = -0.12, 95 % CI -0.20; -0.05) and postmenopausal women (β per one child = -0.07, 95 % CI -0.12; -0.02). The positive associations of breastfeeding with absolute dense and nondense areas were limited to premenopausal women, while the positive association of the age at first child's birth with percent density and the inverse association with nondense area were limited to postmenopausal women. Women with greater number of children and younger age at first child's birth have more favorable breast density patterns that could explain subsequent breast cancer risk reduction.
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Lundberg FE, Johansson ALV, Rodriguez-Wallberg K, Brand JS, Czene K, Hall P, Iliadou AN. Association of infertility and fertility treatment with mammographic density in a large screening-based cohort of women: a cross-sectional study. Breast Cancer Res 2016; 18:36. [PMID: 27072636 PMCID: PMC4830010 DOI: 10.1186/s13058-016-0693-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 03/07/2016] [Indexed: 12/02/2022] Open
Abstract
Background Ovarian stimulation drugs, in particular hormonal agents used for controlled ovarian stimulation (COS) required to perform in vitro fertilization, increase estrogen and progesterone levels and have therefore been suspected to influence breast cancer risk. This study aims to investigate whether infertility and hormonal fertility treatment influences mammographic density, a strong hormone-responsive risk factor for breast cancer. Methods Cross-sectional study including 43,313 women recruited to the Karolinska Mammography Project between 2010 and 2013. Among women who reported having had infertility, 1576 had gone through COS, 1429 had had hormonal stimulation without COS and 5958 had not received any hormonal fertility treatment. Percent and absolute mammographic densities were obtained using the volumetric method Volpara™. Associations with mammographic density were assessed using multivariable generalized linear models, estimating mean differences (MD) with 95 % confidence intervals (CI). Results After multivariable adjustment, women with a history of infertility had 1.53 cm3 higher absolute dense volume compared to non-infertile women (95 % CI: 0.70 to 2.35). Among infertile women, only those who had gone through COS treatment had a higher absolute dense volume than those who had not received any hormone treatment (adjusted MD 3.22, 95 % CI: 1.10 to 5.33). No clear associations were observed between infertility, fertility treatment and percent volumetric density. Conclusions Overall, women reporting infertility had more dense tissue in the breast. The higher absolute dense volume in women treated with COS may indicate a treatment effect, although part of the association might also be due to the underlying infertility. Continued monitoring of cancer risk in infertile women, especially those who undergo COS, is warranted. Electronic supplementary material The online version of this article (doi:10.1186/s13058-016-0693-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Frida E Lundberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden.
| | - Anna L V Johansson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden
| | - Kenny Rodriguez-Wallberg
- Department of Oncology-Pathology, Karolinska Institutet and Reproductive Medicine, Karolinska University Hospital Huddinge, Stockholm, 141 86, Sweden
| | - Judith S Brand
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden
| | - Anastasia N Iliadou
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Stockholm, 171 77, Sweden
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40
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Volpara™ as a measurement tool for breast volume. J Plast Reconstr Aesthet Surg 2016; 69:581-2. [DOI: 10.1016/j.bjps.2015.11.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Revised: 11/18/2015] [Accepted: 11/28/2015] [Indexed: 11/17/2022]
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Assessing within-woman changes in mammographic density: a comparison of fully versus semi-automated area-based approaches. Cancer Causes Control 2016; 27:481-91. [PMID: 26847236 DOI: 10.1007/s10552-016-0722-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Accepted: 01/16/2016] [Indexed: 10/22/2022]
Abstract
BACKGROUND Mammographic density (MD) varies throughout a woman's life. We compared the performance of a fully automated (ImageJ-based) method to the observer-dependent Cumulus approach in the assessment of within-woman changes in MD over time. METHODS MD was assessed in annual pre-diagnostic films (from age 40 to early 50s) from 313 breast cancer cases and 452 matched controls using Cumulus (left medio-lateral oblique (MLO) readings) and the ImageJ-based method (mean left-right MLO readings). Linear mixed models were used to compare within-woman changes in MD among controls. Associations between individual-specific MD trajectories and breast cancer were examined using conditional logistic regression. RESULTS The age-related trajectories predicted by Cumulus and the ImageJ-based method were similar for all MD measures, except that the ImageJ-based method yielded slightly higher (by 2.54%, 95% CI 2.07%, 3.00%) estimates for percent MD. For both methods, the yearly rate of change in percent MD was twice faster after menopause than before, and higher BMI was associated with lower mean percent MD, but not associated with rate of change. Both methods yielded similar associations of individual-specific MD trajectories with breast cancer risk. CONCLUSIONS The ImageJ-based method is a valid fully automated alternative to Cumulus for measuring within-woman changes in MD in digitized films. The Age Trial is registered as an International Standard Randomized Controlled Trial, number ISRCTN24647151.
