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Mohammadi S, Ghaderi S, Mohammadi M, Ghaznavi H, Mohammadian K. Breast percent density changes in digital mammography pre- and post-radiotherapy. J Med Radiat Sci 2024. [PMID: 38571377 DOI: 10.1002/jmrs.788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
INTRODUCTION Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast-conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post-operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy. METHODS This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15 months post-treatment using standardised mean difference (SMD) with Cohen's d, chi-square, and paired sample t-tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis. RESULTS The mean age was 53.2 years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3- and 15-months post-treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15 months post-treatment for detecting changes in breast density. CONCLUSIONS This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short-term follow-up period. However, the results suggest that quantitative density assessment could be valuable for long-term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.
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Affiliation(s)
- Sana Mohammadi
- Department of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Ghaznavi
- Department of Radiology, Faculty of Paramedical Sciences, Kurdistan University of Medical Sciences, Sanandaj, Iran
| | - Kamal Mohammadian
- Department of Radiation Oncology, Hamadan University of Medical Sciences, Mahdieh Center, Hamadan, Iran
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Yan H, Ren W, Jia M, Xue P, Li Z, Zhang S, He L, Qiao Y. Breast cancer risk factors and mammographic density among 12518 average-risk women in rural China. BMC Cancer 2023; 23:952. [PMID: 37814233 PMCID: PMC10561452 DOI: 10.1186/s12885-023-11444-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Mammographic density (MD) is a strong risk factor for breast cancer. We aimed to evaluate the association between MD and breast cancer related risk factors among average-risk women in rural China. METHODS This is a population-based screening study. 12518 women aged 45-64 years with complete MD data from three maternal and childcare hospitals in China were included in the final analysis. ORs and 95%CIs were estimated using generalized logit model by comparing each higher MD (BI-RADS b, c, d) to the lowest group (BI-RADS a). The cumulative logistic regression model was used to estimate the ORtrend (95%CI) and Ptrend by treating MD as an ordinal variable. RESULTS Older age (ORtrend = 0.81, 95%CI: 0.79-0.81, per 2-year increase), higher BMI (ORtrend = 0.73, 95%CI: 0.71-0.75, per 2 kg/m2), more births (ORtrend = 0.47, 95%CI: 0.41-0.54, 3 + vs. 0-1), postmenopausal status (ORtrend = 0.42, 95%CI: 0.38-0.46) were associated with lower MD. For parous women, longer duration of breastfeeding was found to be associated with higher MD when adjusting for study site, age, BMI, and age of first full-term birth (ORtrend = 1.53, 95%CI: 1.27-1.85, 25 + months vs. no breastfeeding; ORtrend = 1.45, 95%CI: 1.20-1.75, 19-24 months vs. no breastfeeding), however, the association became non-significant when adjusting all covariates. Associations between examined risk factors and MD were similar in premenopausal and postmenopausal women except for level of education and oral hormone drug usage. Higher education was only found to be associated with an increased proportion of dense breasts in postmenopausal women (ORtrend = 1.08, 95%CI: 1.02-1.15). Premenopausal women who ever used oral hormone drug were less likely to have dense breasts, though the difference was marginally significant (OR = 0.54, P = 0.045). In postmenopausal women, we also found the proportion of dense breasts increased with age at menopause (ORtrend = 1.31, 95%CI: 1.21-1.43). CONCLUSIONS In Chinese women with average risk for breast cancer, we found MD was associated with age, BMI, menopausal status, lactation, and age at menopausal. This finding may help to understand the etiology of breast cancer and have implications for breast cancer prevention in China.
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Affiliation(s)
- Huijiao Yan
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Wenhui Ren
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Mengmeng Jia
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Zhifang Li
- Changzhi Medical College, Changzhi, 046000, Shanxi, China
| | - Shaokai Zhang
- Department of Cancer Epidemiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Zhengzhou, 450008, China
| | - Lichun He
- Mianyang Maternal & Child Health Care Hospital, Mianyang Children's Hospital, Mianyang, 621000, China
| | - Youlin Qiao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
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Behrens A, Fasching PA, Schwenke E, Gass P, Häberle L, Heindl F, Heusinger K, Lotz L, Lubrich H, Preuß C, Schneider MO, Schulz-Wendtland R, Stumpfe FM, Uder M, Wunderle M, Zahn AL, Hack CC, Beckmann MW, Emons J. Predicting mammographic density with linear ultrasound transducers. Eur J Med Res 2023; 28:384. [PMID: 37770952 PMCID: PMC10537934 DOI: 10.1186/s40001-023-01327-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 08/28/2023] [Indexed: 09/30/2023] Open
Abstract
BACKGROUND High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments. METHODS We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON® 730 Expert system (GE Medical Systems Kretztechnik GmbH & Co OHG, Austria). Gray level bins were calculated from ultrasound images to characterize mammographic density. Percentage mammographic density (PMD) was predicted by gray level bins using various regression models. RESULTS Gray level bins and PMD correlated to a certain extent. Spearman's ρ ranged from - 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R2, 0.255). Overall, ultrasound images from the VOLUSON® 730 Expert device in this study showed limited predictive power for PMD when correlated with the corresponding mammograms. CONCLUSIONS In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).
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Affiliation(s)
- Annika Behrens
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany.
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Eva Schwenke
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Paul Gass
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Lothar Häberle
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
- Biostatistics Unit, Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Felix Heindl
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Katharina Heusinger
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Laura Lotz
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Hannah Lubrich
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Caroline Preuß
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Michael O Schneider
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Rüdiger Schulz-Wendtland
- Department of Radiology, Erlangen University Hospital, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Florian M Stumpfe
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Michael Uder
- Department of Radiology, Erlangen University Hospital, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
| | - Marius Wunderle
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Anna L Zahn
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Carolin C Hack
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
| | - Julius Emons
- Department of Gynecology and Obstetrics, Erlangen University Hospital, University Breast Center for Franconia, Comprehensive Cancer Center European Metropolitan Area Nuremberg (CCC ER-EMN), Friedrich-Alexander University Erlangen-Nuremberg, Universitätsstrasse 21-23, 91054, Erlangen, Germany
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Heindl F, Fasching PA, Hein A, Hack CC, Heusinger K, Gass P, Pöschke P, Stübs FA, Schulz-Wendtland R, Hartmann A, Erber R, Beckmann MW, Meyer J, Häberle L, Jud SM, Emons J. Mammographic density and prognosis in primary breast cancer patients. Breast 2021; 59:51-57. [PMID: 34157655 PMCID: PMC8237359 DOI: 10.1016/j.breast.2021.06.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/11/2021] [Accepted: 06/12/2021] [Indexed: 12/23/2022] Open
Abstract
PURPOSE Mammographic density (MD) is one of the strongest risk factors for breast cancer (BC). However, the influence of MD on the BC prognosis is unclear. The objective of this study was therefore to investigate whether percentage MD (PMD) is associated with a difference in disease-free or overall survival in primary BC patients. METHODS A total of 2525 patients with primary, metastasis-free BC were followed up retrospectively for this analysis. For all patients, PMD was evaluated by two readers using a semi-automated method. The association between PMD and prognosis was evaluated using Cox regression models with disease-free survival (DFS) and overall survival (OS) as the outcome, and the following adjustments: age at diagnosis, year of diagnosis, body mass index, tumor stage, grading, lymph node status, hormone receptor and HER2 status. RESULTS After median observation periods of 9.5 and 10.0 years, no influence of PMD on DFS (p = 0.46, likelihood ratio test (LRT)) or OS (p = 0.22, LRT), respectively, was found. In the initial unadjusted analysis higher PMD was associated with longer DFS and OS. The effect of PMD on DFS and OS disappeared after adjustment for age and was caused by the underlying age effect. CONCLUSIONS Although MD is one of the strongest independent risk factors for BC, in our collective PMD is not associated with disease-free and overall survival in patients with BC.
