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Ye Z, Nguyen TL, Dite GS, MacInnis RJ, Hopper JL, Li S. Mammographic Texture versus Conventional Cumulus Measure of Density in Breast Cancer Risk Prediction: A Literature Review. Cancer Epidemiol Biomarkers Prev 2024; 33:989-998. [PMID: 38787323 DOI: 10.1158/1055-9965.epi-23-1365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/01/2024] [Accepted: 05/22/2024] [Indexed: 05/25/2024] Open
Abstract
Mammographic textures show promise as breast cancer risk predictors, distinct from mammographic density. Yet, there is a lack of comprehensive evidence to determine the relative strengths as risk predictor of textures and density and the reliability of texture-based measures. We searched the PubMed database for research published up to November 2023, which assessed breast cancer risk associations [odds ratios (OR)] with texture-based measures and percent mammographic density (PMD), and their discrimination [area under the receiver operating characteristics curve (AUC)], using same datasets. Of 11 publications, for textures, six found stronger associations (P < 0.05) with 11% to 508% increases on the log scale by study, and four found weaker associations (P < 0.05) with 14% to 100% decreases, compared with PMD. Risk associations remained significant when fitting textures and PMD together. Eleven of 17 publications found greater AUCs for textures than PMD (P < 0.05); increases were 0.04 to 0.25 by study. Discrimination from PMD and these textures jointly was significantly higher than from PMD alone (P < 0.05). Therefore, different textures could capture distinct breast cancer risk information, partially independent of mammographic density, suggesting their joint role in breast cancer risk prediction. Some textures could outperform mammographic density for predicting breast cancer risk. However, obtaining reliable texture-based measures necessitates addressing various issues. Collaboration of researchers from diverse fields could be beneficial for advancing this complex field.
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Affiliation(s)
- Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
- Genetic Technologies Limited, Fitzroy, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, East Melbourne, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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Watt GP, Keshavamurthy KN, Nguyen TL, Lobbes MBI, Jochelson MS, Sung JS, Moskowitz CS, Patel P, Liang X, Woods M, Hopper JL, Pike MC, Bernstein JL. Association of breast cancer with quantitative mammographic density measures for women receiving contrast-enhanced mammography. JNCI Cancer Spectr 2024; 8:pkae026. [PMID: 38565262 PMCID: PMC11060476 DOI: 10.1093/jncics/pkae026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/21/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024] Open
Abstract
Women with high mammographic density have an increased risk of breast cancer. They may be offered contrast-enhanced mammography to improve breast cancer screening performance. Using a cohort of women receiving contrast-enhanced mammography, we evaluated whether conventional and modified mammographic density measures were associated with breast cancer. Sixty-six patients with newly diagnosed unilateral breast cancer were frequency matched on the basis of age to 133 cancer-free control individuals. On low-energy craniocaudal contrast-enhanced mammograms (equivalent to standard mammograms), we measured quantitative mammographic density using CUMULUS software at the conventional intensity threshold ("Cumulus") and higher-than-conventional thresholds ("Altocumulus," "Cirrocumulus"). The measures were standardized to enable estimation of odds ratio per adjusted standard deviation (OPERA). In multivariable logistic regression of case-control status, only the highest-intensity measure (Cirrocumulus) was statistically significantly associated with breast cancer (OPERA = 1.40, 95% confidence interval = 1.04 to 1.89). Conventional Cumulus did not contribute to model fit. For women receiving contrast-enhanced mammography, Cirrocumulus mammographic density may better predict breast cancer than conventional quantitative mammographic density.
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Affiliation(s)
- Gordon P Watt
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Tuong L Nguyen
- Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Marc B I Lobbes
- Department of Medical Imaging, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Maxine S Jochelson
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Janice S Sung
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Prusha Patel
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Xiaolin Liang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meghan Woods
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John L Hopper
- Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Malcolm C Pike
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jonine L Bernstein
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Ye Z, Dite GS, Nguyen TL, MacInnis RJ, Schmidt DF, Makalic E, Al-Qershi OM, Nguyen-Dumont T, Goudey B, Stone J, Dowty JG, Giles GG, Southey MC, Hopper JL, Li S. Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures. Cancer Epidemiol Biomarkers Prev 2024; 33:306-313. [PMID: 38059829 DOI: 10.1158/1055-9965.epi-23-1012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/24/2023] [Accepted: 12/05/2023] [Indexed: 12/08/2023] Open
Abstract
BACKGROUND Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus. METHODS We measured Cirrus for 3,195 breast cancer-free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1,599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The SNP-based heritability was estimated for a subset of 2,356 participants. RESULTS There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52 (SE, 0.03) for MZ pairs and 0.16(SE, 0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32% (SE = 5%) of the variance, consistent with the SNP-based heritability of 36% (SE = 16%). CONCLUSION Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to nongenetic factors operating in utero or in early life that are shared by MZ twins. IMPACT Early-life factors, shared more by MZ pairs than DZ/non-twin sister pairs, could play a role in the variation in Cirrus, consistent with early life being recognized as a critical window of vulnerability to breast carcinogens.
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Affiliation(s)
- Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Genetic Technologies Limited, Fitzroy, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Osamah M Al-Qershi
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Tu Nguyen-Dumont
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
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Ye Z, Nguyen TL, Dite GS, MacInnis RJ, Schmidt DF, Makalic E, Al-Qershi OM, Bui M, Esser VFC, Dowty JG, Trinh HN, Evans CF, Tan M, Sung J, Jenkins MA, Giles GG, Southey MC, Hopper JL, Li S. Causal relationships between breast cancer risk factors based on mammographic features. Breast Cancer Res 2023; 25:127. [PMID: 37880807 PMCID: PMC10598934 DOI: 10.1186/s13058-023-01733-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/17/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology. METHODS We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method. RESULTS The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P < 0.005). We estimated that 28-92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively). CONCLUSIONS In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways.
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Affiliation(s)
- Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
- Genetic Technologies Limited, Fitzroy, VIC, 3065, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of IT, Monash University, Melbourne, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Osamah M Al-Qershi
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Ho N Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500, Sunway City, Malaysia
- School of Electrical and Computer Engineering, The University of Oklahoma, Norman, OK, 73019, USA
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, 08826, Korea
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, 3004, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3051, Australia.
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, 3168, Australia.
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, CB1 8RN, UK.
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, 3051, Australia.
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Hopper JL, Dowty JG, Nguyen TL, Li S, Dite GS, MacInnis RJ, Makalic E, Schmidt DF, Bui M, Stone J, Sung J, Jenkins MA, Giles GG, Southey MC, Mathews JD. Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks. Int J Epidemiol 2023; 52:1557-1568. [PMID: 37349888 PMCID: PMC10655167 DOI: 10.1093/ije/dyad086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 06/16/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered. DEVELOPMENT We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors. APPLICATION For female breast cancer, VALID quantified how much variance in risk is explained-at different ages-by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors. CONCLUSION VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk.