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Singh T, Sharma M, Singla V, Khandelwal N. Breast Density Estimation with Fully Automated Volumetric Method: Comparison to Radiologists' Assessment by BI-RADS Categories. Acad Radiol 2016; 23:78-83. [PMID: 26521687 DOI: 10.1016/j.acra.2015.09.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2015] [Revised: 09/17/2015] [Accepted: 09/20/2015] [Indexed: 10/22/2022]
Abstract
RATIONALE AND OBJECTIVES The objective of our study was to calculate mammographic breast density with a fully automated volumetric breast density measurement method and to compare it to breast imaging reporting and data system (BI-RADS) breast density categories assigned by two radiologists. MATERIALS AND METHODS A total of 476 full-field digital mammography examinations with standard mediolateral oblique and craniocaudal views were evaluated by two blinded radiologists and BI-RADS density categories were assigned. Using a fully automated software, mean fibroglandular tissue volume, mean breast volume, and mean volumetric breast density were calculated. Based on percentage volumetric breast density, a volumetric density grade was assigned from 1 to 4. RESULTS The weighted overall kappa was 0.895 (almost perfect agreement) for the two radiologists' BI-RADS density estimates. A statistically significant difference was seen in mean volumetric breast density among the BI-RADS density categories. With increased BI-RADS density category, increase in mean volumetric breast density was also seen (P < 0.001). A significant positive correlation was found between BI-RADS categories and volumetric density grading by fully automated software (ρ = 0.728, P < 0.001 for first radiologist and ρ = 0.725, P < 0.001 for second radiologist). Pairwise estimates of the weighted kappa between Volpara density grade and BI-RADS density category by two observers showed fair agreement (κ = 0.398 and 0.388, respectively). CONCLUSIONS In our study, a good correlation was seen between density grading using fully automated volumetric method and density grading using BI-RADS density categories assigned by the two radiologists. Thus, the fully automated volumetric method may be used to quantify breast density on routine mammography.
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Sak MA, Littrup PJ, Duric N, Mullooly M, Sherman ME, Gierach GL. Current and Future Methods for Measuring Breast Density: A Brief Comparative Review. BREAST CANCER MANAGEMENT 2015; 4:209-221. [PMID: 28943893 PMCID: PMC5609705 DOI: 10.2217/bmt.15.13] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Breast density is one of the strongest predictors of breast cancer risk. Women with the densest breasts are 4 to 6 times more likely to develop cancer compared with those with the lowest densities. Breast density is generally assessed using mammographic imaging; however, this approach has limitations. Magnetic resonance imaging and ultrasound tomography are some alternative imaging modalities that can aid mammography in patient screening and the measurement of breast density. As breast density becomes more commonly discussed, knowledge of the advantages and limitations of breast density as a marker of risk will become more critical. This review article discusses the relationship between breast density and breast cancer risk, lists the benefits and drawbacks of using multiple different imaging modalities to measure density and briefly discusses how breast density will be applied to aid in breast cancer prevention and treatment.
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Affiliation(s)
- Mark A Sak
- Karmanos Cancer Institute, Wayne State University, 4100 John R Street, Detroit MI 48201
| | - Peter J Littrup
- Delphinus Medical Technologies, 46701 Commerce Center Dr, Plymouth, MI, 48170
- Brown University, Rhode Island Hospital, 593 Eddy Street, Providence RI, 02903
| | - Neb Duric
- Karmanos Cancer Institute, Wayne State University, 4100 John R Street, Detroit MI 48201
- Delphinus Medical Technologies, 46701 Commerce Center Dr, Plymouth, MI, 48170
| | - Maeve Mullooly
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
- Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Mark E Sherman
- Breast and Gynecologic Cancer Research Group, Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Gretchen L Gierach
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
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Hart V, Reeves KW, Sturgeon SR, Reich NG, Sievert LL, Kerlikowske K, Ma L, Shepherd J, Tice JA, Mahmoudzadeh AP, Malkov S, Sprague BL. The effect of change in body mass index on volumetric measures of mammographic density. Cancer Epidemiol Biomarkers Prev 2015; 24:1724-30. [PMID: 26315554 DOI: 10.1158/1055-9965.epi-15-0330] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2015] [Accepted: 07/29/2015] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Understanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear. METHODS We examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007 to 2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV), and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history. RESULTS In cross-sectional analysis, BMI was positively associated with DV [β, 2.95 cm(3); 95% confidence interval (CI), 2.69-3.21] and inversely associated with PDV (β, -2.03%; 95% CI, -2.09, -1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (β, -1.01 cm(3); 95% CI, -1.59, -0.42) and PDV (β, -1.17%; 95% CI, -1.31, -1.04). These findings were consistent for both pre- and postmenopausal women. CONCLUSION Our findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation. IMPACT Longitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI.
