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Hill H, Kearns B, Pashayan N, Roadevin C, Sasieni P, Offman J, Duffy S. The cost-effectiveness of risk-stratified breast cancer screening in the UK. Br J Cancer 2023; 129:1801-1809. [PMID: 37848734 PMCID: PMC10667489 DOI: 10.1038/s41416-023-02461-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 09/09/2023] [Accepted: 10/04/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND There has been growing interest in the UK and internationally of risk-stratified breast screening whereby individualised risk assessment may inform screening frequency, starting age, screening instrument used, or even decisions not to screen. This study evaluates the cost-effectiveness of eight proposals for risk-stratified screening regimens compared to both the current UK screening programme and no national screening. METHODS A person-level microsimulation model was developed to estimate health-related quality of life, cancer survival and NHS costs over the lifetime of the female population eligible for screening in the UK. RESULTS Compared with both the current screening programme and no screening, risk-stratified regimens generated additional costs and QALYs, and had a larger net health benefit. The likelihood of the current screening programme being the optimal scenario was less than 1%. No screening amongst the lowest risk group, and triannual, biennial and annual screening amongst the three higher risk groups was the optimal screening strategy from those evaluated. CONCLUSIONS We found that risk-stratified breast cancer screening has the potential to be beneficial for women at the population level, but the net health benefit will depend on the particular risk-based strategy.
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Affiliation(s)
- Harry Hill
- School of Medicine and Population Health, University of Sheffield, Sheffield, England.
| | - Ben Kearns
- School of Medicine and Population Health, University of Sheffield, Sheffield, England
- Lumanity Inc, Sheffield, England
| | - Nora Pashayan
- Department of Applied Health Research, University College London, London, England
| | - Cristina Roadevin
- Nottingham Clinical Trials Unit, University of Nottingham, Nottingham, England
| | - Peter Sasieni
- Life Sciences & Medicine, King's College London, London, England
- Wolfson Institute of Population Health, Queen Mary University of London, London, England
| | - Judith Offman
- Life Sciences & Medicine, King's College London, London, England
- Wolfson Institute of Population Health, Queen Mary University of London, London, England
| | - Stephen Duffy
- Wolfson Institute of Population Health, Queen Mary University of London, London, England
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2
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Acciavatti RJ, Lee SH, Reig B, Moy L, Conant EF, Kontos D, Moon WK. Beyond Breast Density: Risk Measures for Breast Cancer in Multiple Imaging Modalities. Radiology 2023; 306:e222575. [PMID: 36749212 PMCID: PMC9968778 DOI: 10.1148/radiol.222575] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/23/2022] [Accepted: 12/05/2022] [Indexed: 02/08/2023]
Abstract
Breast density is an independent risk factor for breast cancer. In digital mammography and digital breast tomosynthesis, breast density is assessed visually using the four-category scale developed by the American College of Radiology Breast Imaging Reporting and Data System (5th edition as of November 2022). Epidemiologically based risk models, such as the Tyrer-Cuzick model (version 8), demonstrate superior modeling performance when mammographic density is incorporated. Beyond just density, a separate mammographic measure of breast cancer risk is parenchymal textural complexity. With advancements in radiomics and deep learning, mammographic textural patterns can be assessed quantitatively and incorporated into risk models. Other supplemental screening modalities, such as breast US and MRI, offer independent risk measures complementary to those derived from mammography. Breast US allows the two components of fibroglandular tissue (stromal and glandular) to be visualized separately in a manner that is not possible with mammography. A higher glandular component at screening breast US is associated with higher risk. With MRI, a higher background parenchymal enhancement of the fibroglandular tissue has also emerged as an imaging marker for risk assessment. Imaging markers observed at mammography, US, and MRI are powerful tools in refining breast cancer risk prediction, beyond mammographic density alone.
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Affiliation(s)
| | | | - Beatriu Reig
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Linda Moy
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
| | - Emily F. Conant
- From the Department of Radiology, University of Pennsylvania, 3400
Spruce St, Philadelphia, PA 19104 (R.J.A., E.F.C., D.K.); Department of
Radiology, Seoul National University Hospital, Seoul, South Korea (S.H.L.,
W.K.M.); and Department of Radiology, NYU Langone Health, New York, NY (B.R.,
L.M.)
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3
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Li T, Li J, Heard R, Gandomkar Z, Ren J, Dai M, Brennan P. Understanding mammographic breast density profile in China: A Sino-Australian comparative study of breast density using real-world data from cancer screening programs. Asia Pac J Clin Oncol 2022; 18:696-705. [PMID: 35238173 PMCID: PMC9790382 DOI: 10.1111/ajco.13763] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 01/27/2022] [Indexed: 12/30/2022]
Abstract
AIM This study aims at understanding mammographic density profile in China by comparing the density between women in China and Australia. METHODS Data of 3250 women aged 45-69 were obtained from the Cancer Screening Program in Urban China and data of 1384 Australian counterparts at same age range were gathered from the Lifepool project. Demographic and reproductive details and mammograms for each cohort were collected. Mammographic density was assessed using AutoDensity, and two metrics, percentage density (PD) and dense area (DA), were applied. T-tests were used to compare the means of mammographic density between two populations of all, premenopausal, and postmenopausal women. Two-way ANOVA was conducted to examine interactions of population (Chinese/Australian) and each variable of interest upon mammographic density. RESULTS Chinese women had 9.61%, 8.20%, and 9.28% higher PD than their Australian counterparts in all, premenopausal, and postmenopausal women, respectively (all p < 0.001). The mean differences in DA between two population were 1.81 cm2 (p < 0.001), 0.55 cm2 (p = 0.472), and 1.76 cm2 (p = 0.003) for all, premenopausal, and postmenopausal women, respectively. There were significant interactions between population and age (F[4, 4624] = 4.12, p = 0.003), BMI (F[2, 4628] = 3.92, p = 0.020), age at first birth (F[1, 4250] = 11.69, p < 0.001), breastfeeding history (F[1, 4479] = 17.79, p < 0.001), and breastfeeding duration (F[1, 3526] = 66.90, p < 0.001) upon PD. Interaction was only found for breastfeeding history (F[1, 4479] = 4.79, p = 0.029) and breastfeeding duration (F[1, 3526] = 17.72, p < 0.001) for DA. CONCLUSIONS Both PD and DA were found to be higher in Chinese women compared to Australian women. The density difference by menopause status was shown and breastfeeding history affected breast density differently in both populations.