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Affiliation(s)
- Felix Heindl
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Peter A Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany.
| | - Alexander Hein
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Carolin C Hack
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Katharina Heusinger
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Paul Gass
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Patrik Pöschke
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Frederik A Stübs
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Rüdiger Schulz-Wendtland
- Institute of Diagnostic Radiology, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Arndt Hartmann
- Institute of Pathology, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Ramona Erber
- Institute of Pathology, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Matthias W Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Julia Meyer
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany; Biostatistics Unit, Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Lothar Häberle
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany; Biostatistics Unit, Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Sebastian M Jud
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
| | - Julius Emons
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center, European Metropolitan Area Erlangen-Nuremberg (CCC ER-EMN), Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany
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Khushi M, Dean IM, Teber ET, Chircop M, Arthur JW, Flores-Rodriguez N. Automated classification and characterization of the mitotic spindle following knockdown of a mitosis-related protein. BMC Bioinformatics 2017; 18:566. [PMID: 29297284 PMCID: PMC5751558 DOI: 10.1186/s12859-017-1966-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023] Open
Abstract
Background Cell division (mitosis) results in the equal segregation of chromosomes between two daughter cells. The mitotic spindle plays a pivotal role in chromosome alignment and segregation during metaphase and anaphase. Structural or functional errors of this spindle can cause aneuploidy, a hallmark of many cancers. To investigate if a given protein associates with the mitotic spindle and regulates its assembly, stability, or function, fluorescence microscopy can be performed to determine if disruption of that protein induces phenotypes indicative of spindle dysfunction. Importantly, functional disruption of proteins with specific roles during mitosis can lead to cancer cell death by inducing mitotic insult. However, there is a lack of automated computational tools to detect and quantify the effects of such disruption on spindle integrity. Results We developed the image analysis software tool MatQuantify, which detects both large-scale and subtle structural changes in the spindle or DNA and can be used to statistically compare the effects of different treatments. MatQuantify can quantify various physical properties extracted from fluorescence microscopy images, such as area, lengths of various components, perimeter, eccentricity, fractal dimension, satellite objects and orientation. It can also measure textual properties including entropy, intensities and the standard deviation of intensities. Using MatQuantify, we studied the effect of knocking down the protein clathrin heavy chain (CHC) on the mitotic spindle. We analysed 217 microscopy images of untreated metaphase cells, 172 images of metaphase cells transfected with small interfering RNAs targeting the luciferase gene (as a negative control), and 230 images of metaphase cells depleted of CHC. Using the quantified data, we trained 23 supervised machine learning classification algorithms. The Support Vector Machine learning algorithm was the most accurate method (accuracy: 85.1%; area under the curve: 0.92) for classifying a spindle image. The Kruskal-Wallis and Tukey-Kramer tests demonstrated that solidity, compactness, eccentricity, extent, mean intensity and number of satellite objects (multipolar spindles) significantly differed between CHC-depleted cells and untreated/luciferase-knockdown cells. Conclusion MatQuantify enables automated quantitative analysis of images of mitotic spindles. Using this tool, researchers can unambiguously test if disruption of a protein-of-interest changes metaphase spindle maintenance and thereby affects mitosis.
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Affiliation(s)
- Matloob Khushi
- Children's Medical Research Institute, The University of Sydney, Westmead, NSW, Australia. .,Current address: School of IT, The University of Sydney, Darlington, NSW, Australia.
| | - Imraan M Dean
- Children's Medical Research Institute, The University of Sydney, Westmead, NSW, Australia
| | - Erdahl T Teber
- Children's Medical Research Institute, The University of Sydney, Westmead, NSW, Australia
| | - Megan Chircop
- Children's Medical Research Institute, The University of Sydney, Westmead, NSW, Australia
| | - Jonathan W Arthur
- Children's Medical Research Institute, The University of Sydney, Westmead, NSW, Australia
| | - Neftali Flores-Rodriguez
- Children's Medical Research Institute, The University of Sydney, Westmead, NSW, Australia.,Current address: School of Biomedical Sciences, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
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Vinnicombe SJ. Breast density: why all the fuss? Clin Radiol 2017; 73:334-357. [PMID: 29273225 DOI: 10.1016/j.crad.2017.11.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Accepted: 11/17/2017] [Indexed: 01/06/2023]
Abstract
The term "breast density" or mammographic density (MD) denotes those components of breast parenchyma visualised at mammography that are denser than adipose tissue. MD is composed of a mixture of epithelial and stromal components, notably collagen, in variable proportions. MD is most commonly assessed in clinical practice with the time-honoured method of visual estimation of area-based percent density (PMD) on a mammogram, with categorisation into quartiles. The computerised semi-automated thresholding method, Cumulus, also yielding area-based percent density, is widely used for research purposes; however, the advent of fully automated volumetric methods developed as a consequence of the widespread use of digital mammography (DM) and yielding both absolute and percent dense volumes, has resulted in an explosion of interest in MD recently. Broadly, the importance of MD is twofold: firstly, the presence of marked MD significantly reduces mammographic sensitivity for breast cancer, even with state-of-the-art DM. Recognition of this led to the formation of a powerful lobby group ('Are You Dense') in the US, as a consequence of which 32 states have legislated for mandatory disclosure of MD to women undergoing mammography. Secondly, it is now widely accepted that MD is in itself a risk factor for breast cancer, with a four-to sixfold increased relative risk in women with PMD in the highest quintile compared to those with PMD in the lowest quintile. Consequently, major research efforts are underway to assess whether use of MD could provide a major step forward towards risk-adapted, personalised breast cancer prevention, imaging, and treatment.
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Affiliation(s)
- S J Vinnicombe
- Cancer Research, School of Medicine, Level 7, Mailbox 4, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK.
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Hou XY, Niu HY, Huang XL, Gao Y. Correlation of Breast Ultrasound Classifications with Breast Cancer in Chinese Women. ULTRASOUND IN MEDICINE & BIOLOGY 2016; 42:2616-2621. [PMID: 27554070 DOI: 10.1016/j.ultrasmedbio.2016.07.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Revised: 06/17/2016] [Accepted: 07/09/2016] [Indexed: 06/06/2023]
Abstract
The aim of this study was to identify potential links between ultrasonographic breast parenchymal patterns and the risk of breast cancer in Chinese women. The population of Chinese women at high risk for breast cancer was explored using the ultrasonographic classification. Ultrasonographic parenchymal patterns were classified into four types: heterogeneous type, ductal type, mixed type and fibrous type. A total of 5879 Chinese women underwent breast ultrasound examination from May 2010 to April 2014. Of the 5879 women, 256 women had pathology-confirmed breast cancer. Among the remaining 5623 women, 512 randomly selected, age-matched women were recruited into the present study. The correlation between ultrasonographic type and breast cancer revealed that the odds ratio (OR) was highest for the heterogeneous type (odds ratio = 4.11, 95% confidence interval: 2.01-8.41, p < 0.001), followed by the fibrous type (odds ratio = 2.05, 95% confidence interval: 1.51-2.78, p < 0.001). The odds ratios of the ductal and mixed types were both less than 1 (p < 0.05). This study indicates that the heterogeneous and fibrous types in the ultrasonographic classification are associated with an increased risk of breast cancer and, therefore, can be used as a marker of breast cancer risk in the female population of China.
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Affiliation(s)
- Xin-Yan Hou
- Department of Ultrasound, PLA Beijing Military General Hospital, Beijing, China.
| | - Hai-Yan Niu
- Department of Ultrasound, PLA Beijing Military General Hospital, Beijing, China
| | - Xiao-Ling Huang
- Department of Ultrasound, PLA Beijing Military General Hospital, Beijing, China
| | - Yu Gao
- Department of Ultrasound, PLA Beijing Military General Hospital, Beijing, China
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Gastounioti A, Conant EF, Kontos D. Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment. Breast Cancer Res 2016; 18:91. [PMID: 27645219 PMCID: PMC5029019 DOI: 10.1186/s13058-016-0755-8] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND The assessment of a woman's risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation. MAIN TEXT The analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation. CONCLUSIONS The objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.