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Affiliation(s)
- Vicki Hart
- Department of Surgery and Office of Health Promotion Research, University of Vermont, Burlington, Vermont. Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, Massachusetts.
| | - Katherine W Reeves
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, Massachusetts
| | - Susan R Sturgeon
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, Massachusetts
| | - Nicholas G Reich
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, Massachusetts
| | | | - Karla Kerlikowske
- Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, California
| | - Lin Ma
- Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, California
| | - John Shepherd
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Jeffrey A Tice
- Division of General Internal Medicine, Department of Medicine, University of California San Francisco, San Francisco, California
| | - Amir Pasha Mahmoudzadeh
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Serghei Malkov
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
| | - Brian L Sprague
- Department of Surgery and Office of Health Promotion Research, University of Vermont, Burlington, Vermont
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Keller BM, Chen J, Daye D, Conant EF, Kontos D. Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography. Breast Cancer Res 2015; 17:117. [PMID: 26303303 PMCID: PMC4549121 DOI: 10.1186/s13058-015-0626-8] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 08/04/2015] [Indexed: 12/30/2022] Open
Abstract
INTRODUCTION Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA). METHODS Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided. RESULTS All automated density measures had a significant association with breast cancer (OR = 1.47-2.23, AUC = 0.59-0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96-2.64, AUC = 0.82-0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80). CONCLUSIONS Our study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.
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Affiliation(s)
- Brad M Keller
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
| | - Jinbo Chen
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 203 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, USA.
| | - Dania Daye
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
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46
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Gierach GL, Patel DA, Falk RT, Pfeiffer RM, Geller BM, Vacek PM, Weaver DL, Chicoine RE, Shepherd JA, Mahmoudzadeh AP, Wang J, Fan B, Herschorn SD, Xu X, Veenstra T, Fuhrman B, Sherman ME, Brinton LA. Relationship of serum estrogens and metabolites with area and volume mammographic densities. HORMONES & CANCER 2015; 6:107-19. [PMID: 25757805 PMCID: PMC4558904 DOI: 10.1007/s12672-015-0216-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Accepted: 01/29/2015] [Indexed: 12/12/2022]
Abstract
Elevated mammographic density is a breast cancer risk factor, which has a suggestive, but unproven, relationship with increased exposure to sex steroid hormones. We examined associations of serum estrogens and estrogen metabolites with area and novel volume mammographic density measures among 187 women, ages 40-65, undergoing diagnostic breast biopsies at an academic facility in Vermont. Serum parent estrogens, estrone and estradiol, and their 2-, 4-, and 16-hydroxylated metabolites were measured using liquid chromatography-tandem mass spectrometry. Area mammographic density was measured in the breast contralateral to the biopsy using thresholding software; volume mammographic density was quantified using a density phantom. Linear regression was used to estimate associations of estrogens with mammographic densities, adjusted for age and body mass index, and stratified by menopausal status and menstrual cycle phase. Weak, positive associations between estrogens, estrogen metabolites, and mammographic density were observed, primarily among postmenopausal women. Among premenopausal luteal phase women, the 16-pathway metabolite estriol was associated with percent area (p = 0.04) and volume (p = 0.05) mammographic densities and absolute area (p = 0.02) and volume (p = 0.05) densities. Among postmenopausal women, levels of total estrogens, the sum of parent estrogens, and 2-, 4- and 16-hydroxylation pathway metabolites were positively associated with area density measures (percent: p = 0.03, p = 0.04, p = 0.01, p = 0.02, p = 0.07; absolute: p = 0.02, p = 0.02, p = 0.01, p = 0.02, p = 0.03, respectively) but not volume density measures. Our data suggest that serum estrogen profiles are weak determinants of mammographic density and that analysis of different density metrics may provide complementary information about relationships of estrogen exposure to breast tissue composition.