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Affiliation(s)
- Tong Li
- Medical Imaging Science, School of Health Sciences, Faculty of Medicine and HealthThe University of SydneyCamperdownNew South WalesAustralia
| | - Jing Li
- Department of Diagnostic RadiologyNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Rob Heard
- School of Health Sciences, Faculty of Medicine and HealthThe University of SydneyCamperdownNew South WalesAustralia
| | - Ziba Gandomkar
- Medical Imaging Science, School of Health Sciences, Faculty of Medicine and HealthThe University of SydneyCamperdownNew South WalesAustralia
| | - Jiansong Ren
- Office of Cancer ScreeningNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Min Dai
- Office of Cancer ScreeningNational Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Patrick Brennan
- Medical Imaging Science, School of Health Sciences, Faculty of Medicine and HealthThe University of SydneyCamperdownNew South WalesAustralia
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4
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Chalfant JS, Hoyt AC. Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications. JOURNAL OF BREAST IMAGING 2022; 4:357-370. [PMID: 38416979 DOI: 10.1093/jbi/wbac028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Indexed: 03/01/2024]
Abstract
Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.
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Affiliation(s)
- James S Chalfant
- David Geffen School of Medicine at University of California, Los Angeles, Department of Radiological Sciences, Santa Monica, CA, USA
| | - Anne C Hoyt
- David Geffen School of Medicine at University of California, Los Angeles, Department of Radiological Sciences, Santa Monica, CA, USA
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5
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Ribeiro A, Rodrigues J, Antunes L, Sarmento S. Radiation doses in mammography exams: Effects of oncological treatments. Radiat Phys Chem Oxf Engl 1993 2022. [DOI: 10.1016/j.radphyschem.2022.110286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Hernández A, Miranda DA, Pertuz S. Algorithms and methods for computerized analysis of mammography images in breast cancer risk assessment. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 212:106443. [PMID: 34656014 DOI: 10.1016/j.cmpb.2021.106443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 09/22/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVES The computerized analysis of mammograms for the development of quantitative biomarkers is a growing field with applications in breast cancer risk assessment. Computerized image analysis offers the possibility of using different methods and algorithms to extract additional information from screening and diagnosis images to aid in the assessment of breast cancer risk. In this work, we review the algorithms and methods for the automated, computerized analysis of mammography images for the task mentioned, and discuss the main challenges that the development and improvement of these methods face today. METHODS We review the recent progress in two main branches of mammography-based risk assessment: parenchymal analysis and breast density estimation, including performance indicators of most of the studies considered. Parenchymal analysis methods are divided into feature-based methods and deep learning-based methods; breast density methods are grouped into area-based, volume-based, and breast categorization methods. Additionally, we identify the challenges that these study fields currently face. RESULTS Parenchymal analysis using deep learning algorithms are on the rise, with some studies showing high-performance indicators, such as an area under the receiver operating characteristic curve of up to 90. Methods for risk assessment using breast density report a wider variety of performance indicators; however, we can also identify that the approaches using deep learning methods yield high performance in each of the subdivisions considered. CONCLUSIONS Both breast density estimation and parenchymal analysis are promising tools for the task of breast cancer risk assessment; deep learning methods have shown performance comparable or superior to the other considered methods. All methods considered face challenges such as the lack of objective comparison between them and the lack of access to datasets from different populations.
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Affiliation(s)
| | | | - Said Pertuz
- Universidad Industrial de Santander, Bucaramanga, Colombia.
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7
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Mendes J, Matela N. Breast Cancer Risk Assessment: A Review on Mammography-Based Approaches. J Imaging 2021; 7:98. [PMID: 39080886 PMCID: PMC8321333 DOI: 10.3390/jimaging7060098] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/03/2023] Open
Abstract
Breast cancer affects thousands of women across the world, every year. Methods to predict risk of breast cancer, or to stratify women in different risk levels, could help to achieve an early diagnosis, and consequently a reduction of mortality. This paper aims to review articles that extracted texture features from mammograms and used those features along with machine learning algorithms to assess breast cancer risk. Besides that, deep learning methodologies that aimed for the same goal were also reviewed. In this work, first, a brief introduction to breast cancer statistics and screening programs is presented; after that, research done in the field of breast cancer risk assessment are analyzed, in terms of both methodologies used and results obtained. Finally, considerations about the analyzed papers are conducted. The results of this review allow to conclude that both machine and deep learning methodologies provide promising results in the field of risk analysis, either in a stratification in risk groups, or in a prediction of a risk score. Although promising, future endeavors in this field should consider the possibility of the implementation of the methodology in clinical practice.
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Affiliation(s)
| | - Nuno Matela
- Faculdade de Ciências, Instituto de Biofísica e Engenharia Biomédica, Universidade de Lisboa, 1749-016 Lisboa, Portugal;
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8
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Warner ET, Rice MS, Zeleznik OA, Fowler EE, Murthy D, Vachon CM, Bertrand KA, Rosner BA, Heine J, Tamimi RM. Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study. NPJ Breast Cancer 2021; 7:68. [PMID: 34059687 PMCID: PMC8166859 DOI: 10.1038/s41523-021-00272-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Accepted: 05/03/2021] [Indexed: 12/03/2022] Open
Abstract
Percent mammographic density (PMD) is a strong breast cancer risk factor, however, other mammographic features, such as V, the standard deviation (SD) of pixel intensity, may be associated with risk. We assessed whether PMD, automated PMD (APD), and V, yielded independent associations with breast cancer risk. We included 1900 breast cancer cases and 3921 matched controls from the Nurses' Health Study (NHS) and the NHSII. Using digitized film mammograms, we estimated PMD using a computer-assisted thresholding technique. APD and V were determined using an automated computer algorithm. We used logistic regression to generate odds ratios (ORs) and 95% confidence intervals (CIs). Median time from mammogram to diagnosis was 4.1 years (interquartile range: 1.6-6.8 years). PMD (OR per SD:1.52, 95% CI: 1.42, 1.63), APD (OR per SD:1.32, 95% CI: 1.24, 1.41), and V (OR per SD:1.32, 95% CI: 1.24, 1.40) were positively associated with breast cancer risk. Associations for APD were attenuated but remained statistically significant after mutual adjustment for PMD or V. Women in the highest quartile of both APD and V (OR vs Q1/Q1: 2.49, 95% CI: 2.02, 3.06), or PMD and V (OR vs Q1/Q1: 3.57, 95% CI: 2.79, 4.58) had increased breast cancer risk. An automated method of PMD assessment is feasible and yields similar, but somewhat weaker, estimates to a manual measure. PMD, APD and V are each independently, positively associated with breast cancer risk. Women with dense breasts and greater texture variation are at the highest relative risk of breast cancer.