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Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Genetic Technologies Ltd., Fitzroy, VIC, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Jennifer Stone
- School of Population and Global Health, University of Western Australia, Perth, WA, Australia
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - John D Mathews
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
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Taylor CR, Monga N, Johnson C, Hawley JR, Patel M. Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions. Diagnostics (Basel) 2023; 13:2041. [PMID: 37370936 DOI: 10.3390/diagnostics13122041] [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: 04/20/2023] [Revised: 05/20/2023] [Accepted: 05/29/2023] [Indexed: 06/29/2023] Open
Abstract
Attempts to use computers to aid in the detection of breast malignancies date back more than 20 years. Despite significant interest and investment, this has historically led to minimal or no significant improvement in performance and outcomes with traditional computer-aided detection. However, recent advances in artificial intelligence and machine learning are now starting to deliver on the promise of improved performance. There are at present more than 20 FDA-approved AI applications for breast imaging, but adoption and utilization are widely variable and low overall. Breast imaging is unique and has aspects that create both opportunities and challenges for AI development and implementation. Breast cancer screening programs worldwide rely on screening mammography to reduce the morbidity and mortality of breast cancer, and many of the most exciting research projects and available AI applications focus on cancer detection for mammography. There are, however, multiple additional potential applications for AI in breast imaging, including decision support, risk assessment, breast density quantitation, workflow and triage, quality evaluation, response to neoadjuvant chemotherapy assessment, and image enhancement. In this review the current status, availability, and future directions of investigation of these applications are discussed, as well as the opportunities and barriers to more widespread utilization.
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Affiliation(s)
- Clayton R Taylor
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Natasha Monga
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Candise Johnson
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Jeffrey R Hawley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Mitva Patel
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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Watt GP, Knight JA, Nguyen TL, Reiner AS, Malone KE, John EM, Lynch CF, Brooks JD, Woods M, Liang X, Bernstein L, Pike MC, Hopper JL, Bernstein JL. Association of contralateral breast cancer risk with mammographic density defined at higher-than-conventional intensity thresholds. Int J Cancer 2022; 151:1304-1309. [PMID: 35315524 PMCID: PMC9420749 DOI: 10.1002/ijc.34001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 02/25/2022] [Accepted: 03/04/2022] [Indexed: 11/06/2022]
Abstract
Mammographic dense area (MDA) is an established predictor of future breast cancer risk. Recent studies have found that risk prediction might be improved by redefining MDA in effect at higher-than-conventional intensity thresholds. We assessed whether such higher-intensity MDA measures gave stronger prediction of subsequent contralateral breast cancer (CBC) risk using the Women's Environment, Cancer, and Radiation Epidemiology (WECARE) Study, a population-based CBC case-control study of ≥1 year survivors of unilateral breast cancer diagnosed between 1990 and 2008. Three measures of MDA for the unaffected contralateral breast were made at the conventional intensity threshold ("Cumulus") and at two sequentially higher-intensity thresholds ("Altocumulus" and "Cirrocumulus") using the CUMULUS software and mammograms taken up to 3 years prior to the first breast cancer diagnosis. The measures were fitted separately and together in multivariable-adjusted logistic regression models of CBC (252 CBC cases and 271 unilateral breast cancer controls). The strongest association with CBC was MDA defined using the highest intensity threshold, Cirrocumulus (odds ratio per adjusted SD [OPERA] 1.40, 95% CI 1.13-1.73); and the weakest association was MDA defined at the conventional threshold, Cumulus (1.32, 95% CI 1.05-1.66). In a model fitting the three measures together, the association of CBC with Cirrocumulus was unchanged (1.40, 95% CI 0.97-2.05), and the lower brightness measures did not contribute to the CBC model fit. These results suggest that MDA defined at a high-intensity threshold is a better predictor of CBC risk and has the potential to improve CBC risk stratification beyond conventional MDA measures.
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Affiliation(s)
- Gordon P. Watt
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Julia A. Knight
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Tuong L. Nguyen
- Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Anne S. Reiner
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Kathleen E. Malone
- Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Esther M. John
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California, United States of America
| | | | - Jennifer D. Brooks
- Dalla Lana School of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Meghan Woods
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Xiaolin Liang
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - Leslie Bernstein
- Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, California, United States of America
| | - Malcolm C. Pike
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
| | - John L. Hopper
- Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Jonine L. Bernstein
- Memorial Sloan Kettering Cancer Center, New York, New York, United States of America
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Li S, Nguyen TL, Nguyen-Dumont T, Dowty JG, Dite GS, Ye Z, Trinh HN, Evans CF, Tan M, Sung J, Jenkins MA, Giles GG, Hopper JL, Southey MC. Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk. Cancers (Basel) 2022; 14:2767. [PMID: 35681745 PMCID: PMC9179294 DOI: 10.3390/cancers14112767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 04/27/2022] [Accepted: 05/30/2022] [Indexed: 11/26/2022] Open
Abstract
Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05−0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the “white but not bright area” (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
| | - Tuong L. Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
| | - James G. Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Gillian S. Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Genetic Technologies Limited, Fitzroy, VIC 3065, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Ho N. Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Christopher F. Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Bandar Sunway 47500, Malaysia;
- School of Electrical and Computer Engineering, The University of Oklahoma, Norman, OK 73019, USA
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea;
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
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9
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Nguyen TL, Li S, Dowty JG, Dite GS, Ye Z, Nguyen-Dumont T, Trinh HN, Evans CF, Tan M, Sung J, Jenkins MA, Giles GG, Southey MC, Hopper JL. Familial Aspects of Mammographic Density Measures Associated with Breast Cancer Risk. Cancers (Basel) 2022; 14:1483. [PMID: 35326633 PMCID: PMC8946826 DOI: 10.3390/cancers14061483] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/22/2022] [Accepted: 03/08/2022] [Indexed: 01/10/2023] Open
Abstract
Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) for breast cancer based on mammographic density defined in effect by different levels of pixel brightness and adjusted for age and body mass index. We measured these MRS from digitized film mammograms for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated the correlations in relatives (r) and the proportion of variance due to genetic factors (heritability) using the software FISHER and predicted the familial risk ratio (FRR) associated with each MRS. The ρ estimates ranged from: 0.41 to 0.60 (standard error [SE] 0.02) for MZ pairs, 0.16 to 0.26 (SE 0.05) for DZ pairs, and 0.19 to 0.29 (SE 0.02) for sister pairs (including pairs of a twin and her non-twin sister), respectively. Heritability estimates were 39% to 69% under the classic twin model and 36% to 56% when allowing for shared non-genetic factors specific to MZ pairs. The FRRs were 1.08 to 1.17. These MRSs are substantially familial, due mostly to genetic factors that explain one-quarter to one-half as much of the familial aggregation of breast cancer that is explained by the current best polygenic risk score.