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Affiliation(s)
- Gretchen L. Gierach
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
- 9609 Medical Center Drive, Rm. 7-E108, Bethesda, MD 20892-9774 USA
| | - Deesha A. Patel
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - Roni T. Falk
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - Ruth M. Pfeiffer
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | | | | | | | | | | | | | - Jeff Wang
- University of California, San Francisco, San Francisco, CA USA
- Present Address: Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Bo Fan
- University of California, San Francisco, San Francisco, CA USA
| | | | - Xia Xu
- Laboratory of Proteomics and Analytical Technologies, Cancer Research Technology Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD USA
| | - Timothy Veenstra
- Laboratory of Proteomics and Analytical Technologies, Cancer Research Technology Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD USA
- Present Address: CN Diagnostics, 4041 Forest Park Avenue, Saint Louis, MO USA
| | - Barbara Fuhrman
- Department of Epidemiology, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR USA
| | - Mark E. Sherman
- Breast and Gynecologic Cancer Research Group, Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
| | - Louise A. Brinton
- Hormonal and Reproductive Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD USA
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Wanders JOP, Bakker MF, Veldhuis WB, Peeters PHM, van Gils CH. The effect of weight change on changes in breast density measures over menopause in a breast cancer screening cohort. Breast Cancer Res 2015; 17:74. [PMID: 26025139 PMCID: PMC4487974 DOI: 10.1186/s13058-015-0583-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Accepted: 05/13/2015] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION High weight and high percentage mammographic breast density are both breast cancer risk factors but are negatively correlated. Therefore, we wanted to obtain more insight into this apparent paradox. METHODS We investigated in a longitudinal study how weight change over menopause is related to changes in mammographic breast features. Five hundred ninety-one participants of the EPIC-NL cohort were divided into three groups according to their prospectively measured weight change over menopause: (1) weight loss (more than -3.0 %), (2) stable weight (between -3.0 % and +3.0 %), and (3) weight gain (more than 3.0 %). SPSS GLM univariate analysis was used to determine both the mean breast measure changes in, and the trend over, the weight change groups. RESULTS Over a median period of 5 years, the mean changes in percent density in these groups were -5.0 % (95 % confidence interval (CI) -8.0; -2.1), -6.8 % (95 % CI -9.0; -4.5), and -10.2 % (95 % CI -12.5; -7.9), respectively (P-trend = 0.001). The mean changes in dense area were -16.7 cm(2) (95 % CI -20.1; -13.4), -16.4 cm(2) (95 % CI -18.9; -13.9), and -18.1 cm(2) (95 % CI -20.6; -15.5), respectively (P-trend = 0.437). Finally, the mean changes in nondense area were -6.1 cm(2) (95 % CI -11.9; -0.4), -0.6 cm(2) (95 % CI -4.9; 3.8), and 5.3 cm(2) (95 % CI 0.9; 9.8), respectively (P-trend < 0.001). CONCLUSIONS Going through menopause is associated with a decrease in both percent density and dense area. Owing to an increase in the nondense tissue, the decrease in percent density is largest in women who gain weight. The decrease in dense area is not related to weight change. So the fact that both high percent density and high weight or weight gain are associated with high postmenopausal breast cancer risk can probably not be explained by an increase (or slower decrease) of dense area in women gaining weight compared with women losing weight or maintaining a stable weight. These results suggest that weight and dense area are presumably two independent postmenopausal breast cancer risk factors.
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Affiliation(s)
- Johanna Olga Pauline Wanders
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Str. 6.131, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands.
| | - Marije Fokje Bakker
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Str. 6.131, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands.
| | - Wouter Bernard Veldhuis
- Department of Radiology, University Medical Center Utrecht, Room E01.132, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands.
| | - Petra Huberdina Maria Peeters
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Str. 6.131, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands.
- MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, W2 1PG, London, UK.
| | - Carla Henrica van Gils
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Str. 6.131, P.O. Box 85500, 3508, GA, Utrecht, The Netherlands.