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Affiliation(s)
- Erica T Warner
- Clinical and Translational Epidemiology Unit, Department of Medicine, Mongan Institute, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Megan S Rice
- Clinical and Translational Epidemiology Unit, Department of Medicine, Mongan Institute, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Erin E Fowler
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Divya Murthy
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Celine M Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Bernard A Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Heine
- Division of Population Sciences, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Rulla M Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
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9
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Fully Automated Breast Density Segmentation and Classification Using Deep Learning. Diagnostics (Basel) 2020; 10:diagnostics10110988. [PMID: 33238512 PMCID: PMC7700286 DOI: 10.3390/diagnostics10110988] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 11/12/2020] [Accepted: 11/17/2020] [Indexed: 01/16/2023] Open
Abstract
Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms’ fatty tissue background. The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques. The conditional Generative Adversarial Networks (cGAN) network is applied to segment the dense tissues in mammograms. To have a complete system for breast density classification, we propose a Convolutional Neural Network (CNN) to classify mammograms based on the standardization of Breast Imaging-Reporting and Data System (BI-RADS). The classification network is fed by the segmented masks of dense tissues generated by the cGAN network. For screening mammography, 410 images of 115 patients from the INbreast dataset were used. The proposed framework can segment the dense regions with an accuracy, Dice coefficient, Jaccard index of 98%, 88%, and 78%, respectively. Furthermore, we obtained precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%, respectively, for breast density classification. This study’s findings are promising and show that the proposed deep learning-based techniques can produce a clinically useful computer-aided tool for breast density analysis by digital mammography.
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10
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Cheasley D, Devereux L, Hughes S, Nickson C, Procopio P, Lee G, Li N, Pridmore V, Elder K, Bruce Mann G, Kader T, Rowley SM, Fox SB, Byrne D, Saunders H, Fujihara KM, Lim B, Gorringe KL, Campbell IG. The TP53 mutation rate differs in breast cancers that arise in women with high or low mammographic density. NPJ Breast Cancer 2020; 6:34. [PMID: 32802943 PMCID: PMC7414106 DOI: 10.1038/s41523-020-00176-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 07/13/2020] [Indexed: 01/01/2023] Open
Abstract
Mammographic density (MD) influences breast cancer risk, but how this is mediated is unknown. Molecular differences between breast cancers arising in the context of the lowest and highest quintiles of mammographic density may identify the mechanism through which MD drives breast cancer development. Women diagnosed with invasive or in situ breast cancer where MD measurement was also available (n = 842) were identified from the Lifepool cohort of >54,000 women participating in population-based mammographic screening. This group included 142 carcinomas in the lowest quintile of MD and 119 carcinomas in the highest quintile. Clinico-pathological and family history information were recorded. Tumor DNA was collected where available (n = 56) and sequenced for breast cancer predisposition and driver gene mutations, including copy number alterations. Compared to carcinomas from low-MD breasts, those from high-MD breasts were significantly associated with a younger age at diagnosis and features associated with poor prognosis. Low- and high-MD carcinomas matched for grade, histological subtype, and hormone receptor status were compared for somatic genetic features. Low-MD carcinomas had a significantly increased frequency of TP53 mutations, higher homologous recombination deficiency, higher fraction of the genome altered, and more copy number gains on chromosome 1q and losses on 17p. While high-MD carcinomas showed enrichment of tumor-infiltrating lymphocytes in the stroma. The data demonstrate that when tumors were matched for confounding clinico-pathological features, a proportion in the lowest quintile of MD appear biologically distinct, reflective of microenvironment differences between the lowest and highest quintiles of MD.
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Affiliation(s)
- Dane Cheasley
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC Australia
| | - Lisa Devereux
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC Australia
- Lifepool, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
| | - Siobhan Hughes
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
| | - Carolyn Nickson
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC Australia
- Cancer Research Division, Cancer Council NSW, Sydney, NSW Australia
- Sydney School of Public Health, University of Sydney, Sydney, NSW Australia
| | - Pietro Procopio
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC Australia
- Cancer Research Division, Cancer Council NSW, Sydney, NSW Australia
- Sydney School of Public Health, University of Sydney, Sydney, NSW Australia
| | - Grant Lee
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC Australia
| | - Na Li
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
| | | | - Kenneth Elder
- Department of Surgery, University of Melbourne, Melbourne, VIC Australia
- The Royal Melbourne and Royal Women’s Hospitals, Parkville, VIC Australia
- The Edinburgh Breast Unit, Western General Hospital, Edinburgh, UK
| | - G. Bruce Mann
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC Australia
- Department of Surgery, University of Melbourne, Melbourne, VIC Australia
- The Royal Melbourne and Royal Women’s Hospitals, Parkville, VIC Australia
| | - Tanjina Kader
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC Australia
| | - Simone M. Rowley
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
| | - Stephen B. Fox
- Department of Pathology, Peter MacCallum Cancer Centre, and University of Melbourne, Melbourne, VIC Australia
| | - David Byrne
- Department of Pathology, Peter MacCallum Cancer Centre, and University of Melbourne, Melbourne, VIC Australia
| | - Hugo Saunders
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
| | - Kenji M. Fujihara
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
| | - Belle Lim
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia
| | - Kylie L. Gorringe
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC Australia
- Cancer Genetics and Genomics Program, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
| | - Ian G. Campbell
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC Australia
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11
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Dench E, Bond-Smith D, Darcey E, Lee G, Aung YK, Chan A, Cuzick J, Ding ZY, Evans CF, Harvey J, Highnam R, Hsieh MK, Kontos D, Li S, Mariapun S, Nickson C, Nguyen TL, Pertuz S, Procopio P, Rajaram N, Repich K, Tan M, Teo SH, Trinh NH, Ursin G, Wang C, Dos-Santos-Silva I, McCormack V, Nielsen M, Shepherd J, Hopper JL, Stone J. Measurement challenge: protocol for international case-control comparison of mammographic measures that predict breast cancer risk. BMJ Open 2019; 9:e031041. [PMID: 31892647 PMCID: PMC6955467 DOI: 10.1136/bmjopen-2019-031041] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 11/29/2019] [Accepted: 12/04/2019] [Indexed: 11/12/2022] Open
Abstract
INTRODUCTION For women of the same age and body mass index, increased mammographic density is one of the strongest predictors of breast cancer risk. There are multiple methods of measuring mammographic density and other features in a mammogram that could potentially be used in a screening setting to identify and target women at high risk of developing breast cancer. However, it is unclear which measurement method provides the strongest predictor of breast cancer risk. METHODS AND ANALYSIS The measurement challenge has been established as an international resource to offer a common set of anonymised mammogram images for measurement and analysis. To date, full field digital mammogram images and core data from 1650 cases and 1929 controls from five countries have been collated. The measurement challenge is an ongoing collaboration and we are continuing to expand the resource to include additional image sets across different populations (from contributors) and to compare additional measurement methods (by challengers). The intended use of the measurement challenge resource is for refinement and validation of new and existing mammographic measurement methods. The measurement challenge resource provides a standardised dataset of mammographic images and core data that enables investigators to directly compare methods of measuring mammographic density or other mammographic features in case/control sets of both raw and processed images, for the purposes of the comparing their predictions of breast cancer risk. ETHICS AND DISSEMINATION Challengers and contributors are required to enter a Research Collaboration Agreement with the University of Melbourne prior to participation in the measurement challenge. The Challenge database of collated data and images are stored in a secure data repository at the University of Melbourne. Ethics approval for the measurement challenge is held at University of Melbourne (HREC ID 0931343.3).