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Affiliation(s)
- Aimilia Gastounioti
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Emily F Conant
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Winkel RR, von Euler-Chelpin M, Nielsen M, Petersen K, Lillholm M, Nielsen MB, Lynge E, Uldall WY, Vejborg I. Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study. BMC Cancer 2016; 16:414. [PMID: 27387546 PMCID: PMC4936245 DOI: 10.1186/s12885-016-2450-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 06/21/2016] [Indexed: 01/12/2023] Open
Abstract
Background Mammographic density is a well-established risk factor for breast cancer. We investigated the association between three different methods of measuring density or parenchymal pattern/texture on digitized film-based mammograms, and examined to what extent textural features independently and jointly with density can improve the ability to identify screening women at increased risk of breast cancer. Methods The study included 121 cases and 259 age- and time matched controls based on a cohort of 14,736 women with negative screening mammograms from a population-based screening programme in Denmark in 2007 (followed until 31 December 2010). Mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, Tabár’s classification on parenchymal patterns and a fully automated texture quantification technique. The individual and combined association with breast cancer was estimated using binary logistic regression to calculate Odds Ratios (ORs) and the area under the receiver operating characteristic (ROC) curves (AUCs). Results Cases showed significantly higher BI-RADS and texture scores on average than controls (p < 0.001). All three methods were individually able to segregate women into different risk groups showing significant ORs for BI-RADS D3 and D4 (OR: 2.37; 1.32–4.25 and 3.93; 1.88–8.20), Tabár’s PIII and PIV (OR: 3.23; 1.20–8.75 and 4.40; 2.31–8.38), and the highest quartile of the texture score (3.04; 1.63–5.67). AUCs for BI-RADS, Tabár and the texture scores (continuous) were 0.63 (0.57–0–69), 0.65 (0.59–0–71) and 0.63 (0.57–0–69), respectively. Combining two or more methods increased model fit in all combinations, demonstrating the highest AUC of 0.69 (0.63-0.74) when all three methods were combined (a significant increase from standard BI-RADS alone). Conclusion Our findings suggest that the (relative) amount of fibroglandular tissue (density) and mammographic structural features (texture/parenchymal pattern) jointly can improve risk segregation of screening women, using information already available from normal screening routine, in respect to future personalized screening strategies. Electronic supplementary material The online version of this article (doi:10.1186/s12885-016-2450-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rikke Rass Winkel
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark.
| | - My von Euler-Chelpin
- Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, DK-1014, Copenhagen K, Denmark
| | - Mads Nielsen
- Department of Computer Sciences, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark.,Biomediq, Fruebjergvej 3, DK-2100, Copenhagen Ø, Denmark
| | - Kersten Petersen
- Department of Computer Sciences, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark
| | | | - Michael Bachmann Nielsen
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark
| | - Elsebeth Lynge
- Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, DK-1014, Copenhagen K, Denmark
| | - Wei Yao Uldall
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark
| | - Ilse Vejborg
- Department of Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark
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van Luijt PA, Rozemeijer K, Naber SK, Heijnsdijk EAM, van Rosmalen J, van Ballegooijen M, de Koning HJ. The role of pre-invasive disease in overdiagnosis: A microsimulation study comparing mass screening for breast cancer and cervical cancer. J Med Screen 2016; 23:210-216. [DOI: 10.1177/0969141316629505] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 01/07/2016] [Indexed: 12/29/2022]
Abstract
Objective Although early detection of cancer through screening can prevent cancer deaths, a drawback of screening is overdiagnosis. Overdiagnosis has been much debated in breast cancer screening, but less so in cervical cancer screening. We examined the impact of overdiagnosis by comparing two screening programmes in the Netherlands. Methods We estimated overdiagnosis rates by microsimulation for breast cancer screening and cervical cancer screening, using a cohort of women born in 1982 with lifelong follow-up. Overdiagnosis estimates were made analogous to two definitions formed by the UK 2012 breast screening review. Pre-invasive disease was included in both definitions. Results Screening prevented 921 cervical cancers (−55%) and 378 cervical cancer deaths (−59%), and 169 (−1.3%) breast cancer cases and 970 breast cancer deaths (−21%). The cervical cancer overdiagnosis rate was 74.8% (including pre-invasive disease). Breast cancer overdiagnosis was estimated at 2.5% (including pre-invasive disease). For women of all ages in breast cancer screening, an excess of 207 diagnoses/100,000 women was found, compared with an excess of 3999 diagnoses/100,000 women in cervical cancer screening. Conclusions For breast cancer, the frequency of overdiagnosis in screening is relatively low, but consequences are evident. For cervical cancer, the frequency of overdiagnosis in screening is high, because of detection of pre-invasive disease, but the consequences per case are relatively small due to less invasive treatment. This illustrates that it is necessary to present overdiagnosis in relation to disease stage and consequences.
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Affiliation(s)
| | | | - Steffie K Naber
- Department of Public Health, Erasmus MC, Rotterdam, The Netherlands
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11
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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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12
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Pape R, Spuur KM, Currie G, Greene L. Mammographic parenchymal patterns and breast cancer risk in New South Wales North Coast Aboriginal and Torres Strait Islander women. J Med Radiat Sci 2016; 63:81-8. [PMID: 27350887 PMCID: PMC4914812 DOI: 10.1002/jmrs.160] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2015] [Revised: 11/29/2015] [Accepted: 12/22/2015] [Indexed: 11/07/2022] Open
Abstract
INTRODUCTION The objective of the study was to document the distribution of mammographic parenchymal patterns (MPP) of Indigenous Australian women attending BreastScreen New South Wales (NSW) North Coast, to profile breast cancer risk as it relates to breast density and to explore the correlation between MPP, breast size as described by the posterior nipple line (PNL) and age. METHODS Ethics was granted from CQUniversity Human Research Ethics Committee, NSW Population Health Services Research Ethics Committee and the Aboriginal Health and Medical Research Council Ethics Committee. A quantitative retrospective analysis reviewed 502 screening mammograms against the Tabár I-V MPP classification system. The PNL was measured in millimetres (mm) and the age of the patient documented. RESULTS A statistically significant variation in the distribution of MPP (P < 0.0001) was demonstrated, with patterns of I (23.9%), II (45.6%), III (10.4%), IV (15.9%) and V (4.2%). Statistically significant differences were noted in the age of subjects between patterns (P = 0.0002). Patterns I and V demonstrated statistically significant lower ages than II, III and IV (all P < 0.05). Pattern V demonstrated a statistically significant lower age than pattern I (P = 0.0393). Pattern V demonstrated a statistically significant lower PNL value than all other patterns (all P < 0.001/P < 0.0002); pattern II was statistically significantly higher in PNL value than all other patterns (P < 0.002/P < 0.001). No significant relationship was noted between PNL and age. CONCLUSION The study demonstrated that no identifiable or unique distribution of MPP was noted in this snapshot of Indigenous women. A larger study of Indigenous Australian women is required for validation.
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Affiliation(s)
- Ruth Pape
- School of Medical and Applied Sciences Faculty of Sciences Engineering and Health CQUniversity Mackay Queensland Australia; School of Medicine and Health Sciences Discipline of Medical Imaging UPNG Taurama Campus University of Papua New Guinea Boroko NCD Papua New Guinea
| | - Kelly Maree Spuur
- School of Medical and Applied Sciences Faculty of Sciences Engineering and Health CQUniversity Mackay Queensland Australia; School of Dentistry and Health Sciences Faculty of Science Charles Sturt University Wagga Wagga New South Wales Australia
| | - Geoffrey Currie
- School of Dentistry and Health Sciences Faculty of Science Charles Sturt University Wagga Wagga New South Wales Australia
| | - Lacey Greene
- School of Dentistry and Health Sciences Faculty of Science Charles Sturt University Wagga Wagga New South Wales Australia
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13
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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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14
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15
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Rudolph A, Fasching PA, Behrens S, Eilber U, Bolla MK, Wang Q, Thompson D, Czene K, Brand JS, Li J, Scott C, Pankratz VS, Brandt K, Hallberg E, Olson JE, Lee A, Beckmann MW, Ekici AB, Haeberle L, Maskarinec G, Le Marchand L, Schumacher F, Milne RL, Knight JA, Apicella C, Southey MC, Kapuscinski MK, Hopper JL, Andrulis IL, Giles GG, Haiman CA, Khaw KT, Luben R, Hall P, Pharoah PDP, Couch FJ, Easton DF, Dos-Santos-Silva I, Vachon C, Chang-Claude J. A comprehensive evaluation of interaction between genetic variants and use of menopausal hormone therapy on mammographic density. Breast Cancer Res 2015; 17:110. [PMID: 26275715 PMCID: PMC4537547 DOI: 10.1186/s13058-015-0625-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Accepted: 07/29/2015] [Indexed: 02/02/2023] Open
Abstract
INTRODUCTION Mammographic density is an established breast cancer risk factor with a strong genetic component and can be increased in women using menopausal hormone therapy (MHT). Here, we aimed to identify genetic variants that may modify the association between MHT use and mammographic density. METHODS The study comprised 6,298 postmenopausal women from the Mayo Mammography Health Study and nine studies included in the Breast Cancer Association Consortium. We selected for evaluation 1327 single nucleotide polymorphisms (SNPs) showing the lowest P-values for interaction (P int) in a meta-analysis of genome-wide gene-environment interaction studies with MHT use on risk of breast cancer, 2541 SNPs in candidate genes (AKR1C4, CYP1A1-CYP1A2, CYP1B1, ESR2, PPARG, PRL, SULT1A1-SULT1A2 and TNF) and ten SNPs (AREG-rs10034692, PRDM6-rs186749, ESR1-rs12665607, ZNF365-rs10995190, 8p11.23-rs7816345, LSP1-rs3817198, IGF1-rs703556, 12q24-rs1265507, TMEM184B-rs7289126, and SGSM3-rs17001868) associated with mammographic density in genome-wide studies. We used multiple linear regression models adjusted for potential confounders to evaluate interactions between SNPs and current use of MHT on mammographic density. RESULTS No significant interactions were identified after adjustment for multiple testing. The strongest SNP-MHT interaction (unadjusted P int <0.0004) was observed with rs9358531 6.5kb 5' of PRL. Furthermore, three SNPs in PLCG2 that had previously been shown to modify the association of MHT use with breast cancer risk were found to modify also the association of MHT use with mammographic density (unadjusted P int <0.002), but solely among cases (unadjusted P int SNP×MHT×case-status <0.02). CONCLUSIONS The study identified potential interactions on mammographic density between current use of MHT and SNPs near PRL and in PLCG2, which require confirmation. Given the moderate size of the interactions observed, larger studies are needed to identify genetic modifiers of the association of MHT use with mammographic density.