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Keller BM, McCarthy AM, Chen J, Armstrong K, Conant EF, Domchek SM, Kontos D. Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography. BMC Cancer 2015; 15:143. [PMID: 25881232 PMCID: PMC4365961 DOI: 10.1186/s12885-015-1159-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2014] [Accepted: 03/04/2015] [Indexed: 12/16/2022] Open
Abstract
Background Breast density and single-nucleotide polymorphisms (SNPs) have both been associated with breast cancer risk. To determine the extent to which these two breast cancer risk factors are associated, we investigate the association between a panel of validated SNPs related to breast cancer and quantitative measures of mammographic density in a cohort of Caucasian and African-American women. Methods In this IRB-approved, HIPAA-compliant study, we analyzed a screening population of 639 women (250 African American and 389 Caucasian) who were tested with a validated panel assay of 12 SNPs previously associated to breast cancer risk. Each woman underwent digital mammography as part of routine screening and all were interpreted as negative. Both absolute and percent estimates of area and volumetric density were quantified on a per-woman basis using validated software. Associations between the number of risk alleles in each SNP and the density measures were assessed through a race-stratified linear regression analysis, adjusted for age, BMI, and Gail lifetime risk. Results The majority of SNPs were not found to be associated with any measure of breast density. SNP rs3817198 (in LSP1) was significantly associated with both absolute area (p = 0.004) and volumetric (p = 0.019) breast density in Caucasian women. In African-American women, SNPs rs3803662 (in TNRC9/TOX3) and rs4973768 (in NEK10) were significantly associated with absolute (p = 0.042) and percent (p = 0.028) volume density respectively. Conclusions The majority of SNPs investigated in our study were not found to be significantly associated with breast density, even when accounting for age, BMI, and Gail risk, suggesting that these two different risk factors contain potentially independent information regarding a woman’s risk to develop breast cancer. Additionally, the few statistically significant associations between breast density and SNPs were different for Caucasian versus African American women. Larger prospective studies are warranted to validate our findings and determine potential implications for breast cancer risk assessment. Electronic supplementary material The online version of this article (doi:10.1186/s12885-015-1159-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Brad M Keller
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3600 Market St. Ste 360, Philadelphia, PA, 19104, USA.
| | - Anne Marie McCarthy
- Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
| | - Jinbo Chen
- Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
| | - Katrina Armstrong
- Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
| | - Emily F Conant
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3600 Market St. Ste 360, Philadelphia, PA, 19104, USA.
| | - Susan M Domchek
- Abramson Cancer Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3600 Market St. Ste 360, Philadelphia, PA, 19104, USA.
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Yaghjyan L, Colditz GA, Rosner B, Tamimi RM. Mammographic breast density and breast cancer risk: interactions of percent density, absolute dense, and non-dense areas with breast cancer risk factors. Breast Cancer Res Treat 2015; 150:181-9. [PMID: 25677739 DOI: 10.1007/s10549-015-3286-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2015] [Accepted: 01/24/2015] [Indexed: 12/20/2022]
Abstract
We investigated if associations of breast density and breast cancer differ according to the level of other known breast cancer risk factors, including body mass index (BMI), age at menarche, parity, age at first child's birth, age at menopause, alcohol consumption, a family history of breast cancer, a history of benign breast disease, and physical activity. This study included 1,044 postmenopausal incident breast cancer cases diagnosed within the Nurses' Health Study cohort and 1,794 matched controls. Percent breast density, absolute dense, and non-dense areas were measured from digitized film images with computerized techniques. Information on breast cancer risk factors was obtained prospectively from biennial questionnaires. Percent breast density was more strongly associated with breast cancer risk in current postmenopausal hormone users (≥50 vs. 10 %: OR 5.34, 95 % CI 3.36-8.49) as compared to women with past (OR 2.69, 95 % CI 1.32-5.49) or no hormone history (OR 2.57, 95 % CI 1.18-5.60, p-interaction = 0.03). Non-dense area was inversely associated with breast cancer risk in parous women, but not in women without children (p-interaction = 0.03). Associations of density with breast cancer risk did not differ by the levels of BMI, age at menarche, parity, age at first child's birth, age at menopause, alcohol consumption, a family history of breast cancer, a history of benign breast disease, and physical activity. Women with dense breasts, who currently use menopausal hormone therapy are at a particularly high risk of breast cancer. Most breast cancer risk factors do not modify the association between mammographic breast density and breast cancer risk.