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Affiliation(s)
- Evenda Dench
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia
| | - Daniela Bond-Smith
- School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia
| | - Ellie Darcey
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia
| | - Grant Lee
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Ye K Aung
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Ariane Chan
- Science and Technology, Volpara Health Technologies, Wellington, New Zealand
| | - Jack Cuzick
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - Ze Y Ding
- Electrical and Computer Systems Engineering, School of Engineering, Monash University - Malaysia Campus, Bandar Sunway, Selangor, Malaysia
| | - Chris F Evans
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Jennifer Harvey
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - Ralph Highnam
- Science and Technology, Volpara Health Technologies, Wellington, New Zealand
| | - Meng-Kang Hsieh
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Shuai Li
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Shivaani Mariapun
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- Department of Applied Mathematics, Faculty of Engineering, University of Nottingham - Malaysia Campus, Semenyih, Selangor, Malaysia
| | - Carolyn Nickson
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
- Cancer Research Division, Cancer Council New South Wales, Sydney, New South Wales, Austalia
| | - Tuong L Nguyen
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Said Pertuz
- Laboratory of Signal Processing, Tampere University of Technology, Tampere, Pirkanmaa, Finland
- Connectivity and Signal Processing group, Universidad Industrial de Santander, Bucaramanga, Colombia
| | - Pietro Procopio
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
- Cancer Research Division, Cancer Council New South Wales, Sydney, New South Wales, Austalia
| | - Nadia Rajaram
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
- Department of Applied Mathematics, Faculty of Engineering, University of Nottingham - Malaysia Campus, Semenyih, Selangor, Malaysia
| | - Kathy Repich
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, Virginia, USA
| | - Maxine Tan
- Electrical and Computer Systems Engineering, School of Engineering, Monash University - Malaysia Campus, Bandar Sunway, Selangor, Malaysia
- School of Electrical and Computer Engineering, University of Oklahoma Norman Campus, Norman, Oklahoma, USA
| | - Soo-Hwang Teo
- Cancer Research Malaysia, Subang Jaya, Selangor, Malaysia
| | - Nhut Ho Trinh
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | | | - Chao Wang
- Faculty of Health, Social Care and Education, Kingston University and St George's, University of London, Kingston-Upon-Thames, London, UK
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, London, UK
| | - Valerie McCormack
- Section of Environment and Radiation, International Agency for Research on Cancer, IARC, Lyon, France
| | | | - John Shepherd
- University of Hawai'i Cancer Center, Honolulu, Hawaii, USA
| | - John L Hopper
- Centre for Epidemiology & Biostatistics, The University of Melbourne School of Population and Global Health, Melbourne, Victoria, Australia
| | - Jennifer Stone
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia
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12
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Walsh SM, Brennan SB, Zabor EC, Rosenberger LH, Stempel M, Lebron-Zapata L, Gemignani ML. Does Breast Density Increase the Risk of Re-excision for Women with Breast Cancer Having Breast-Conservation Therapy? Ann Surg Oncol 2019; 26:4246-4253. [PMID: 31396783 DOI: 10.1245/s10434-019-07647-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Indexed: 12/11/2022]
Abstract
BACKGROUND Women with dense breasts may have less-accurate preoperative evaluation of extent of disease, potentially affecting the achievement of negative margins. The goal of this study is to examine the association between breast density and re-excision rates in women having breast-conserving surgery for invasive breast cancer. PATIENTS AND METHODS Women with stage I/II invasive breast cancer treated with breast-conserving surgery between 1/1/2014 and 10/31/2014 were included. Breast density was assessed by two radiologists. The association between breast density and re-excision was examined using logistic regression. RESULTS Seven hundred and one women were included. Overall, 106 (15.1%) women had at least one re-excision. Younger age at diagnosis was associated with increased breast density (p < 0.001). On univariable analysis, increased breast density was associated with significantly increased odds of re-excision (odds ratio [OR] 1.38, 95% confidence interval [CI] 1.04-1.83), as was multifocal disease, human epidermal growth factor receptor 2 (HER2) positive status, and extensive intraductal component (EIC) (all p < 0.05). On multivariable analysis, breast density remained significantly associated with increased odds of re-excision (OR 1.37, 95% CI 1.00-1.86), as did multifocality and EIC. HER2 positive status was not significantly associated with re-excision on multivariable analysis. CONCLUSIONS Women with dense breasts are more likely to need additional surgery (re-excision after breast-conserving surgery), but increased breast density did not adversely affect disease-free survival in our study. Our findings support the need for further study in developing techniques that can help decrease re-excisions for women with dense breasts who undergo breast-conserving surgery.
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Affiliation(s)
- Siun M Walsh
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sandra B Brennan
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Emily C Zabor
- Biostatistics Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Laura H Rosenberger
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Michelle Stempel
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lizza Lebron-Zapata
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mary L Gemignani
- Breast Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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13
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Rebolj M, Blyuss O, Chia KS, Duffy SW. Long-term excess risk of breast cancer after a single breast density measurement. Eur J Cancer 2019; 117:41-47. [PMID: 31229948 PMCID: PMC6658627 DOI: 10.1016/j.ejca.2019.05.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/02/2019] [Accepted: 05/03/2019] [Indexed: 12/20/2022]
Abstract
AIM Breast density is a risk factor for breast cancer. As density changes across a woman's life span, we studied for how long a single density measurement taken in (post-)menopausal women remains informative. METHODS We used data from Singaporean women who underwent a single mammography screen at age 50-64 years. For each case with breast cancer diagnosed at screening or in the subsequent 10 years, whether screen detected or diagnosed following symptoms, two age-matched controls were selected. We studied the excess risk of breast cancer, calculated as an odds ratio (OR) with conditional logistic regression and adjusted for body mass index, associated with 26-50% and with 51-100% density compared with ≤25% density by time since screening. RESULTS In total, 490 women had breast cancer, of which 361 were diagnosed because of symptoms after screening. Women with 51-100% breast density had an excess risk of breast cancer that did not seem to attenuate with time. In 1-3 years after screening, the OR was 2.22 (95% confidence interval [CI]: 1.07-4.61); in 4-6 years after screening, the OR was 4.09 (95% CI: 2.21-7.58), and in 7-10 years after screening, the OR was 5.35 (95% CI: 2.57-11.15). Excess risk with a stable OR of about 2 was also observed for women with 26-50% breast density. These patterns were robust when the analyses were limited to post-menopausal women, non-users of hormonal replacement therapy and after stratification by age at density measurement. CONCLUSION A single breast density measurement identifies women with an excess risk of breast cancer during at least the subsequent 10 years.