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Affiliation(s)
- Anja Rudolph
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120, Heidelberg, Germany.
| | - Peter A Fasching
- Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.
- David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, Los Angeles, CA, USA.
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120, Heidelberg, Germany.
| | - Ursula Eilber
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120, Heidelberg, Germany.
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Deborah Thompson
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Judith S Brand
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Jingmei Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | | | | | | | - Emily Hallberg
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
| | - Janet E Olson
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
| | - Adam Lee
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA.
| | - Matthias W Beckmann
- Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.
| | - Arif B Ekici
- Institute of Human Genetics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.
| | - Lothar Haeberle
- Department of Gynaecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany.
| | | | | | - Fredrick Schumacher
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Roger L Milne
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
| | - Julia A Knight
- Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Canada.
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada.
| | - Carmel Apicella
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
| | - Melissa C Southey
- Department of Pathology, The University of Melbourne, Melbourne, Australia.
| | - Miroslav K Kapuscinski
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
| | - Irene L Andrulis
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Canada.
| | - Graham G Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia.
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Kay-Tee Khaw
- MRC Centre for Nutritional Epidemiology in Cancer Prevention and Survival (CNC), University of Cambridge, Cambridge, UK.
| | - Robert Luben
- Clinical Gerontology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
| | - Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, D-69120, Heidelberg, Germany.
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Stone J, Thompson DJ, Dos Santos Silva I, Scott C, Tamimi RM, Lindstrom S, Kraft P, Hazra A, Li J, Eriksson L, Czene K, Hall P, Jensen M, Cunningham J, Olson JE, Purrington K, Couch FJ, Brown J, Leyland J, Warren RML, Luben RN, Khaw KT, Smith P, Wareham NJ, Jud SM, Heusinger K, Beckmann MW, Douglas JA, Shah KP, Chan HP, Helvie MA, Le Marchand L, Kolonel LN, Woolcott C, Maskarinec G, Haiman C, Giles GG, Baglietto L, Krishnan K, Southey MC, Apicella C, Andrulis IL, Knight JA, Ursin G, Alnaes GIG, Kristensen VN, Borresen-Dale AL, Gram IT, Bolla MK, Wang Q, Michailidou K, Dennis J, Simard J, Pharoah P, Dunning AM, Easton DF, Fasching PA, Pankratz VS, Hopper JL, Vachon CM. Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures. Cancer Res 2015; 75:2457-67. [PMID: 25862352 PMCID: PMC4470785 DOI: 10.1158/0008-5472.can-14-2012] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2014] [Accepted: 03/10/2015] [Indexed: 12/30/2022]
Abstract
Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.
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Affiliation(s)
- Jennifer Stone
- Centre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Western Australia, Australia
| | - Deborah J Thompson
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Isabel Dos Santos Silva
- Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Christopher Scott
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Rulla M Tamimi
- Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Sara Lindstrom
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, Massachusetts. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Aditi Hazra
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Jingmei Li
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. Human Genetics, Genome Institute of Singapore, Singapore, Singapore
| | - Louise Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Matt Jensen
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Julie Cunningham
- Department of Laboratory Medicine and Pathology, Division of Experimental Pathology, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - Janet E Olson
- Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
| | - Kristen Purrington
- Department of Oncology, Wayne State University School of Medicine and Karmanos Cancer Institute, Detroit, Michigan
| | - Fergus J Couch
- Department of Laboratory Medicine and Pathology, Division of Experimental Pathology, Mayo Clinic College of Medicine, Rochester, Minnesota. Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
| | - Judith Brown
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Jean Leyland
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Ruth M L Warren
- Department of Radiology, University of Cambridge, Addenbrooke's NHS Foundation Trust, Cambridge, United Kingdom
| | - Robert N Luben
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Kay-Tee Khaw
- MRC Centre for Nutritional Epidemiology in Cancer Prevention and Survival (CNC), University of Cambridge, Cambridge, United Kingdom
| | - Paula Smith
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Sebastian M Jud
- University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-Nuremberg, Erlangen-Nuremberg, Germany
| | - Katharina Heusinger
- University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-Nuremberg, Erlangen-Nuremberg, Germany
| | - Matthias W Beckmann
- University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-Nuremberg, Erlangen-Nuremberg, Germany
| | - Julie A Douglas
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan
| | - Kaanan P Shah
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, Michigan
| | - Heang-Ping Chan
- Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan
| | - Mark A Helvie
- Department of Radiology, University of Michigan Medical School, Ann Arbor, Michigan
| | | | | | - Christy Woolcott
- Department of Obstetrics and Genecology, IWK Health Centre, Halifax, Canada
| | | | - Christopher Haiman
- Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Graham G Giles
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Laura Baglietto
- Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia. Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia. Centre for Research in Epidemiology and Population Health, Gustave Roussy Institute, Villejuif Cedex, France. Paris-South University, Villejuif, France
| | - Kavitha Krishnan
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Melissa C Southey
- Department of Pathology, University of Melbourne, Melbourne, Australia
| | - Carmel Apicella
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Irene L Andrulis
- Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Julia A Knight
- Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada. Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Giske Ursin
- Institute of Basic Medical Sciences, University of Oslo, Norway. Department of Preventive Medicine, University of Southern California, California
| | - Grethe I Grenaker Alnaes
- Department of Genetics, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, Oslo, Norway
| | - Vessela N Kristensen
- Department of Genetics, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, Oslo, Norway
| | - Anne-Lise Borresen-Dale
- Department of Genetics, Institute for Cancer Research, The Norwegian Radium Hospital, Montebello, Oslo, Norway
| | - Inger Torhild Gram
- Faculty of Health Sciences, Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Qin Wang
- Faculty of Health Sciences, Department of Community Medicine, UiT The Arctic University of Norway, Tromsø, Norway
| | - Kyriaki Michailidou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Jacques Simard
- Centre Hospitalier Universitaire de Québec Research Center and Laval University, Quebec, Canada
| | - Paul Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Peter A Fasching
- University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-Nuremberg, Erlangen-Nuremberg, Germany. Department of Medicine, Division of Hematology and Oncology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California
| | - V Shane Pankratz
- Department of Health Sciences Research, Division of Biostatistics, Mayo Clinic College of Medicine, Rochester, Minnesota
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Celine M Vachon
- Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota.