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Affiliation(s)
- Lusine Yaghjyan
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, USA
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50
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Gilbert FJ, Tucker L, Gillan MG, Willsher P, Cooke J, Duncan KA, Michell MJ, Dobson HM, Lim YY, Purushothaman H, Strudley C, Astley SM, Morrish O, Young KC, Duffy SW. The TOMMY trial: a comparison of TOMosynthesis with digital MammographY in the UK NHS Breast Screening Programme--a multicentre retrospective reading study comparing the diagnostic performance of digital breast tomosynthesis and digital mammography with digital mammography alone. Health Technol Assess 2015; 19:i-xxv, 1-136. [PMID: 25599513 PMCID: PMC4781321 DOI: 10.3310/hta19040] [Citation(s) in RCA: 114] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Digital breast tomosynthesis (DBT) is a three-dimensional mammography technique with the potential to improve accuracy by improving differentiation between malignant and non-malignant lesions. OBJECTIVES The objectives of the study were to compare the diagnostic accuracy of DBT in conjunction with two-dimensional (2D) mammography or synthetic 2D mammography, against standard 2D mammography and to determine if DBT improves the accuracy of detection of different types of lesions. STUDY POPULATION Women (aged 47-73 years) recalled for further assessment after routine breast screening and women (aged 40-49 years) with moderate/high of risk of developing breast cancer attending annual mammography screening were recruited after giving written informed consent. INTERVENTION All participants underwent a two-view 2D mammography of both breasts and two-view DBT imaging. Image-processing software generated a synthetic 2D mammogram from the DBT data sets. RETROSPECTIVE READING STUDY In an independent blinded retrospective study, readers reviewed (1) 2D or (2) 2D + DBT or (3) synthetic 2D + DBT images for each case without access to original screening mammograms or prior examinations. Sensitivities and specificities were calculated for each reading arm and by subgroup analyses. RESULTS Data were available for 7060 subjects comprising 6020 (1158 cancers) assessment cases and 1040 (two cancers) family history screening cases. Overall sensitivity was 87% [95% confidence interval (CI) 85% to 89%] for 2D only, 89% (95% CI 87% to 91%) for 2D + DBT and 88% (95% CI 86% to 90%) for synthetic 2D + DBT. The difference in sensitivity between 2D and 2D + DBT was of borderline significance (p = 0.07) and for synthetic 2D + DBT there was no significant difference (p = 0.6). Specificity was 58% (95% CI 56% to 60%) for 2D, 69% (95% CI 67% to 71%) for 2D + DBT and 71% (95% CI 69% to 73%) for synthetic 2D + DBT. Specificity was significantly higher in both DBT reading arms for all subgroups of age, density and dominant radiological feature (p < 0.001 all cases). In all reading arms, specificity tended to be lower for microcalcifications and higher for distortion/asymmetry. Comparing 2D + DBT to 2D alone, sensitivity was significantly higher: 93% versus 86% (p < 0.001) for invasive tumours of size 11-20 mm. Similarly, for breast density 50% or more, sensitivities were 93% versus 86% (p = 0.03); for grade 2 invasive tumours, sensitivities were 91% versus 87% (p = 0.01); where the dominant radiological feature was a mass, sensitivities were 92% and 89% (p = 0.04) For synthetic 2D + DBT, there was significantly (p = 0.006) higher sensitivity than 2D alone in invasive cancers of size 11-20 mm, with a sensitivity of 91%. CONCLUSIONS The specificity of DBT and 2D was better than 2D alone but there was only marginal improvement in sensitivity. The performance of synthetic 2D appeared to be comparable to standard 2D. If these results were observed with screening cases, DBT and 2D mammography could benefit to the screening programme by reducing the number of women recalled unnecessarily, especially if a synthetic 2D mammogram were used to minimise radiation exposure. Further research is required into the feasibility of implementing DBT in a screening setting, prognostic modelling on outcomes and mortality, and comparison of 2D and synthetic 2D for different lesion types. STUDY REGISTRATION Current Controlled Trials ISRCTN73467396. FUNDING This project was funded by the NIHR Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 19, No. 4. See the HTA programme website for further project information.
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Affiliation(s)
- Fiona J Gilbert
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Lorraine Tucker
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Maureen Gc Gillan
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK
| | - Paula Willsher
- Department of Radiology, University of Cambridge, Cambridge, UK
| | | | - Karen A Duncan
- North East Scotland Breast Screening Centre, Aberdeen, UK
| | | | | | - Yit Yoong Lim
- The Nightingale Centre, University Hospital South Manchester, Manchester, UK
| | | | - Celia Strudley
- National Co-ordinating Centre for Physics of Mammography, Royal Surrey County Hospital, Guildford, UK
| | - Susan M Astley
- Department of Imaging Science and Biomedical Engineering, University of Manchester, Manchester, UK
| | - Oliver Morrish
- East Anglian Regional Radiation Protection Service, Cambridge University Hospitals, Cambridge, UK
| | - Kenneth C Young
- National Co-ordinating Centre for Physics of Mammography, Royal Surrey County Hospital, Guildford, UK
| | - Stephen W Duffy
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
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