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Affiliation(s)
- Matejka Rebolj
- Cancer Prevention Group, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, London SE1 9RT, UK; Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK.
| | - Oleg Blyuss
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK; Department of Paediatrics, Sechenov University, Moscow, Russia
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Stephen W Duffy
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK.
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14
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A review of the influence of mammographic density on breast cancer clinical and pathological phenotype. Breast Cancer Res Treat 2019; 177:251-276. [PMID: 31177342 DOI: 10.1007/s10549-019-05300-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 05/27/2019] [Indexed: 10/26/2022]
Abstract
PURPOSE It is well established that high mammographic density (MD), when adjusted for age and body mass index, is one of the strongest known risk factors for breast cancer (BC), and also associates with higher incidence of interval cancers in screening due to the masking of early mammographic abnormalities. Increasing research is being undertaken to determine the underlying histological and biochemical determinants of MD and their consequences for BC pathogenesis, anticipating that improved mechanistic insights may lead to novel preventative or treatment interventions. At the same time, technological advances in digital and contrast mammography are such that the validity of well-established relationships needs to be re-examined in this context. METHODS With attention to old versus new technologies, we conducted a literature review to summarise the relationships between clinicopathologic features of BC and the density of the surrounding breast tissue on mammography, including the associations with BC biological features inclusive of subtype, and implications for the clinical disease course encompassing relapse, progression, treatment response and survival. RESULTS AND CONCLUSIONS There is reasonable evidence to support positive relationships between high MD (HMD) and tumour size, lymph node positivity and local relapse in the absence of radiotherapy, but not between HMD and LVI, regional relapse or distant metastasis. Conflicting data exist for associations of HMD with tumour location, grade, intrinsic subtype, receptor status, second primary incidence and survival, which need further confirmatory studies. We did not identify any relationships that did not hold up when data involving newer imaging techniques were employed in analysis.
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15
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Characteristics of Mammographic Breast Density and Associated Factors for Chinese Women: Results from an Automated Measurement. JOURNAL OF ONCOLOGY 2019; 2019:4910854. [PMID: 31015834 PMCID: PMC6444251 DOI: 10.1155/2019/4910854] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Revised: 02/01/2019] [Accepted: 02/19/2019] [Indexed: 11/18/2022]
Abstract
Background Characteristics of mammographic density for Chinese women are understudied. This study aims to identify factors associated with mammographic density in China using a quantitative method. Methods Mammographic density was measured for a total of 1071 (84 with and 987 without breast cancer) women using an automatic algorithm AutoDensity. Pearson tests examined relationships between density and continuous variables and t-tests compared differences of mean density values between groupings of categorical variables. Linear models were built using multiple regression. Results Percentage density and dense area were positively associated with each other for cancer-free (r=0.487, p<0.001) and cancer groups (r=0.446, p<0.001), respectively. For women without breast cancer, weight and BMI (p<0.001) were found to be negatively associated (r=-0.237, r=-0.272) with percentage density whereas they were found to be positively associated (r=0.110, r=0.099) with dense area; age at mammography was found to be associated with percentage density (r=-0.202, p<0.001) and dense area (r=-0.086, p<0.001) but did not add any prediction within multivariate models; lower percentage density was found within women with secondary education background or below compared to women with tertiary education. For women with breast cancer, percentage density demonstrated similar relationships with that of cancer-free women whilst breast area was the only factor associated with dense area (r=0.739, p<0.001). Conclusion This is the first time that mammographic density was measured by a quantitative method for women in China and identified associations should be useful to health policy makers who are responsible for introducing effective models of breast cancer prevention and diagnosis.
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16
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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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17
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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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18
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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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19
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Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer. J Digit Imaging 2018; 30:215-227. [PMID: 27832519 DOI: 10.1007/s10278-016-9922-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Breast cancer is the most common invasive cancer among women and its incidence is increasing. Risk assessment is valuable and recent methods are incorporating novel biomarkers such as mammographic density. Artificial neural networks (ANN) are adaptive algorithms capable of performing pattern-to-pattern learning and are well suited for medical applications. They are potentially useful for calibrating full-field digital mammography (FFDM) for quantitative analysis. This study uses ANN modeling to estimate volumetric breast density (VBD) from FFDM on Japanese women with and without breast cancer. ANN calibration of VBD was performed using phantom data for one FFDM system. Mammograms of 46 Japanese women diagnosed with invasive carcinoma and 53 with negative findings were analyzed using ANN models learned. ANN-estimated VBD was validated against phantom data, compared intra-patient, with qualitative composition scoring, with MRI VBD, and inter-patient with classical risk factors of breast cancer as well as cancer status. Phantom validations reached an R 2 of 0.993. Intra-patient validations ranged from R 2 of 0.789 with VBD to 0.908 with breast volume. ANN VBD agreed well with BI-RADS scoring and MRI VBD with R 2 ranging from 0.665 with VBD to 0.852 with breast volume. VBD was significantly higher in women with cancer. Associations with age, BMI, menopause, and cancer status previously reported were also confirmed. ANN modeling appears to produce reasonable measures of mammographic density validated with phantoms, with existing measures of breast density, and with classical biomarkers of breast cancer. FFDM VBD is significantly higher in Japanese women with cancer.