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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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18
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Winkel RR, von Euler-Chelpin M, Nielsen M, Diao P, Nielsen MB, Uldall WY, Vejborg I. Inter-observer agreement according to three methods of evaluating mammographic density and parenchymal pattern in a case control study: impact on relative risk of breast cancer. BMC Cancer 2015; 15:274. [PMID: 25884160 PMCID: PMC4397728 DOI: 10.1186/s12885-015-1256-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2014] [Accepted: 03/25/2015] [Indexed: 01/09/2023] Open
Abstract
Background Mammographic breast density and parenchymal patterns are well-established risk factors for breast cancer. We aimed to report inter-observer agreement on three different subjective ways of assessing mammographic density and parenchymal pattern, and secondarily to examine what potential impact reproducibility has on relative risk estimates of breast cancer. Methods This retrospective case–control study included 122 cases and 262 age- and time matched controls (765 breasts) based on a 2007 screening cohort of 14,736 women with negative screening mammograms from Bispebjerg Hospital, Copenhagen. Digitised randomized film-based mammograms were classified independently by two readers according to two radiological visual classifications (BI-RADS and Tabár) and a computerized interactive threshold technique measuring area-based percent mammographic density (denoted PMD). Kappa statistics, Intraclass Correlation Coefficient (ICC) (equivalent to weighted kappa), Pearson’s linear correlation coefficient and limits-of-agreement analysis were used to evaluate inter-observer agreement. High/low-risk agreement was also determined by defining the following categories as high-risk: BI-RADS’s D3 and D4, Tabár’s PIV and PV and the upper two quartiles (within density range) of PMD. The relative risk of breast cancer was estimated using logistic regression to calculate odds ratios (ORs) adjusted for age, which were compared between the two readers. Results Substantial inter-observer agreement was seen for BI-RADS and Tabár (κ=0.68 and 0.64) and agreement was almost perfect when ICC was calculated for the ordinal BI-RADS scale (ICC=0.88) and the continuous PMD measure (ICC=0.93). The two readers judged 5% (PMD), 10% (Tabár) and 13% (BI-RADS) of the women to different high/low-risk categories, respectively. Inter-reader variability showed different impact on the relative risk of breast cancer estimated by the two readers on a multiple-category scale, however, not on a high/low-risk scale. Tabár’s pattern IV demonstrated the highest ORs of all density patterns investigated. Conclusions Our study shows the Tabár classification has comparable inter-observer reproducibility with well tested density methods, and confirms the association between Tabár’s PIV and breast cancer. In spite of comparable high inter-observer agreement for all three methods, impact on ORs for breast cancer seems to differ according to the density scale used. Automated computerized techniques are needed to fully overcome the impact of subjectivity.
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Affiliation(s)
- Rikke Rass Winkel
- Department of Radiology, University Hospital Copenhagen, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark.
| | - My von Euler-Chelpin
- Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, DK-1014, Copenhagen K, Denmark.
| | - Mads Nielsen
- Department of Computer Sciences, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark. .,Biomediq, Fruebjergvej 3, DK-2100, Copenhagen Ø, Denmark.
| | - Pengfei Diao
- Department of Computer Sciences, University of Copenhagen, Universitetsparken 5, DK-2100, Copenhagen Ø, Denmark.
| | - Michael Bachmann Nielsen
- Department of Radiology, University Hospital Copenhagen, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark.
| | - Wei Yao Uldall
- Department of Radiology, University Hospital Copenhagen, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark.
| | - Ilse Vejborg
- Department of Radiology, University Hospital Copenhagen, Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen Ø, Denmark.
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Martínez Gómez I, Casals el Busto M, Antón Guirao J, Ruiz Perales F, Llobet Azpitarte R. Estimación semiautomática de la densidad mamaria con DM-Scan. RADIOLOGIA 2014; 56:429-34. [DOI: 10.1016/j.rx.2012.11.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2012] [Revised: 11/20/2012] [Accepted: 11/22/2012] [Indexed: 10/27/2022]
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20
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Martínez Gómez I, Casals el Busto M, Antón Guirao J, Ruiz Perales F, Llobet Azpitarte R. Semiautomatic estimation of breast density with DM-Scan software. RADIOLOGIA 2014. [DOI: 10.1016/j.rxeng.2012.11.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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21
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Yochum L, Tamimi RM, Hankinson SE. Birthweight, early life body size and adult mammographic density: a review of epidemiologic studies. Cancer Causes Control 2014; 25:1247-59. [PMID: 25053404 DOI: 10.1007/s10552-014-0432-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2014] [Accepted: 07/01/2014] [Indexed: 01/09/2023]
Abstract
PURPOSE To evaluate the association between birth weight and early life body size with adult mammographic density in the peer-reviewed literature. METHODS A comprehensive literature search was conducted through January, 2014. English language articles that assessed adult mammographic density (MD) in relation to early life body size (≤18 years old), or birthweight were included. RESULTS Nine studies reported results for early life body size and %MD. Both exposure and outcome were assessed at different ages using multiple methods. In premenopausal women, findings were inconsistent; two studies reported significant, inverse associations, one reported a non-significant, inverse association, and two observed no association. Reasons for these inconsistencies were not obvious. In postmenopausal women, four of five studies supported an inverse association. Two of three studies that adjusted for menopausal status found significant, inverse associations. Birthweight and %MD was evaluated in nine studies. No association was seen in premenopausal women and two of three studies reported positive associations in postmenopausal women. Three of four studies that adjusted for menopausal status found no association. DISCUSSION Early life body size and birthweight appear unrelated to %MD in premenopausal women while an inverse association in postmenopausal women is more likely. Although based on limited data, birthweight and %MD appear positively associated in postmenopausal women. Given the small number of studies, the multiple methods of data collection and analysis, other methodologic issues, and lack of consistency in results, additional research is needed to clarify this complex association and develop a better understanding of the underlying biologic mechanisms.
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Affiliation(s)
- Laura Yochum
- University of Massachusetts Amherst, 426 Arnold House, 716 North Pleasant Street, Amherst, MA, 01003, USA,
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22
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Sohn G, Lee JW, Park SW, Park J, Woo J, Kim HJ, Shin HJ, Kim HH, Jung KH, Sung J, Lee SW, Son BH, Ahn SH. Reliability of the percent density in digital mammography with a semi-automated thresholding method. J Breast Cancer 2014; 17:174-9. [PMID: 25013440 PMCID: PMC4090321 DOI: 10.4048/jbc.2014.17.2.174] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2013] [Accepted: 03/17/2014] [Indexed: 11/30/2022] Open
Abstract
PURPOSE The reliability of the quantitative measurement of breast density with a semi-automated thresholding method (Cumulus™) has mainly been investigated with film mammograms. This study aimed to evaluate the intrarater reproducibility of percent density (PD) by Cumulus™ with digital mammograms. METHODS This study included 1,496 craniocaudal digital mammograms from the unaffected breast of breast cancer patients. One rater reviewed each mammogram and estimated the PD using the Cumulus™ method. All images were reassessed by the same rater 1 month later without reference to the previously assigned values. The repeatability of the PD was evaluated by an intraclass correlation coefficient (ICC). All patients were grouped based on their body mass index (BMI), age, family history of breast cancer, breastfeeding history and breast area (calculated with Cumulus™), and subgroup analysis for the ICC of each group was performed. All patients were categorized by their Breast Imaging Reporting and Data System (BI-RADS) density pattern, and the mean and standard deviation of the PD by each BI-RADS categories were compared. RESULTS The ICC for the PD was 0.94, indicating excellent repeatability. The discrepancy between the paired PD values ranged from 0 to 23.93, with an average of 3.90 (standard deviation=3.39). The subgroup ICCs for the PD ranged from 0.88 to 0.96, indicating excellent reliability in all subgroups regardless of patient variables. The ICCs of the PD for the high-risk (BI-RADS 3 and 4) and low-risk (BI-RADS 1 and 2) groups were 0.90 and 0.88, respectively. CONCLUSION This study suggests that PD calculated with digital mammograms has an acceptable reliability regardless of patient age, BMI, family history of breast cancer, breastfeeding history, breast size, and BI-RADS density pattern.
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Affiliation(s)
- Guiyun Sohn
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jong Won Lee
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sung Won Park
- Department of Radiology, Health Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jihoon Park
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jiyoung Woo
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hwa Jung Kim
- Department of Biostatistics and Clinical Epidemiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hak Hee Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Kyung Hae Jung
- Department of Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joohon Sung
- Department of Epidemiology, School of Public Health and Institution of Health and Environment, Seoul National University, Seoul, Korea
| | - Seung Wook Lee
- Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Byung Ho Son
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Sei-Hyun Ahn
- Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Breast density assessment using a 3T MRI system: comparison among different sequences. PLoS One 2014; 9:e99027. [PMID: 24892933 PMCID: PMC4044003 DOI: 10.1371/journal.pone.0099027] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Accepted: 05/09/2014] [Indexed: 11/23/2022] Open
Abstract
Purpose To compare MRI sequences for breast density measurements on a 3T MRI system using IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least squares estimation) as possible physiology-like reference. Materials and Methods MRI examination was performed in 48 consecutive patients (mean age 41, years; range, 35–67 years) on a 3.0T scanner and 46 were included. All (fertile) women, were examined between days 5 and 15 of their menstrual cycle. MRI protocol included: T1-turbo spin-echo (T1-tSE), T2-turbo spin-echo (T2-tSE), VIBRANT (Volume Imaging for Breast Assessment) before and after injection of contrast media and IDEAL. Breast density was calculated with semi-automated software. Statistical analysis was performed with non-parametric tests. Results Mean percentage of breast density calculated in each sequence was: T1-tSE = 56%; T2-tSE = 52%; IDEAL FatOnly = 55%; IDEAL WaterOnly = 53%, VIBRANT = 55%. Significant differences were observed between T2-tSE and both T1-tSE (p<0.001), VIBRANT sequences (p = 0.009), T1-tSE and both IDEAL WaterOnly (p = 0.007) and IDEAL FatOnly (p = 0.047). Breast density percentage showed a positive linear correlation among different sequences: r≥0.93. Conclusions Differences exist between MRI sequences used to assess breast density percentage. T1-weighted sequences values were similar to IDEAL sequences.