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Affiliation(s)
- Tuong L. Nguyen
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC 3010, Australia; (T.L.N.); (S.L.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC 3010, Australia; (T.L.N.); (S.L.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Precision Medicine Group, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
| | - James G. Dowty
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC 3010, Australia; (T.L.N.); (S.L.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Gillian S. Dite
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC 3010, Australia; (T.L.N.); (S.L.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Genetic Technologies Limited, Melbourne, VIC 3065, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC 3010, Australia; (T.L.N.); (S.L.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Tu Nguyen-Dumont
- Precision Medicine Group, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Ho N. Trinh
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC 3010, Australia; (T.L.N.); (S.L.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Christopher F. Evans
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC 3010, Australia; (T.L.N.); (S.L.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Bandar Sunway 47500, Malaysia;
- School of Electrical and Computer Engineering, The University of Oklahoma, Norman, OK 73019, USA
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea;
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC 3010, Australia; (T.L.N.); (S.L.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC 3010, Australia; (T.L.N.); (S.L.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Precision Medicine Group, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - Melissa C. Southey
- Precision Medicine Group, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC 3010, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC 3010, Australia; (T.L.N.); (S.L.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
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10
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Hopper JL, Nguyen TL, Li S. RE: Chemopreventive Agents to Reduce Mammographic Breast Density in Premenopausal Women: A Systematic Review of Clinical Trials. JNCI Cancer Spectr 2021; 5:pkab051. [PMID: 34377932 PMCID: PMC8346692 DOI: 10.1093/jncics/pkab051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 03/12/2021] [Indexed: 11/26/2022] Open
Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
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11
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Nguyen TL, Schmidt DF, Makalic E, Maskarinec G, Li S, Dite GS, Aung YK, Evans CF, Trinh HN, Baglietto L, Stone J, Song YM, Sung J, MacInnis RJ, Dugué PA, Dowty JG, Jenkins MA, Milne RL, Southey MC, Giles GG, Hopper JL. Novel mammogram-based measures improve breast cancer risk prediction beyond an established mammographic density measure. Int J Cancer 2020; 148:2193-2202. [PMID: 33197272 DOI: 10.1002/ijc.33396] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 10/28/2020] [Accepted: 11/02/2020] [Indexed: 12/11/2022]
Abstract
Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1197 matched controls; and 354 younger-diagnosis cases and 944 controls frequency-matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We estimated measure-specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen-detected and younger-diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41-2.31]) and Cirrus (1.72 [1.38-2.14]); Cirrus (1.49 [1.32-1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27-1.68]), respectively. The AUCs were: 0.73 [0.68-0.77], 0.63 [0.60-0.66], and 0.72 [0.69-0.75], respectively. Combined, our new mammogram-based measures have twice the risk gradient for screen-detected and younger-diagnosis breast cancer (P ≤ 10-12 ), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk-based personalised breast screening.
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Affiliation(s)
- Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Daniel F Schmidt
- Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | | | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Ye K Aung
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Ho N Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Jennifer Stone
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, Western Australia, Australia
| | - Yun-Mi Song
- Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Joohon Sung
- Department of Epidemiology School of Public Health, Seoul National University, Seoul, South Korea.,Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Melissa C Southey
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia
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12
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Hopper JL, Nguyen TL, Schmidt DF, Makalic E, Song YM, Sung J, Dite GS, Dowty JG, Li S. Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk. J Clin Med 2020; 9:jcm9030627. [PMID: 32110975 PMCID: PMC7141100 DOI: 10.3390/jcm9030627] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 02/15/2020] [Accepted: 02/17/2020] [Indexed: 12/12/2022] Open
Abstract
This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.
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13
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Nguyen TL, Li S, Dite GS, Aung YK, Evans CF, Trinh HN, Baglietto L, Stone J, Song YM, Sung J, English DR, Jenkins MA, Dugué PA, Milne RL, Southey MC, Giles GG, Pike MC, Hopper JL. Interval breast cancer risk associations with breast density, family history and breast tissue aging. Int J Cancer 2019; 147:375-382. [PMID: 31609476 PMCID: PMC7318124 DOI: 10.1002/ijc.32731] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 09/16/2019] [Accepted: 09/27/2019] [Indexed: 01/04/2023]
Abstract
Interval breast cancers (those diagnosed between recommended mammography screens) generally have poorer outcomes and are more common among women with dense breasts. We aimed to develop a risk model for interval breast cancer. We conducted a nested case-control study within the Melbourne Collaborative Cohort Study involving 168 interval breast cancer patients and 498 matched control subjects. We measured breast density using the CUMULUS software. We recorded first-degree family history by questionnaire, measured body mass index (BMI) and calculated age-adjusted breast tissue aging, a novel measure of exposure to estrogen and progesterone based on the Pike model. We fitted conditional logistic regression to estimate odds ratio (OR) or odds ratio per adjusted standard deviation (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). The stronger risk associations were for unadjusted percent breast density (OPERA = 1.99; AUC = 0.66), more so after adjusting for age and BMI (OPERA = 2.26; AUC = 0.70), and for family history (OR = 2.70; AUC = 0.56). When the latter two factors and their multiplicative interactions with age-adjusted breast tissue aging (p = 0.01 and 0.02, respectively) were fitted, the AUC was 0.73 (95% CI 0.69-0.77), equivalent to a ninefold interquartile risk ratio. In summary, compared with using dense breasts alone, risk discrimination for interval breast cancers could be doubled by instead using breast density, BMI, family history and hormonal exposure. This would also give women with dense breasts, and their physicians, more information about the major consequence of having dense breasts-an increased risk of developing an interval breast cancer.