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20
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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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21
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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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22
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Li T, Tang L, Gandomkar Z, Heard R, Mello-Thoms C, Shao Z, Brennan P. Mammographic density and other risk factors for breast cancer among women in China. Breast J 2017; 24:426-428. [PMID: 29193600 DOI: 10.1111/tbj.12967] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 01/17/2017] [Accepted: 01/19/2017] [Indexed: 11/28/2022]
Affiliation(s)
- Tong Li
- Medical Radiation Sciences, Faculty of Health Sciences, The University of Sydney, Lidcombe, NSW, Australia
| | - Lichen Tang
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Ziba Gandomkar
- Medical Radiation Sciences, Faculty of Health Sciences, The University of Sydney, Lidcombe, NSW, Australia
| | - Rob Heard
- Behaviour and Social Sciences in Health, Faculty of Health Sciences, The University of Sydney, Lidcombe, NSW, Australia
| | - Claudia Mello-Thoms
- Medical Radiation Sciences, Faculty of Health Sciences, The University of Sydney, Lidcombe, NSW, Australia
| | - Zhimin Shao
- Department of Breast Surgery, Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Patrick Brennan
- Medical Radiation Sciences, Faculty of Health Sciences, The University of Sydney, Lidcombe, NSW, Australia
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23
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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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24
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Waade GG, Highnam R, Hauge IHR, McEntee MF, Hofvind S, Denton E, Kelly J, Sarwar JJ, Hogg P. Impact of errors in recorded compressed breast thickness measurements on volumetric density classification using volpara v1.5.0 software. Med Phys 2017; 43:2870-2876. [PMID: 27277035 DOI: 10.1118/1.4948503] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
PURPOSE Mammographic density has been demonstrated to predict breast cancer risk. It has been proposed that it could be used for stratifying screening pathways and recommending additional imaging. Volumetric density tools use the recorded compressed breast thickness (CBT) of the breast measured at the x-ray unit in their calculation; however, the accuracy of the recorded thickness can vary. The aim of this study was to investigate whether inaccuracies in recorded CBT impact upon volumetric density classification and to examine whether the current quality control (QC) standard is sufficient for assessing mammographic density. METHODS Raw data from 52 digital screening mammograms were included in the study. For each image, the clinically recorded CBT was artificially increased and decreased in increments of 1 mm to simulate measurement error, until ±15% from the recorded CBT was reached. New images were created for each 1 mm step in thickness resulting in a total of 974 images which then had volpara density grade (VDG) and volumetric density percentage assigned. RESULTS A change in VDG was observed in 38.5% (n = 20) of mammograms when applying ±15% error to the recorded CBT and 11.5% (n = 6) was within the QC standard prescribed error of ±5 mm. CONCLUSIONS The current QC standard of ±5 mm error in recorded CBT creates the potential for error in mammographic density measurement. This may lead to inaccurate classification of mammographic density. The current QC standard for assessing mammographic density should be reconsidered.
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Affiliation(s)
- Gunvor Gipling Waade
- Department of Life Sciences and Health, Faculty of Health Sciences, Oslo and Akershus University College of Applied Sciences, Box 4, St. Olavs Plass, 0130, Oslo, Norway and School of Health Sciences, University of Salford, Salford M6 6PU, United Kingdom
| | - Ralph Highnam
- Volpara Solutions Limited, P.O. Box 24404, Manners St Central, Wellington 6142, New Zealand
| | - Ingrid H R Hauge
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, 4950 Nydalen, Norway
| | - Mark F McEntee
- Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, M205, Cumberland Campus, 75 East Street, Lidcombe, Sydney, NSW, 2141, Australia
| | - Solveig Hofvind
- Department of Screening, Cancer Registry of Norway, N-0304, Oslo, Norway and Department of Life Sciences and Health, Faculty of Health Sciences, Oslo and Akershus University College of Applied Sciences, Box 4, St. Olavs Plass, 0130, Oslo, Norway
| | - Erika Denton
- Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, United Kingdom
| | - Judith Kelly
- The Countess of Chester Hospitals NHS Foundation Trust, Chester, CH2 1UL, United Kingdom
| | - Jasmine J Sarwar
- School of Health Sciences, University of Salford, Salford M6 6PU, United Kingdom
| | - Peter Hogg
- School of Health Sciences, University of Salford, Salford M6 6PU, United Kingdom and Karolinska Institute, Stockholm, Sweden
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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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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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Reliability of Computer-Assisted Breast Density Estimation: Comparison of Interactive Thresholding, Semiautomated, and Fully Automated Methods. AJR Am J Roentgenol 2016; 207:126-34. [PMID: 27187523 DOI: 10.2214/ajr.15.15469] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
OBJECTIVE The purpose of this study was to investigate the reliability of computer-assisted methods of estimating breast density. MATERIALS AND METHODS Craniocaudal mammograms of 100 healthy subjects were collected from a screening mammography database. Three expert readers independently assessed mammographic breast density twice in a 1-month period using interactive thresholding and semiautomated methods. In addition, fully automated breast density estimation software was used to generate objective breast density estimates. The reliability of the computer-assisted breast density estimation was assessed in terms of concordance correlation coefficients, limits of agreement, systematic difference, and reader variability. RESULTS Statistically significant systematic bias (paired t test, p < 0.01) and variability (4.75-10.91) were found within and between readers for both the interactive thresholding and the semiautomated methods. Using the semiautomated method significantly reduced the within-reader bias of one reader (p < 0.02) and the between-reader variability of all three readers (p < 0.05). The breast density estimates obtained with the fully automated method had excellent agreement with those of the reference standard (concordance correlation coefficient, 0.93) without a significant systematic difference. CONCLUSION Reader-dependent variability and systematic bias exist in breast density estimates obtained with the interactive thresholding method, but they may be reduced in part by use of the semiautomated method. Assessing reader performance may be necessary for more reliable breast density estimation, especially for surveillance of breast density over time. The fully automated method has the potential to provide reliable breast density estimates nearly free from reader-dependent systematic bias and reader variability.
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Kallenberg M, Petersen K, Nielsen M, Ng AY, Igel C, Vachon CM, Holland K, Winkel RR, Karssemeijer N, Lillholm M. Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:1322-1331. [PMID: 26915120 DOI: 10.1109/tmi.2016.2532122] [Citation(s) in RCA: 278] [Impact Index Per Article: 34.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/14/2023]
Abstract
Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.
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Ng KH, Lau S. Vision 20/20: Mammographic breast density and its clinical applications. Med Phys 2015; 42:7059-77. [PMID: 26632060 DOI: 10.1118/1.4935141] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Affiliation(s)
- Kwan-Hoong Ng
- Department of Biomedical Imaging and University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
| | - Susie Lau
- Department of Biomedical Imaging and University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia
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31
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Mammographic Breast Density in Chinese Women: Spatial Distribution and Autocorrelation Patterns. PLoS One 2015; 10:e0136881. [PMID: 26332221 PMCID: PMC4558090 DOI: 10.1371/journal.pone.0136881] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Accepted: 08/10/2015] [Indexed: 01/01/2023] Open
Abstract
Mammographic breast density (MBD) is a strong risk factor for breast cancer. The spatial distribution of MBD in the breast is variable and dependent on physiological, genetic, environmental and pathological factors. This pilot study aims to define the spatial distribution and autocorrelation patterns of MBD in Chinese women aged 40–60. By analyzing their digital mammographic images using a public domain Java image processing program for segmentation and quantification of MBD, we found their left and right breasts were symmetric to each other in regard to their breast size (Total Breast Area), the amount of BMD (overall PD) and Moran's I values. Their MBD was also spatially autocorrelated together in the anterior part of the breast in those with a smaller breast size, while those with a larger breast size tend to have their MBD clustered near the posterior part of the breast. Finally, we observed that the autocorrelation pattern of MBD was dispersed after a 3-year observation period.