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Woolcott CG, Conroy SM, Nagata C, Ursin G, Vachon CM, Yaffe MJ, Pagano IS, Byrne C, Maskarinec G. Methods for assessing and representing mammographic density: an analysis of 4 case-control studies. Am J Epidemiol 2014; 179:236-44. [PMID: 24124193 DOI: 10.1093/aje/kwt238] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
To maximize statistical power in studies of mammographic density and breast cancer, it is advantageous to combine data from several studies, but standardization of the density assessment is desirable. Using data from 4 case-control studies, we describe the process of reassessment and the resulting correlation between values, identify predictors of differences in density readings, and evaluate the strength of the association between mammographic density and breast cancer risk using different representations of density values. The pooled analysis included 1,699 cases and 2,422 controls from California (1990-1998), Hawaii (1996-2003), Minnesota (1992-2001), and Japan (1999-2003). In 2010, a single reader reassessed all images for mammographic density using Cumulus software (Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada). The mean difference between original and reassessed percent density values was -0.7% (95% confidence interval: -1.1, -0.3), with a correlation of 0.82 that varied by location (r = 0.80-0.89). Case status, weight status, age, parity, density assessment method, mammogram view, and race/ethnicity were significant determinants of the difference between original and reassessed values; in combination, these factors explained 9.2% of the variation. The associations of mammographic density with breast cancer and the model fits were similar using the original values and the reassessed values but were slightly strengthened when a calibrated value based on 100 reassessed radiographs was used.
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25
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Tagliafico AS, Tagliafico G, Cavagnetto F, Calabrese M, Houssami N. Estimation of percentage breast tissue density: comparison between digital mammography (2D full field digital mammography) and digital breast tomosynthesis according to different BI-RADS categories. Br J Radiol 2013; 86:20130255. [PMID: 24029631 DOI: 10.1259/bjr.20130255] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To compare breast density estimated from two-dimensional full-field digital mammography (2D FFDM) and from digital breast tomosynthesis (DBT) according to different Breast Imaging-Reporting and Data System (BI-RADS) categories, using automated software. METHODS Institutional review board approval and written informed patient consent were obtained. DBT and 2D FFDM were performed in the same patients to allow within-patient comparison. A total of 160 consecutive patients (mean age: 50±14 years; mean body mass index: 22±3) were included to create paired data sets of 40 patients for each BI-RADS category. Automatic software (MedDensity(©), developed by Giulio Tagliafico) was used to compare the percentage breast density between DBT and 2D FFDM. The estimated breast percentage density obtained using DBT and 2D FFDM was examined for correlation with the radiologists' visual BI-RADS density classification. RESULTS The 2D FFDM differed from DBT by 16.0% in BI-RADS Category 1, by 11.9% in Category 2, by 3.5% in Category 3 and by 18.1% in Category 4. These differences were highly significant (p<0.0001). There was a good correlation between the BI-RADS categories and the density evaluated using 2D FFDM and DBT (r=0.56, p<0.01 and r=0.48, p<0.01, respectively). CONCLUSION Using DBT, breast density values were lower than those obtained using 2D FFDM, with a non-linear relationship across the BI-RADS categories. These data are relevant for clinical practice and research studies using density in determining the risk. ADVANCES IN KNOWLEDGE On DBT, breast density values were lower than with 2D FFDM, with a non-linear relationship across the classical BI-RADS categories.
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Affiliation(s)
- A S Tagliafico
- Department of Experimental Medicine, University of Genova, Genova, Italy
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26
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Trocchi P, Ursin G, Kuss O, Ruschke K, Schmidt-Pokrzywniak A, Holzhausen HJ, Löning T, Thomssen C, Böcker W, Kluttig A, Stang A. Mammographic density and inter-observer variability of pathologic evaluation of core biopsies among women with mammographic abnormalities. BMC Cancer 2012; 12:554. [PMID: 23176326 PMCID: PMC3529189 DOI: 10.1186/1471-2407-12-554] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2012] [Accepted: 11/21/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND As high percentage of mammographic densities complicates the assessment of imaging findings, mammographic density may influence the histopathological evaluation of core-biopsies of the breast. We measured the influence of mammographic density on the inter-observer variability of histopathological findings of breast biopsies. METHODS Histological slides of 695 women who underwent core biopsies of the breast at University of Halle between 2006 and 2008 were evaluated in a blinded fashion by two pathologists using the five levels of the B-categorization scheme (B1-B5). To quantify mammographic density, we used a computer-based threshold method (Madena). We calculated observed and chance-corrected agreements (weighted kappa) and 95% confidence intervals (95% CI) according to four categories of mammographic density (<10%, 10<25%, 25<50%, ≥50%). RESULTS The weighted kappa decreased monotonically from 89.6% (95% CI: 85.8%, 93.3%) among women with less than 10% of mammographic density to 80.4% (95% CI: 69.9%, 90.9%) for women with more than 50% of mammographic density, respectively. Results of a kappa regression analysis showed that agreement of pathologists on clinically relevant categories (B1-B2 versus B3-B5) decreased with mammographic density. CONCLUSIONS Mammographic density is a relevant modifier of the agreement between pathologists who assess breast biopsies using the B-categorization scheme. The influence of mammographic density on the inter-observer variability can be explained to some extent by varying prevalences of histological entities across B categories that have typically different inter-observer agreement. Women with high mammographic density are at higher risk of inter-observer variability compared to women with low mammographic density and should possibly undergo a second pathology review.
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Affiliation(s)
- Pietro Trocchi
- Institute of Clinical Epidemiology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Magdeburger Str. 8, Halle (Saale), 06097, Germany
| | - Giske Ursin
- Cancer Registry of Norway, Postboks 5313 Majorstuen, Oslo, 0304, Norway
- Department of Nutrition, University of Oslo, Oslo, Norway
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Oliver Kuss
- Institute of Medical Epidemiology, Biometry and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Kathrin Ruschke
- Department of Radiology, Martin-Luther-University Halle-Wittenberg, Ernst-Grube-Str. 40, Halle (Saale), 06097, Germany
| | - Andrea Schmidt-Pokrzywniak
- Institute of Clinical Epidemiology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Magdeburger Str. 8, Halle (Saale), 06097, Germany
| | - Hans-Jürgen Holzhausen
- Institute of Pathology, Martin-Luther-University Halle-Wittenberg, Magdeburger Str. 14, Halle (Saale), 06097, Germany
| | - Thomas Löning
- Albertinen-Pathology Hamburg, Fangdieckstr. 75 a, Hamburg, 22547, Germany
| | - Christoph Thomssen
- Department of Gynaecology, Martin-Luther-University Halle-Wittenberg, Ernst-Grube-Str. 40, Halle (Saale), 06097, Germany
| | - Werner Böcker
- Reference Center for Gynaeco- and Mammapathology, Fangdieckstr. 75 a, Hamburg, 22547, Germany
| | - Alexander Kluttig
- Institute of Medical Epidemiology, Biometry and Informatics, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany
| | - Andreas Stang
- Institute of Clinical Epidemiology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Magdeburger Str. 8, Halle (Saale), 06097, Germany
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Jun JK, Kim MJ, Choi KS, Suh M, Jung KW. Development of a sampling strategy and sample size calculation to estimate the distribution of mammographic breast density in Korean women. Asian Pac J Cancer Prev 2012; 13:4661-4. [PMID: 23167398 DOI: 10.7314/apjcp.2012.13.9.4661] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Mammographic breast density is a known risk factor for breast cancer. To conduct a survey to estimate the distribution of mammographic breast density in Korean women, appropriate sampling strategies for representative and efficient sampling design were evaluated through simulation. Using the target population from the National Cancer Screening Programme (NCSP) for breast cancer in 2009, we verified the distribution estimate by repeating the simulation 1,000 times using stratified random sampling to investigate the distribution of breast density of 1,340,362 women. According to the simulation results, using a sampling design stratifying the nation into three groups (metropolitan, urban, and rural), with a total sample size of 4,000, we estimated the distribution of breast density in Korean women at a level of 0.01% tolerance. Based on the results of our study, a nationwide survey for estimating the distribution of mammographic breast density among Korean women can be conducted efficiently.