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Affiliation(s)
- Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Ye K Aung
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Ho N Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Perth, WA, Australia
| | - Yun-Mi Song
- Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Joohon Sung
- Department of Epidemiology School of Public Health, Seoul National University, Seoul, South Korea.,Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Malcolm C Pike
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, VIC, Australia
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14
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Li S, Dugué PA, Baglietto L, Severi G, Wong EM, Nguyen TL, Stone J, English DR, Southey MC, Giles GG, Hopper JL, Milne RL. Genome-wide association study of peripheral blood DNA methylation and conventional mammographic density measures. Int J Cancer 2019; 145:1768-1773. [PMID: 30694562 DOI: 10.1002/ijc.32171] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 01/18/2019] [Accepted: 01/22/2019] [Indexed: 02/06/2023]
Abstract
Age- and body mass index (BMI)-adjusted mammographic density is one of the strongest breast cancer risk factors. DNA methylation is a molecular mechanism that could underlie inter-individual variation in mammographic density. We aimed to investigate the association between breast cancer risk-predicting mammographic density measures and blood DNA methylation. For 436 women from the Australian Mammographic Density Twins and Sisters Study and 591 women from the Melbourne Collaborative Cohort Study, mammographic density (dense area, nondense area and percentage dense area) defined by the conventional brightness threshold was measured using the CUMULUS software, and peripheral blood DNA methylation was measured using the HumanMethylation450 (HM450) BeadChip assay. Associations between DNA methylation at >400,000 sites and mammographic density measures adjusted for age and BMI were assessed within each cohort and pooled using fixed-effect meta-analysis. Associations with methylation at genetic loci known to be associated with mammographic density were also examined. We found no genome-wide significant (p < 10-7 ) association for any mammographic density measure from the meta-analysis, or from the cohort-specific analyses. None of the 299 methylation sites located at genetic loci associated with mammographic density was associated with any mammographic density measure after adjusting for multiple testing (all p > 0.05/299 = 1.7 × 10-4 ). In summary, our study did not find evidence for associations between blood DNA methylation, as measured by the HM450 assay, and conventional mammographic density measures that predict breast cancer risk.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Gianluca Severi
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Centre de Recherche en Épidémiologie et Santé des Populations (INSERM U1018), Université Paris-Saclay, Université Paris-Sud, Université Versailles Saint-Quentin-en-Yvelines, Institut Gustave Roussy, Villejuif, France
| | - Ee Ming Wong
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Genetic Epidemiology Laboratory, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, WA, Australia
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.,Genetic Epidemiology Laboratory, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
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15
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Tan M, Mariapun S, Yip CH, Ng KH, Teo SH. A novel method of determining breast cancer risk using parenchymal textural analysis of mammography images on an Asian cohort. Phys Med Biol 2019; 64:035016. [PMID: 30577031 DOI: 10.1088/1361-6560/aafabd] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Historically, breast cancer risk prediction models are based on mammographic density measures, which are dichotomous in nature and generally categorize each voxel or area of the breast parenchyma as 'dense' or 'not dense'. Using these conventional methods, the structural patterns or textural components of the breast tissue elements are not considered or ignored entirely. This study presents a novel method to predict breast cancer risk that combines new texture and mammographic density based image features. We performed a comprehensive study of the correlation of 944 new and conventional texture and mammographic density features with breast cancer risk on a cohort of Asian women. We studied 250 breast cancer cases and 250 controls matched at full-field digital mammography (FFDM) status for age, BMI and ethnicity. Stepwise regression analysis identified relevant features to be included in a linear discriminant analysis (LDA) classifier model, trained and tested using a leave-one-out based cross-validation method. The area under the receiver operating characteristic (AUC) and adjusted odds ratios (ORs) were used as the two performance assessment indices in our study. For the LDA trained classifier, the adjusted OR was 6.15 (95% confidence interval: 3.55-10.64) and for Volpara volumetric breast density, 1.10 (0.67-1.81). The AUC for the LDA trained classifier was 0.68 (0.64-0.73), compared to 0.52 (0.47-0.57) for Volpara volumetric breast density (p < 0.001). The regression analysis of OR values for the LDA classifier also showed a significant increase in slope (p < 0.02). Mammographic texture features derived from digital mammograms are important quantitative measures for breast cancer risk assessment based models. Parenchymal texture analysis has an important role for stratifying breast cancer risk in women, which can be implemented to routine breast cancer screening strategies.
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Affiliation(s)
- Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, 47500 Bandar Sunway, Malaysia. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, United States of America
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16
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Nguyen TL, Aung YK, Li S, Trinh NH, Evans CF, Baglietto L, Krishnan K, Dite GS, Stone J, English DR, Song YM, Sung J, Jenkins MA, Southey MC, Giles GG, Hopper JL. Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds. Breast Cancer Res 2018; 20:152. [PMID: 30545395 PMCID: PMC6293866 DOI: 10.1186/s13058-018-1081-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 11/19/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Case-control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds. We asked if this applied to interval and/or screen-detected cancers. METHOD We conducted a nested case-control study within the prospective Melbourne Collaborative Cohort Study including 168 women with interval and 422 with screen-detected breast cancers, and 498 and 1197 matched controls, respectively. We measured absolute and percent mammographic density using the Cumulus software at the conventional threshold (Cumulus) and two increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were transformed and adjusted for age and body mass index (BMI). Using conditional logistic regression and adjusting for BMI by age at mammogram, we estimated risk discrimination by the odds ratio per adjusted standard deviation (OPERA), calculated the area under the receiver operating characteristic curve (AUC) and compared nested models using the likelihood ratio criterion and models with the same number of parameters using the difference in Bayesian information criterion (ΔBIC). RESULTS For interval cancer, there was very strong evidence that the association was best predicted by Cumulus as a percentage (OPERA = 2.33 (95% confidence interval (CI) 1.85-2.92); all ΔBIC > 14), and the association with BMI was independent of age at mammogram. After adjusting for percent Cumulus, no other measure was associated with risk (all P > 0.1). For screen-detected cancer, however, the associations were strongest for the absolute and percent Cirrocumulus measures (all ΔBIC > 6), and after adjusting for Cirrocumulus, no other measure was associated with risk (all P > 0.07). CONCLUSION The amount of brighter areas is the best mammogram-based measure of screen-detected breast cancer risk, while the percentage of the breast covered by white or bright areas is the best mammogram-based measure of interval breast cancer risk, irrespective of BMI. Therefore, there are different features of mammographic images that give clinically important information about different outcomes.
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Affiliation(s)
- Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Ye K Aung
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Nhut Ho Trinh
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Laura Baglietto
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia.,Department of Clinical and Experimental Medicine, University of Pisa, ᅟPisa, Italy
| | - Kavitha Krishnan
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Jennifer Stone
- Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Western WA, 6009, Australia
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Yun-Mi Song
- Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Irwon-ro 81, Gangnamgu, Seoul, 06351, South Korea
| | - Joohon Sung
- Department of Epidemiology School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Korea.,Institute of Health and Environment, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 151-742, Korea
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Melissa C Southey
- Department of Pathology, University of Melbourne, Carlton, Victoria, 3053, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia.,Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Level 3/207 Bouverie Street, Carlton, VIC, 3053, Australia.
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17
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Schmidt DF, Makalic E, Goudey B, Dite GS, Stone J, Nguyen TL, Dowty JG, Baglietto L, Southey MC, Maskarinec G, Giles GG, Hopper JL. Cirrus: An Automated Mammography-Based Measure of Breast Cancer Risk Based on Textural Features. JNCI Cancer Spectr 2018; 2:pky057. [PMID: 31360877 PMCID: PMC6649799 DOI: 10.1093/jncics/pky057] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 09/13/2018] [Accepted: 09/24/2018] [Indexed: 11/30/2022] Open
Abstract
Background We applied machine learning to find a novel breast cancer predictor based on information in a mammogram. Methods Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case–control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index. Results Combining studies, almost all textural features were associated with case–control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all P < 10−6). For the Cirrus measure derived from combining studies, the OR was 1.90 (95% confidence interval [CI] = 1.73 to 2.09), equivalent to a fourfold interquartile risk ratio, and was little attenuated after adjusting for conventional measures. In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 (95% CI = 1.08 to 1.24; P = 4 × 10−5). Conclusions A fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.