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Keller BM, Chen J, Daye D, Conant EF, Kontos D. Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography. Breast Cancer Res 2015; 17:117. [PMID: 26303303 PMCID: PMC4549121 DOI: 10.1186/s13058-015-0626-8] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 08/04/2015] [Indexed: 12/30/2022] Open
Abstract
INTRODUCTION Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA). METHODS Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided. RESULTS All automated density measures had a significant association with breast cancer (OR = 1.47-2.23, AUC = 0.59-0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96-2.64, AUC = 0.82-0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80). CONCLUSIONS Our study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.
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Affiliation(s)
- Brad M Keller
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
| | - Jinbo Chen
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 203 Blockley Hall, 423 Guardian Drive, Philadelphia, PA, USA.
| | - Dania Daye
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
| | - Despina Kontos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 1 Silverstein Building, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
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He W, Juette A, Denton ERE, Oliver A, Martí R, Zwiggelaar R. A Review on Automatic Mammographic Density and Parenchymal Segmentation. Int J Breast Cancer 2015; 2015:276217. [PMID: 26171249 PMCID: PMC4481086 DOI: 10.1155/2015/276217] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2015] [Revised: 04/21/2015] [Accepted: 05/17/2015] [Indexed: 01/03/2023] Open
Abstract
Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.
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Affiliation(s)
- Wenda He
- Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
| | - Arne Juette
- Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK
| | - Erika R. E. Denton
- Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK
| | - Arnau Oliver
- Department of Architecture and Computer Technology, University of Girona, 17071 Girona, Spain
| | - Robert Martí
- Department of Architecture and Computer Technology, University of Girona, 17071 Girona, Spain
| | - Reyer Zwiggelaar
- Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
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Keller BM, McCarthy AM, Chen J, Armstrong K, Conant EF, Domchek SM, Kontos D. Associations between breast density and a panel of single nucleotide polymorphisms linked to breast cancer risk: a cohort study with digital mammography. BMC Cancer 2015; 15:143. [PMID: 25881232 PMCID: PMC4365961 DOI: 10.1186/s12885-015-1159-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2014] [Accepted: 03/04/2015] [Indexed: 12/16/2022] Open
Abstract
Background Breast density and single-nucleotide polymorphisms (SNPs) have both been associated with breast cancer risk. To determine the extent to which these two breast cancer risk factors are associated, we investigate the association between a panel of validated SNPs related to breast cancer and quantitative measures of mammographic density in a cohort of Caucasian and African-American women. Methods In this IRB-approved, HIPAA-compliant study, we analyzed a screening population of 639 women (250 African American and 389 Caucasian) who were tested with a validated panel assay of 12 SNPs previously associated to breast cancer risk. Each woman underwent digital mammography as part of routine screening and all were interpreted as negative. Both absolute and percent estimates of area and volumetric density were quantified on a per-woman basis using validated software. Associations between the number of risk alleles in each SNP and the density measures were assessed through a race-stratified linear regression analysis, adjusted for age, BMI, and Gail lifetime risk. Results The majority of SNPs were not found to be associated with any measure of breast density. SNP rs3817198 (in LSP1) was significantly associated with both absolute area (p = 0.004) and volumetric (p = 0.019) breast density in Caucasian women. In African-American women, SNPs rs3803662 (in TNRC9/TOX3) and rs4973768 (in NEK10) were significantly associated with absolute (p = 0.042) and percent (p = 0.028) volume density respectively. Conclusions The majority of SNPs investigated in our study were not found to be significantly associated with breast density, even when accounting for age, BMI, and Gail risk, suggesting that these two different risk factors contain potentially independent information regarding a woman’s risk to develop breast cancer. Additionally, the few statistically significant associations between breast density and SNPs were different for Caucasian versus African American women. Larger prospective studies are warranted to validate our findings and determine potential implications for breast cancer risk assessment. Electronic supplementary material The online version of this article (doi:10.1186/s12885-015-1159-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Brad M Keller
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3600 Market St. Ste 360, Philadelphia, PA, 19104, USA.
| | - Anne Marie McCarthy
- Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
| | - Jinbo Chen
- Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
| | - Katrina Armstrong
- Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
| | - Emily F Conant
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3600 Market St. Ste 360, Philadelphia, PA, 19104, USA.
| | - Susan M Domchek
- Abramson Cancer Center, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3600 Market St. Ste 360, Philadelphia, PA, 19104, USA.
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Section Editor's Notebook. Breast Density and Breast Cancer Risk—Eyes Wide Open. AJR Am J Roentgenol 2015; 204:231-3. [DOI: 10.2214/ajr.14.14113] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Howell A, Anderson AS, Clarke RB, Duffy SW, Evans DG, Garcia-Closas M, Gescher AJ, Key TJ, Saxton JM, Harvie MN. Risk determination and prevention of breast cancer. Breast Cancer Res 2014; 16:446. [PMID: 25467785 PMCID: PMC4303126 DOI: 10.1186/s13058-014-0446-2] [Citation(s) in RCA: 204] [Impact Index Per Article: 20.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is an increasing public health problem. Substantial advances have been made in the treatment of breast cancer, but the introduction of methods to predict women at elevated risk and prevent the disease has been less successful. Here, we summarize recent data on newer approaches to risk prediction, available approaches to prevention, how new approaches may be made, and the difficult problem of using what we already know to prevent breast cancer in populations. During 2012, the Breast Cancer Campaign facilitated a series of workshops, each covering a specialty area of breast cancer to identify gaps in our knowledge. The risk-and-prevention panel involved in this exercise was asked to expand and update its report and review recent relevant peer-reviewed literature. The enlarged position paper presented here highlights the key gaps in risk-and-prevention research that were identified, together with recommendations for action. The panel estimated from the relevant literature that potentially 50% of breast cancer could be prevented in the subgroup of women at high and moderate risk of breast cancer by using current chemoprevention (tamoxifen, raloxifene, exemestane, and anastrozole) and that, in all women, lifestyle measures, including weight control, exercise, and moderating alcohol intake, could reduce breast cancer risk by about 30%. Risk may be estimated by standard models potentially with the addition of, for example, mammographic density and appropriate single-nucleotide polymorphisms. This review expands on four areas: (a) the prediction of breast cancer risk, (b) the evidence for the effectiveness of preventive therapy and lifestyle approaches to prevention, (c) how understanding the biology of the breast may lead to new targets for prevention, and (d) a summary of published guidelines for preventive approaches and measures required for their implementation. We hope that efforts to fill these and other gaps will lead to considerable advances in our efforts to predict risk and prevent breast cancer over the next 10 years.