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Affiliation(s)
- Jae Kwan Jun
- National Cancer Control Institute, National Cancer Center, Goyang, Korea.
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Lu LJW, Nishino TK, Johnson RF, Nayeem F, Brunder DG, Ju H, Leonard MH, Grady JJ, Khamapirad T. Comparison of breast tissue measurements using magnetic resonance imaging, digital mammography and a mathematical algorithm. Phys Med Biol 2012; 57:6903-27. [PMID: 23044556 DOI: 10.1088/0031-9155/57/21/6903] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Women with mostly mammographically dense fibroglandular tissue (breast density, BD) have a four- to six-fold increased risk for breast cancer compared to women with little BD. BD is most frequently estimated from two-dimensional (2D) views of mammograms by a histogram segmentation approach (HSM) and more recently by a mathematical algorithm consisting of mammographic imaging parameters (MATH). Two non-invasive clinical magnetic resonance imaging (MRI) protocols: 3D gradient-echo (3DGRE) and short tau inversion recovery (STIR) were modified for 3D volumetric reconstruction of the breast for measuring fatty and fibroglandular tissue volumes by a Gaussian-distribution curve-fitting algorithm. Replicate breast exams (N = 2 to 7 replicates in six women) by 3DGRE and STIR were highly reproducible for all tissue-volume estimates (coefficients of variation <5%). Reliability studies compared measurements from four methods, 3DGRE, STIR, HSM, and MATH (N = 95 women) by linear regression and intra-class correlation (ICC) analyses. Rsqr, regression slopes, and ICC, respectively, were (1) 0.76-0.86, 0.8-1.1, and 0.87-0.92 for %-gland tissue, (2) 0.72-0.82, 0.64-0.96, and 0.77-0.91, for glandular volume, (3) 0.87-0.98, 0.94-1.07, and 0.89-0.99, for fat volume, and (4) 0.89-0.98, 0.94-1.00, and 0.89-0.98, for total breast volume. For all values estimated, the correlation was stronger for comparisons between the two MRI than between each MRI versus mammography, and between each MRI versus MATH data than between each MRI versus HSM data. All ICC values were >0.75 indicating that all four methods were reliable for measuring BD and that the mathematical algorithm and the two complimentary non-invasive MRI protocols could objectively and reliably estimate different types of breast tissues.
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Affiliation(s)
- Lee-Jane W Lu
- Department of Preventative Medicine and Community Health, The University of Texas Medical Branch, Galveston, TX 77555-1109, USA
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Tagliafico A, Tagliafico G, Astengo D, Cavagnetto F, Rosasco R, Rescinito G, Monetti F, Calabrese M. Mammographic density estimation: one-to-one comparison of digital mammography and digital breast tomosynthesis using fully automated software. Eur Radiol 2012; 22:1265-70. [DOI: 10.1007/s00330-012-2380-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2011] [Revised: 11/18/2011] [Accepted: 12/07/2011] [Indexed: 10/28/2022]
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Do the majority of Malaysian women have dense breasts on mammogram? Biomed Imaging Interv J 2011; 7:e14. [PMID: 22291859 PMCID: PMC3265152 DOI: 10.2349/biij.7.2.e14] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2010] [Revised: 01/06/2011] [Accepted: 01/09/2011] [Indexed: 11/21/2022] Open
Abstract
Purpose: To determine: (i) the mammographic parenchymal patterns in Malaysian women and whether the breasts are dense on mammogram; (ii) the effect of age on breast density; (iii) the effect of parity on breast density; (iv) the difference in breast parenchymal patterns among the major races of women in Malaysia. Methods: This was a descriptive cross-sectional study of 1,784 patients (981 Malays, 571 Chinese, 214 Indians and 18 others) who had undergone mammography during the 1-year study period. Majority of women (41.7%) were aged between 51 and 60 years and majority (43%) had 3–4 children. The Tabar classification (Pattern I - V) was used to evaluate breast parenchymal patterns on mammogram. Tabar Pattern I was further divided into 3 sub-groups (Pattern IA, IB, and IC). The different patterns were then grouped into dense (IB, IC, IV, V) and not dense (IA, II, III) breasts. The SPSS package was used for statistical analysis. Results: Majority (59%) of Malaysian women had dense breasts (Pattern IB 29%, IC 20%, IV 5%, and V 5%) and 41% did not have dense breasts (Pattern IA 28%, II 6%, and III 7%). Age and parity were inversely related to breast density (p < 0.0001). Chinese women (65.7%) had the highest percentage of dense breasts (p = 0.69, odds ratio = 1.22), followed by the Indians (57.2%) and the Malays (50.5%). Conclusion: Majority of women had dense breasts but Pattern IV, which has been associated with increased risk of breast cancer, was seen in only 5% of the women. The breast density reduced steadily with increasing age and parity. There was no statistically significant difference in breast density in the three main races.
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Jeon JH, Kang JH, Kim Y, Lee HY, Choi KS, Jun JK, Oh DK, Lee CY, Ko K, Park EC. Reproductive and Hormonal Factors Associated with Fatty or Dense Breast Patterns among Korean Women. Cancer Res Treat 2011; 43:42-8. [PMID: 21509162 PMCID: PMC3072534 DOI: 10.4143/crt.2011.43.1.42] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2010] [Accepted: 05/19/2010] [Indexed: 11/24/2022] Open
Abstract
Purpose Dense breasts have been suggested as a risk factor for breast cancer, but controversy still remains. This study evaluates the association of reproductive and hormonal factors with dense breasts among Korean women. Materials and Methods Using a cross-sectional design, 516 women were recruited and classified for breast density patterns as being either fatty or dense, using the Breast Imaging Reporting and Data System (BI-RADS) of the American College of Radiology. Univariate and multivariate logistic regression models were used for statistical analysis. Results In univariate logistic regression, older age, higher body mass index, older age at menarche, and oral contraceptive use were associated with more fatty breasts. On the contrary, longer duration of education, alcohol consumption, lower parity, menopause and use of hormone replacement therapy were associated with dense breasts. After adjustment, age and body mass index were inversely associated with breast density (p-value for trend <0.01, respectively), whereas nulliparous and premenopausal status were positively associated. Compared to women who had ≥2 children, nulliparous women had an 11.8-fold increase of dense breasts (p-value for trend <0.01). Compared to postmenopausal women, premenopausal women had 2.4-fold increase of dense breasts (odds ratio, 2.42; 95% confidence interval, 1.36 to 4.32). Conclusion Young age, lower body mass index, lower parity, and premenopausal status were significantly associated with dense breasts in Korea.
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Affiliation(s)
- Jei-Hun Jeon
- Department of Medicine, Yonsei University College of Medicine, Seoul, Korea
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Marmara EA, Papacharalambous XN, Kouloulias VE, Maridaki DM, Baltopoulos JP. Physical activity and mammographic parenchymal patterns among Greek postmenopausal women. Maturitas 2011; 69:74-80. [PMID: 21377300 DOI: 10.1016/j.maturitas.2011.02.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2010] [Revised: 01/28/2011] [Accepted: 02/02/2011] [Indexed: 11/26/2022]
Abstract
OBJECTIVE To examine whether physical activity during the last five years is related to later breast mammographic density in postmenopausal Greek women. METHODS We designed a cross-sectional study in 724 women, of ages 45-67 years. An interview-administered questionnaire was used to obtain information on duration and intensity of recreational physical activity during five years preceding study recruitment. Mammograms were evaluated according to BIRADS classification and BIRADS score was also estimated. Multivariate ordinal logistic regression analysis was used to assess associations between physical activity index and breast density according to the BIRADS classification methods. RESULTS We observed a statistically significant inverse association of mammographic breast density measured by the BIRADS classification method and recreational exercise (OR=-0.10; 95% CI -0.018, -0.001; p=0.022). For one unit increase in physical activity as expressed by the MET-h/week score, the odds of lower versus higher breast density categories are 1.105 greater, given that all of the other variables in the model are held constant. A modifying effect by age at recruitment was evident among participants, with a stronger inverse association between recreational activity and mammographic breast density among older women (OR=-0.036; 95% CI -0.063, -0.009; p=0.009). An inverse association between physical activity and BIRADS score was evident, not reaching statistical significance (OR=0.00; 95% CI -0.009, 0.008; p=0.887). CONCLUSIONS Mammographic breast area was lower in postmenopausal women who participated in sports/recreational physical activity compared to inactive controls. Increasing physical activity levels among postmenopausal women might be a reasonable approach to reduce mammographic density. However, until more physical activity and mammographic breast density studies are conducted that confirm our findings, they have to be interpreted with caution, due to the retrospective nature of our data and the possibility of memory bias.