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Affiliation(s)
- Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Benjamin Goudey
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.,IBM Australia - Research, Southbank, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University, and the University of Western Australia, Perth, Western Australia, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Laura Baglietto
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Melissa C Southey
- Department of Pathology, University of Melbourne, Carlton, Victoria, Australia.,Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | | | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.,Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia
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18
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Wang C, Brentnall AR, Cuzick J, Harkness EF, Evans DG, Astley S. Exploring the prediction performance for breast cancer risk based on volumetric mammographic density at different thresholds. Breast Cancer Res 2018; 20:49. [PMID: 29884207 PMCID: PMC5994123 DOI: 10.1186/s13058-018-0979-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 05/08/2018] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The percentage of mammographic dense tissue (PD) defined by pixel value threshold is a well-established risk factor for breast cancer. Recently there has been some evidence to suggest that an increased threshold based on visual assessment could improve risk prediction. It is unknown, however, whether this also applies to volumetric density using digital raw mammograms. METHOD Two case-control studies nested within a screening cohort (ages of participants 46-73 years) from Manchester UK were used. In the first study (317 cases and 947 controls) cases were detected at the first screen; whereas in the second study (318 cases and 935 controls), cases were diagnosed after the initial mammogram. Volpara software was used to estimate dense tissue height at each pixel point, and from these, volumetric and area-based PD were computed at a range of thresholds. Volumetric and area-based PDs were evaluated using conditional logistic regression, and their predictive ability was assessed using the Akaike information criterion (AIC) and matched concordance index (mC). RESULTS The best performing volumetric PD was based on a threshold of 5 mm of dense tissue height (which we refer to as VPD5), and the best areal PD was at a threshold level of 6 mm (which we refer to as APD6), using pooled data and in both studies separately. VPD5 showed a modest improvement in prediction performance compared to the original volumetric PD by Volpara with ΔAIC = 5.90 for the pooled data. APD6, on the other hand, shows much stronger evidence for better prediction performance, with ΔAIC = 14.52 for the pooled data, and mC increased slightly from 0.567 to 0.577. CONCLUSION These results suggest that imposing a 5 mm threshold on dense tissue height for volumetric PD could result in better prediction of cancer risk. There is stronger evidence that area-based density with a 6 mm threshold gives better prediction than the original volumetric density metric.
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Affiliation(s)
- Chao Wang
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Adam R. Brentnall
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Jack Cuzick
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Elaine F. Harkness
- Centre for Imaging Science, School of Health Sciences, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT UK
| | - D. Gareth Evans
- Department of Genomic Medicine, University of Manchester, St Mary’s Hospital, M13 9WL, Manchester, UK
| | - Susan Astley
- Centre for Imaging Science, School of Health Sciences, University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT UK
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19
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Nguyen TL, Aung YK, Evans CF, Dite GS, Stone J, MacInnis RJ, Dowty JG, Bickerstaffe A, Aujard K, Rommens JM, Song YM, Sung J, Jenkins MA, Southey MC, Giles GG, Apicella C, Hopper JL. Mammographic density defined by higher than conventional brightness thresholds better predicts breast cancer risk. Int J Epidemiol 2018; 46:652-661. [PMID: 28338721 PMCID: PMC5837222 DOI: 10.1093/ije/dyw212] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2016] [Indexed: 11/24/2022] Open
Abstract
Background: Mammographic density defined by the conventional pixel brightness threshold, and adjusted for age and body mass index (BMI), is a well-established risk factor for breast cancer. We asked if higher thresholds better separate women with and without breast cancer. Methods: We studied Australian women, 354 with breast cancer over-sampled for early-onset and family history, and 944 unaffected controls frequency-matched for age at mammogram. We measured mammographic dense area and percent density using the CUMULUS software at the conventional threshold, which we call Cumulus, and at two increasingly higher thresholds, which we call Altocumulus and Cirrocumulus, respectively. All measures were Box–Cox transformed and adjusted for age and BMI. We estimated the odds per adjusted standard deviation (OPERA) using logistic regression and the area under the receiver operating characteristic curve (AUC). Results:Altocumulus and Cirrocumulus were correlated with Cumulus (r ∼ 0.8 and 0.6, respectively). For dense area, the OPERA was 1.62, 1.74 and 1.73 for Cumulus, Altocumulus and Cirrocumulus, respectively (all P < 0.001). After adjusting for Altocumulus and Cirrocumulus, Cumulus was not significant (P > 0.6). The OPERAs for percent density were less but gave similar findings. The mean of the standardized adjusted Altocumulus and Cirrocumulus dense area measures was the best predictor; OPERA = 1.87 [95% confidence interval (CI): 1.64–2.14] and AUC = 0.68 (0.65–0.71). Conclusions: The areas of higher mammographically dense regions are associated with almost 30% stronger breast cancer risk gradient, explain the risk association of the conventional measure and might be more aetiologically important. This has substantial implications for clinical translation and molecular, genetic and epidemiological research.