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Affiliation(s)
- Anthony Howell
- Genesis Breast Cancer Prevention Centre, University Hospital of South Manchester, Southmoor Road, Wythenshawe, M29 9LT Manchester, UK
- The Christie, NHS Foundation Trust, Wilmslow Road, Manchester, M20 2QJ UK
- Breakthrough Breast Cancer Research Unit, Institute of Cancer Sciences, University of Manchester, Wilmslow Road, Manchester, M20 2QJ UK
| | - Annie S Anderson
- Centre for Public Health Nutrition Research, Division of Cancer Research, Level 7, University of Dundee, Ninewells Hospital & Medical School, Mailbox 7, George Pirie Way, Dundee, DD1 9SY UK
| | - Robert B Clarke
- Breakthrough Breast Cancer Research Unit, Institute of Cancer Sciences, University of Manchester, Wilmslow Road, Manchester, M20 2QJ UK
| | - Stephen W Duffy
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - D Gareth Evans
- Genesis Breast Cancer Prevention Centre, University Hospital of South Manchester, Southmoor Road, Wythenshawe, M29 9LT Manchester, UK
- The Christie, NHS Foundation Trust, Wilmslow Road, Manchester, M20 2QJ UK
- Manchester Centre for Genomic Medicine, The University of Manchester, Manchester Academic Health Science Centre, Central Manchester Foundation Trust, St. Mary’s Hospital, Oxford Road, Manchester, M13 9WL UK
| | - Montserat Garcia-Closas
- Division of Genetics and Epidemiology, Institute of Cancer Research, Cotswold Road, Sutton, SM2 5NG London, UK
| | - Andy J Gescher
- Department of Cancer Studies and Molecular Medicine, University of Leicester, University Road, Leicester, LE2 7LX UK
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Roosevelt Drive, Oxford, OX3 7LF UK
| | - John M Saxton
- School of Health Sciences, Faculty of Medicine and Health Sciences, University of East Anglia, University Drive, Norwich, NR4 7TJ UK
| | - Michelle N Harvie
- Genesis Breast Cancer Prevention Centre, University Hospital of South Manchester, Southmoor Road, Wythenshawe, M29 9LT Manchester, UK
- The Christie, NHS Foundation Trust, Wilmslow Road, Manchester, M20 2QJ UK
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Ekpo EU, McEntee MF. Measurement of breast density with digital breast tomosynthesis--a systematic review. Br J Radiol 2014; 87:20140460. [PMID: 25146640 DOI: 10.1259/bjr.20140460] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Digital breast tomosynthesis (DBT) has gained acceptance as an adjunct to digital mammography in screening. Now that breast density reporting is mandated in several states in the USA, it is increasingly important that the methods of breast density measurement be robust, reliable and consistent. Breast density assessment with DBT needs some consideration since quantitative methods are modelled for two-dimensional (2D) mammography. A review of methods used for breast density assessment with DBT was performed. Existing evidence shows Cumulus has better reproducibility than that of the breast imaging reporting and data system (BI-RADS®) but still suffers from subjective variability; MedDensity is limited by image noise, whilst Volpara and Quantra are robust and consistent. The reported BI-RADs inter-reader breast density agreement (k) ranged from 0.65 to 0.91, with inter-reader correlation (r) ranging from 0.70 to 0.93. The correlation (r) between BI-RADS and Cumulus ranged from 0.54-0.94, whilst that of BI-RADs and MedDensity ranged from 0.48-0.78. The reported agreement (k) between BI-RADs and Volpara is 0.953. Breast density correlation between DBT and 2D mammography ranged from 0.73 to 0.97, with agreement (k) ranging from 0.56 to 0.96. To avoid variability and provide more reliable breast density information for clinicians, automated volumetric methods are preferred.
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Affiliation(s)
- E U Ekpo
- 1 Discipline of Medical Radiation Science, Faculty of Health Science, University of Sydney, Sydney, NSW, Australia
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Assessment of a fully automated, high-throughput mammographic density measurement tool for use with processed digital mammograms. Cancer Causes Control 2014; 25:1037-43. [PMID: 24962023 DOI: 10.1007/s10552-014-0404-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Accepted: 05/20/2014] [Indexed: 10/25/2022]
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Huo CW, Chew GL, Britt KL, Ingman WV, Henderson MA, Hopper JL, Thompson EW. Mammographic density-a review on the current understanding of its association with breast cancer. Breast Cancer Res Treat 2014; 144:479-502. [PMID: 24615497 DOI: 10.1007/s10549-014-2901-2] [Citation(s) in RCA: 144] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Accepted: 02/24/2014] [Indexed: 01/07/2023]
Abstract
There has been considerable recent interest in the genetic, biological and epidemiological basis of mammographic density (MD), and the search for causative links between MD and breast cancer (BC) risk. This report will critically review the current literature on MD and summarize the current evidence for its association with BC. Keywords 'mammographic dens*', 'dense mammary tissue' or 'percent dens*' were used to search the existing literature in English on PubMed and Medline. All reports were critically analyzed. The data were assigned to one of the following aspects of MD: general association with BC, its relationship with the breast hormonal milieu, the cellular basis of MD, the generic variations of MD, and its significance in the clinical setting. MD adjusted for age, and BMI is associated with increased risk of BC diagnosis, advanced tumour stage at diagnosis and increased risk of both local recurrence and second primary cancers. The MD measures that predict BC risk have high heritability, and to date several genetic markers associated with BC risk have been found to also be associated with these MD risk predictors. Change in MD could be a predictor of the extent of chemoprevention with tamoxifen. Although the biological and genetic pathways that determine and perhaps modulate MD remain largely unresolved, significant inroads are being made into the understanding of MD, which may lead to benefits in clinical screening, assessment and treatment strategies. This review provides a timely update on the current understanding of MD's association with BC risk.
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Affiliation(s)
- C W Huo
- Department of Surgery, University of Melbourne, St. Vincent's Hospital, Melbourne, Australia,
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