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Affiliation(s)
- Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia
| | - Ye K Aung
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia
| | - Christopher F Evans
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia
| | - Jennifer Stone
- Curtin UWA Centre for Genetic Origins of Health and Disease, Curtin University and The University of Western Australia, Perth, WA, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia
| | - Adrian Bickerstaffe
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia
| | - Kelly Aujard
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia
| | - Johanna M Rommens
- Program in Genetics and Genomic Biology, Hospital for Sick Children, Toronto, ON, Canada
| | - Yun-Mi Song
- Department of Family Medicine, Sungkyunkwan University School of Medicine, Seoul, South Korea and
| | - Joohon Sung
- Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea.,Institute of Health and Environment, Seoul National University, Seoul, Korea
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia
| | | | - Graham G Giles
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.,Cancer Council Victoria, Melbourne, VIC, Australia
| | - Carmel Apicella
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, University of Melbourne, Carlton, VIC, Australia.,Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea.,Institute of Health and Environment, Seoul National University, Seoul, Korea
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20
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Stone J. Should breast cancer screening programs routinely measure mammographic density? J Med Imaging Radiat Oncol 2018; 62:151-158. [DOI: 10.1111/1754-9485.12652] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 08/05/2017] [Indexed: 11/29/2022]
Affiliation(s)
- Jennifer Stone
- Centre for Genetic Origins of Health and Disease; Curtin University and The University of Western Australia; Perth Western Australia Australia
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21
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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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22
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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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23
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Tourell MC, Ali TS, Hugo HJ, Pyke C, Yang S, Lloyd T, Thompson EW, Momot KI. T 1 -based sensing of mammographic density using single-sided portable NMR. Magn Reson Med 2018; 80:1243-1251. [PMID: 29399874 DOI: 10.1002/mrm.27098] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 12/16/2017] [Accepted: 12/31/2017] [Indexed: 12/16/2022]
Affiliation(s)
- Monique C Tourell
- School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology (QUT), Brisbane, Australia.,Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, Australia
| | - Tonima S Ali
- School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology (QUT), Brisbane, Australia.,Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, Australia
| | - Honor J Hugo
- Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, Australia.,School of Biomedical Sciences, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Australia.,Translational Research Institute, Woolloongabba, Australia
| | - Chris Pyke
- Department of Surgery, Mater Hospital, University of Queensland, St Lucia, Australia
| | - Samuel Yang
- Department of Plastic and Reconstructive Surgery, Greenslopes Private Hospital, Brisbane, Australia
| | - Thomas Lloyd
- Division of Radiology, Princess Alexandra Hospital, Woolloongabba, Australia
| | - Erik W Thompson
- Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, Australia.,School of Biomedical Sciences, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Australia.,Translational Research Institute, Woolloongabba, Australia.,University of Melbourne Department of Surgery, St Vincent's Hospital, Melbourne, Australia
| | - Konstantin I Momot
- School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology (QUT), Brisbane, Australia.,Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, Australia
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24
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Nguyen TL, Choi YH, Aung YK, Evans CF, Trinh NH, Li S, Dite GS, Kim MS, Brennan PC, Jenkins MA, Sung J, Song YM, Hopper JL. Breast Cancer Risk Associations with Digital Mammographic Density by Pixel Brightness Threshold and Mammographic System. Radiology 2018; 286:433-442. [DOI: 10.1148/radiol.2017170306] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Tuong L Nguyen
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Yoon-Ho Choi
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Ye K Aung
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Christopher F Evans
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Nhut H Trinh
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Shuai Li
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Gillian S Dite
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Myeong-Seong Kim
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Patrick C Brennan
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Mark A Jenkins
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Joohon Sung
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - Yun-Mi Song
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
| | - John L Hopper
- From the Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, University of Melbourne, Level 3, 207 Bouverie St, Carlton, VIC 3053, Australia (T.L.N., Y.K.A., C.F.E., N.H.T., S.L., G.S.D., M.A.J., J.L.H.); Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.H.C..); Department of Radiology, National Cancer Center, Goyang-si, South Korea (M.S.K.); Faculty of Health Sciences, University of Sydney, Lidcombe, NSW, Australia (P.C.B.); Department of Epidemiology School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, Korea (J.S., J.L.H.); and Department of Family Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea (Y.M.S.)
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Krishnan K, Baglietto L, Stone J, McLean C, Southey MC, English DR, Giles GG, Hopper JL. Mammographic density and risk of breast cancer by tumor characteristics: a case-control study. BMC Cancer 2017; 17:859. [PMID: 29246131 PMCID: PMC5732428 DOI: 10.1186/s12885-017-3871-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Accepted: 12/04/2017] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND In a previous paper, we had assumed that the risk of screen-detected breast cancer mostly reflects inherent risk, and the risk of whether a breast cancer is interval versus screen-detected mostly reflects risk of masking. We found that inherent risk was predicted by body mass index (BMI) and dense area (DA) or percent dense area (PDA), but not by non-dense area (NDA). Masking, however, was best predicted by PDA but not BMI. In this study, we aimed to investigate if these associations vary by tumor characteristics and mode of detection. METHODS We conducted a case-control study nested within the Melbourne Collaborative Cohort Study of 244 screen-detected cases matched to 700 controls and 148 interval cases matched to 446 controls. DA, NDA and PDA were measured using the Cumulus software. Tumor characteristics included size, grade, lymph node involvement, and ER, PR, and HER2 status. Conditional and unconditional logistic regression were applied as appropriate to estimate the Odds per Adjusted Standard Deviation (OPERA) adjusted for age and BMI, allowing the association with BMI to be a function of age at diagnosis. RESULTS For screen-detected cancer, both DA and PDA were associated to an increased risk of tumors of large size (OPERA ~ 1.6) and positive lymph node involvement (OPERA ~ 1.8); no association was observed for BMI and NDA. For risk of interval versus screen-detected breast cancer, the association with risk for any of the three mammographic measures did not vary by tumor characteristics; an association was observed for BMI for positive lymph nodes (OPERA ~ 0.6). No associations were observed for tumor grade and ER, PR and HER2 status of tumor. CONCLUSIONS Both DA and PDA were predictors of inherent risk of larger breast tumors and positive nodal status, whereas for each of the three mammographic density measures the association with risk of masking did not vary by tumor characteristics. This might raise the hypothesis that the risk of breast tumours with poorer prognosis, such as larger and node positive tumours, is intrinsically associated with increased mammographic density and not through delay of diagnosis due to masking.
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Affiliation(s)
- Kavitha Krishnan
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC 3053 Australia
| | - Laura Baglietto
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC 3053 Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, Villejuif, France
- Gustave Roussy, F-94805 Villejuif, France
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC 3053 Australia
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Perth, Australia
| | | | - Melissa C. Southey
- Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Melbourne, Australia
| | - Dallas R. English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC 3053 Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC 3053 Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC 3053 Australia
- Seoul Department of Epidemiology, School of Public Health, Seoul National University, Seoul, South Korea
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
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26
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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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27
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Burton A, Byrnes G, Stone J, Tamimi RM, Heine J, Vachon C, Ozmen V, Pereira A, Garmendia ML, Scott C, Hipwell JH, Dickens C, Schüz J, Aribal ME, Bertrand K, Kwong A, Giles GG, Hopper J, Pérez Gómez B, Pollán M, Teo SH, Mariapun S, Taib NAM, Lajous M, Lopez-Riduara R, Rice M, Romieu I, Flugelman AA, Ursin G, Qureshi S, Ma H, Lee E, Sirous R, Sirous M, Lee JW, Kim J, Salem D, Kamal R, Hartman M, Miao H, Chia KS, Nagata C, Vinayak S, Ndumia R, van Gils CH, Wanders JOP, Peplonska B, Bukowska A, Allen S, Vinnicombe S, Moss S, Chiarelli AM, Linton L, Maskarinec G, Yaffe MJ, Boyd NF, dos-Santos-Silva I, McCormack VA. Mammographic density assessed on paired raw and processed digital images and on paired screen-film and digital images across three mammography systems. Breast Cancer Res 2016; 18:130. [PMID: 27993168 PMCID: PMC5168805 DOI: 10.1186/s13058-016-0787-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 11/23/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Inter-women and intra-women comparisons of mammographic density (MD) are needed in research, clinical and screening applications; however, MD measurements are influenced by mammography modality (screen film/digital) and digital image format (raw/processed). We aimed to examine differences in MD assessed on these image types. METHODS We obtained 1294 pairs of images saved in both raw and processed formats from Hologic and General Electric (GE) direct digital systems and a Fuji computed radiography (CR) system, and 128 screen-film and processed CR-digital pairs from consecutive screening rounds. Four readers performed Cumulus-based MD measurements (n = 3441), with each image pair read by the same reader. Multi-level models of square-root percent MD were fitted, with a random intercept for woman, to estimate processed-raw MD differences. RESULTS Breast area did not differ in processed images compared with that in raw images, but the percent MD was higher, due to a larger dense area (median 28.5 and 25.4 cm2 respectively, mean √dense area difference 0.44 cm (95% CI: 0.36, 0.52)). This difference in √dense area was significant for direct digital systems (Hologic 0.50 cm (95% CI: 0.39, 0.61), GE 0.56 cm (95% CI: 0.42, 0.69)) but not for Fuji CR (0.06 cm (95% CI: -0.10, 0.23)). Additionally, within each system, reader-specific differences varied in magnitude and direction (p < 0.001). Conversion equations revealed differences converged to zero with increasing dense area. MD differences between screen-film and processed digital on the subsequent screening round were consistent with expected time-related MD declines. CONCLUSIONS MD was slightly higher when measured on processed than on raw direct digital mammograms. Comparisons of MD on these image formats should ideally control for this non-constant and reader-specific difference.
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Affiliation(s)
- Anya Burton
- Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France
| | - Graham Byrnes
- Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, Curtin University and the University of Western Australia, Perth, Australia
| | - Rulla M. Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | | | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - Vahit Ozmen
- Department of Surgery, Istanbul Faculty of Medicine, Istanbul University, Istanbul, Turkey
| | - Ana Pereira
- Institute of Nutrition and Food Technology, University of Chile, Santiago, Chile
| | | | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN USA
| | - John H. Hipwell
- Centre for Medical Image Computing, University College London, London, UK
| | - Caroline Dickens
- Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Joachim Schüz
- Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France
| | | | | | - Ava Kwong
- Division of Breast Surgery, Department of Surgery, The University of Hong Kong, Hong Kong, People’s Republic of China
- Department of Surgery, Hong Kong Sanatorium and Hospital, Hong Kong, People’s Republic of China
| | - Graham G. Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Victoria Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria Australia
| | - John Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria Australia
| | - Beatriz Pérez Gómez
- Cancer Epidemiology Unit, Instituto de Salud Carlos III and CIBERESP, Madrid, Spain
| | - Marina Pollán
- Cancer Epidemiology Unit, Instituto de Salud Carlos III and CIBERESP, Madrid, Spain
| | - Soo-Hwang Teo
- Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia
- Cancer Research Malaysia, Subang Jaya, Malaysia
| | | | - Nur Aishah Mohd Taib
- Breast Cancer Research Group, University Malaya Medical Centre, University Malaya, Kuala Lumpur, Malaysia
| | - Martín Lajous
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA USA
- Center for Research on Population Health, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | - Ruy Lopez-Riduara
- Center for Research on Population Health, Instituto Nacional de Salud Pública, Mexico City, Mexico
| | - Megan Rice
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Isabelle Romieu
- Section of Nutrition and Metabolism, International Agency for Research on Cancer, Lyon, France
| | | | - Giske Ursin
- Cancer Registry of Norway, Oslo, Norway
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA USA
| | - Samera Qureshi
- Norwegian Center for Minority and Migrant Health Research (NAKMI), Oslo, Norway
| | - Huiyan Ma
- Department of Population Sciences, Beckman Research Institute, City of Hope, CA USA
| | - Eunjung Lee
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA USA
| | - Reza Sirous
- Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mehri Sirous
- Isfahan University of Medical Sciences, Isfahan, Iran
| | - Jong Won Lee
- Department of Surgery, Asan Medical Center, Seoul, Republic of Korea
| | - Jisun Kim
- Department of Surgery, Asan Medical Center, Seoul, Republic of Korea
| | | | - Rasha Kamal
- Woman Imaging Unit, Radiodiagnosis Department, Kasr El Aini, Cairo University Hospitals, Cairo, Egypt
| | - Mikael Hartman
- Department of Surgery, Yong Loo Lin School of Medicine, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Hui Miao
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Kee-Seng Chia
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
| | | | | | - Rose Ndumia
- Aga Khan University Hospital, Nairobi, Kenya
| | - Carla H. van Gils
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Johanna O. P. Wanders
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | | | - Steve Allen
- Department of Imaging, Royal Marsden NHS Foundation Trust, London, UK
| | - Sarah Vinnicombe
- Division of Cancer Research, Ninewells Hospital & Medical School, Dundee, UK
| | - Sue Moss
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Anna M. Chiarelli
- Ontario Breast Screening Program, Cancer Care Ontario, Toronto, Canada
| | - Linda Linton
- Princess Margaret Cancer Centre, Toronto, Canada
| | | | | | | | - Isabel dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Valerie A. McCormack
- Section of Environment and Radiation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon, Cedex 09, France
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28
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Rajaram N, Mariapun S, Eriksson M, Tapia J, Kwan PY, Ho WK, Harun F, Rahmat K, Czene K, Taib NAM, Hall P, Teo SH. Differences in mammographic density between Asian and Caucasian populations: a comparative analysis. Breast Cancer Res Treat 2016; 161:353-362. [PMID: 27864652 DOI: 10.1007/s10549-016-4054-y] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 11/09/2016] [Indexed: 10/20/2022]
Abstract
PURPOSE Mammographic density is a measurable and modifiable biomarker that is strongly and independently associated with breast cancer risk. Paradoxically, although Asian women have lower risk of breast cancer, studies of minority Asian women in predominantly Caucasian populations have found that Asian women have higher percent density. In this cross-sectional study, we compared the distribution of mammographic density for a matched cohort of Asian women from Malaysia and Caucasian women from Sweden, and determined if variations in mammographic density could be attributed to population differences in breast cancer risk factors. METHODS Volumetric mammographic density was compared for 1501 Malaysian and 4501 Swedish healthy women, matched on age and body mass index. We used multivariable log-linear regression to determine the risk factors associated with mammographic density and mediation analysis to identify factors that account for differences in mammographic density between the two cohorts. RESULTS Compared to Caucasian women, percent density was 2.0% higher among Asian women (p < 0.001), and dense volume was 5.7 cm3 higher among pre-menopausal Asian women (p < 0.001). Dense volume was 3.0 cm3 lower among post-menopausal Asian women (p = 0.009) compared to post-menopausal Caucasian women, and this difference was attributed to population differences in height, weight, and parity (p < 0.001). CONCLUSIONS Our analysis suggests that among post-menopausal women, population differences in mammographic density and risk to breast cancer may be accounted for by height, weight, and parity. Given that pre-menopausal Asian and Caucasian women have similar population risk to breast cancer but different dense volume, development of more appropriate biomarkers of risk in pre-menopausal women is required.
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Affiliation(s)
- Nadia Rajaram
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500, Subang Jaya, Selangor, Malaysia.,Department of Applied Mathematics, Faculty of Engineering, University of Nottingham Malaysia Campus, Jalan Broga, 43500, Semenyih, Selangor, Malaysia
| | - Shivaani Mariapun
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500, Subang Jaya, Selangor, Malaysia
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, 171 77, Stockholm, Sweden
| | - Jose Tapia
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, 171 77, Stockholm, Sweden
| | - Pui Yoke Kwan
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500, Subang Jaya, Selangor, Malaysia
| | - Weang Kee Ho
- Department of Applied Mathematics, Faculty of Engineering, University of Nottingham Malaysia Campus, Jalan Broga, 43500, Semenyih, Selangor, Malaysia
| | - Faizah Harun
- Breast Cancer Research Unit, Faculty of Medicine, University Malaya Cancer Research Institute, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Kartini Rahmat
- Breast Cancer Research Unit, Faculty of Medicine, University Malaya Cancer Research Institute, University of Malaya, 50603, Kuala Lumpur, Malaysia.,Biomedical Imaging Department, University Malaya Medical Centre, 50603, Kuala Lumpur, Malaysia
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, 171 77, Stockholm, Sweden
| | - Nur Aishah Mohd Taib
- Breast Cancer Research Unit, Faculty of Medicine, University Malaya Cancer Research Institute, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, 171 77, Stockholm, Sweden.,Department of Radiology, South General Hospital, Stockholm, Sweden
| | - Soo Hwang Teo
- Cancer Research Malaysia, 1 Jalan SS12/1A, 47500, Subang Jaya, Selangor, Malaysia. .,Breast Cancer Research Unit, Faculty of Medicine, University Malaya Cancer Research Institute, University of Malaya, 50603, Kuala Lumpur, Malaysia.
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Bae JM, Kim EH. Breast Density and Risk of Breast Cancer in Asian Women: A Meta-analysis of Observational Studies. J Prev Med Public Health 2016; 49:367-375. [PMID: 27951629 PMCID: PMC5160133 DOI: 10.3961/jpmph.16.054] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 10/21/2016] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES The established theory that breast density is an independent predictor of breast cancer risk is based on studies targeting white women in the West. More Asian women than Western women have dense breasts, but the incidence of breast cancer is lower among Asian women. This meta-analysis investigated the association between breast density in mammography and breast cancer risk in Asian women. METHODS PubMed and Scopus were searched, and the final date of publication was set as December 31, 2015. The effect size in each article was calculated using the interval-collapse method. Summary effect sizes (sESs) and 95% confidence intervals (CIs) were calculated by conducting a meta-analysis applying a random effect model. To investigate the dose-response relationship, random effect dose-response meta-regression (RE-DRMR) was conducted. RESULTS Six analytical epidemiology studies in total were selected, including one cohort study and five case-control studies. A total of 17 datasets were constructed by type of breast density index and menopausal status. In analyzing the subgroups of premenopausal vs. postmenopausal women, the percent density (PD) index was confirmed to be associated with a significantly elevated risk for breast cancer (sES, 2.21; 95% CI, 1.52 to 3.21; I2=50.0%). The RE-DRMR results showed that the risk of breast cancer increased 1.73 times for each 25% increase in PD in postmenopausal women (95% CI, 1.20 to 2.47). CONCLUSIONS In Asian women, breast cancer risk increased with breast density measured using the PD index, regardless of menopausal status. We propose the further development of a breast cancer risk prediction model based on the application of PD in Asian women.
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Affiliation(s)
- Jong-Myon Bae
- Department of Preventive Medicine, Jeju National University School of Medicine, Jeju, Korea
| | - Eun Hee Kim
- Department of Preventive Medicine, Jeju National University School of Medicine, Jeju, Korea
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Krishnan K, Baglietto L, Apicella C, Stone J, Southey MC, English DR, Giles GG, Hopper JL. Mammographic density and risk of breast cancer by mode of detection and tumor size: a case-control study. Breast Cancer Res 2016; 18:63. [PMID: 27316945 PMCID: PMC4912759 DOI: 10.1186/s13058-016-0722-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 05/28/2016] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Risk of screen-detected breast cancer mostly reflects inherent risk, while risk of interval cancer reflects inherent risk and risk of masking (risk of the tumor not being detected due to increased dense tissue). Therefore the predictors of whether a breast cancer is interval or screen-detected include those that predict masking. Our aim was to investigate the associations between mammographic measures and (1) inherent risk, and (2) masking. METHODS We conducted a case-control study nested within the Melbourne collaborative cohort study of 244 screen-detected cases (192 small tumors (<2 cm)) matched to 700 controls and 148 interval cases (76 small tumors) matched to 446 controls. Dense area (DA), percent dense area (PDA), and non-dense area (NDA) were measured using the Cumulus software. Conditional and unconditional logistic regression were applied as appropriate to estimate the odds per adjusted standard deviation (OPERA) adjusted for age and body mass index (BMI), allowing for the association with BMI to be a function of age at diagnosis. Tests of fit were performed using the Bayesian information criterion (BIC) and the area under the receiver operating characteristic curve. RESULTS For screen-detected cancer, the association with BMI had a marginally significant dependence on age at diagnosis, and after adjustment both DA and PDA were associated with risk (OPERA approximately 1.2) and gave a similar fit. NDA was not associated with risk. For interval cancer, the BMI risk association was not dependent on age at diagnosis and the best fitting model was PDA alone (OPERA = 2.24, 95 % confidence interval 1.75, 2.86). Prediction of interval versus screen-detected cancer was best achieved by PDA alone (OPERA = 1.76, 95 % confidence interval 1.39, 2.22) with no association with BMI. When the analysis was restricted to small tumors to reduce the influence of tumor growth, we obtained similar results. CONCLUSIONS Inherent breast cancer risk is predicted by BMI and DA or PDA, but not NDA. Masking is predicted by PDA, and not by BMI. Understanding risk and masking could help tailor mammographic screening.
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Affiliation(s)
- Kavitha Krishnan
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Laura Baglietto
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
- Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, Villejuif, France
- Gustave Roussy, F-94805, Villejuif, France
| | - Carmel Apicella
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia
- Centre for Genetic Origins of Health and Disease, Curtin University and University of Western Australia, Crawley, Australia
| | - Melissa C Southey
- Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Melbourne, Australia
| | - Dallas R English
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Level 3, 207 Bouverie Street, Carlton, VIC, 3053, Australia.
- Seoul Department of Epidemiology, School of Public Health, Seoul National University, Seoul, Korea.
- Institute of Health and Environment, Seoul National University, Seoul, Korea.
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31
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Hopper JL, Nguyen TL, Stone J, Aujard K, Matheson MC, Abramson MJ, Burgess JA, Walters EH, Dite GS, Bui M, Evans C, Makalic E, Schmidt DF, Ward G, Jenkins MA, Giles GG, Dharmage SC, Apicella C. Childhood body mass index and adult mammographic density measures that predict breast cancer risk. Breast Cancer Res Treat 2016; 156:163-70. [DOI: 10.1007/s10549-016-3719-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2015] [Accepted: 02/11/2016] [Indexed: 01/01/2023]
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