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Affiliation(s)
- Eleni A Marmara
- Division of Sports Medicine and Biology of Exercise, Laboratory of Functional Anatomy, TEFAA University of Athens, Dafni, Greece.
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Factors That Influence Changes in Mammographic Density With Postmenopausal Hormone Therapy. Taiwan J Obstet Gynecol 2010; 49:413-8. [DOI: 10.1016/s1028-4559(10)60091-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/24/2009] [Indexed: 11/21/2022] Open
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Maskarinec G, Morimoto Y, Daida Y, Laidevant A, Malkov S, Shepherd JA, Novotny R. Comparison of breast density measured by dual energy X-ray absorptiometry with mammographic density among adult women in Hawaii. Cancer Epidemiol 2010; 35:188-93. [PMID: 20688593 DOI: 10.1016/j.canep.2010.06.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2010] [Revised: 06/11/2010] [Accepted: 06/20/2010] [Indexed: 11/28/2022]
Abstract
BACKGROUND While use of mammography is limited, due to concerns related to radiation exposure, dual energy X-ray absorptiometry (DXA), commonly available in medical care settings, is characterized by low radiation exposure. METHODS In the current paper, we compared breast density measured by DXA with mammographic density in 101 adult women who had a screening mammogram during the last 2 years. DXA scans of both breasts were taken using a clinical DXA system calibrated to measure breast density. The total projected breast area was manually delineated on each image and percent fibroglandular volume density (%FGV), absolute fibroglandular volume, total breast area and volume were computed. After digitizing mammographic films, total breast area, dense area, and percent density (PD) were estimated using computer-assisted mammographic density assessment. RESULTS Both DXA and mammographic measures showed high correlations between left and right breasts ranging from 0.85 to 0.98 (p<0.0001). Mean %FGV was 38.8±14.3%, and mean percent density was 31.9±18.2% for craniocaudal views and 28.3±16.2% for mediolateral views. The correlation between the two measures was 0.76 for both views (p<0.0001). Associations with common risk factors showed similar patterns for DXA and mammographic densities; in particular, the inverse associations with BMI and age at menarche were evident for both methods. Multilinear regression with stepwise selection indicated an explained variance of 0.56 for %FGV alone and of 0.58 for %FGV plus number of children. CONCLUSION Despite some differences in methodology, the current comparison suggests that DXA may provide a low-radiation option in evaluating breast density.
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Affiliation(s)
- Gertraud Maskarinec
- Cancer Research Center of Hawaii, 1236 Lauhala St., Honolulu, HI 96813, USA.
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Berge EO, Knappskog S, Geisler S, Staalesen V, Pacal M, Børresen-Dale AL, Puntervoll P, Lillehaug JR, Lønning PE. Identification and characterization of retinoblastoma gene mutations disturbing apoptosis in human breast cancers. Mol Cancer 2010; 9:173. [PMID: 20594292 PMCID: PMC2908580 DOI: 10.1186/1476-4598-9-173] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2010] [Accepted: 07/01/2010] [Indexed: 12/29/2022] Open
Abstract
Background The tumor suppressor pRb plays a key role regulating cell cycle arrest, and disturbances in the RB1 gene have been reported in different cancer forms. However, the literature reports contradictory findings with respect to a pro - versus anti - apoptotic role of pRb, and the consequence of alterations in RB1 to chemotherapy sensitivity remains unclear. This study is part of a project investigating alterations in pivotal genes as predictive factors to chemotherapy sensitivity in breast cancer. Results Analyzing 73 locally advanced (stage III) breast cancers, we identified two somatic and one germline single nucleotide changes, each leading to amino acid substitution in the pRb protein (Leu607Ile, Arg698Trp, and Arg621Cys, respectively). This is the first study reporting point mutations affecting RB1 in breast cancer tissue. In addition, MLPA analysis revealed two large multiexon deletions (exons 13 to 27 and exons 21 to 23) with the exons 21-23 deletion occurring in the tumor also harboring the Leu607Ile mutation. Interestingly, Leu607Ile and Arg621Cys point mutations both localize to the spacer region of the pRb protein, a region previously shown to harbor somatic and germline mutations. Multiple sequence alignment across species indicates the spacer to be evolutionary conserved. All three RB1 point mutations encoded nuclear proteins with impaired ability to induce apoptosis compared to wild-type pRb in vitro. Notably, three out of four tumors harboring RB1 mutations displayed primary resistance to treatment with either 5-FU/mitomycin or doxorubicin while only 14 out of 64 tumors without mutations were resistant (p = 0.046). Conclusions Although rare, our findings suggest RB1 mutations to be of pathological importance potentially affecting sensitivity to mitomycin/anthracycline treatment in breast cancer.
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Cox B, Ballard-Barbash R, Broeders M, Dowling E, Malila N, Shumak R, Taplin S, Buist D, Miglioretti D. Recording of hormone therapy and breast density in breast screening programs: summary and recommendations of the International Cancer Screening Network. Breast Cancer Res Treat 2010; 124:793-800. [PMID: 20414718 DOI: 10.1007/s10549-010-0893-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2010] [Accepted: 04/07/2010] [Indexed: 11/30/2022]
Abstract
Breast density and the use of hormone therapy (HT) for menopausal symptoms alter the risk of breast cancer and both factors influence screening mammography performance. The International Cancer Screening Network (ICSN) surveyed its 29 member countries and found that few programs record breast density or the use of HT among screening participants. This may affect the ability of programs to assess their effectiveness in reducing breast cancer mortality. Seven countries recorded the use of HT at screening, and some were able to link screening records to individual prescribing records of HT. Eight countries reported recording breast density at screening mammography for some or all women screened. The recommendations of the ICSN for recording information about breast density and HT are presented.
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Affiliation(s)
- Brian Cox
- Hugh Adam Cancer Epidemiology Unit, Department of Preventive and Social Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand.
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Biong M, Gram IT, Brill I, Johansen F, Solvang HK, Alnaes GIG, Fagerheim T, Bremnes Y, Chanock SJ, Burdett L, Yeager M, Ursin G, Kristensen VN. Genotypes and haplotypes in the insulin-like growth factors, their receptors and binding proteins in relation to plasma metabolic levels and mammographic density. BMC Med Genomics 2010; 3:9. [PMID: 20302654 PMCID: PMC2853484 DOI: 10.1186/1755-8794-3-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2009] [Accepted: 03/19/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Increased mammographic density is one of the strongest independent risk factors for breast cancer. It is believed that one third of breast cancers are derived from breasts with more than 50% density. Mammographic density is affected by age, BMI, parity, and genetic predisposition. It is also greatly influenced by hormonal and growth factor changes in a woman's life cycle, spanning from puberty through adult to menopause. Genetic variations in genes coding for hormones and growth factors involved in development of the breast are therefore of great interest. The associations between genetic polymorphisms in genes from the IGF pathway on mammographic density and circulating levels of IGF1, its binding protein IGFBP3, and their ratio in postmenopausal women are reported here. METHODS Samples from 964 postmenopausal Norwegian women aged 55-71 years were collected as a part of the Tromsø Mammography and Breast Cancer Study. All samples were genotyped for 25 SNPs in IGF1, IGF2, IGF1R, IGF2R, IGFALS and IGFBP3 using Taqman (ABI). The main statistical analyses were conducted with the PROC HAPLOTYPE procedure within SAS/GENETICS (SAS 9.1.3). RESULTS The haplotype analysis revealed six haploblocks within the studied genes. Of those, four had significant associations with circulating levels of IGF1 or IGFBP3 and/or mammographic density. One haplotype variant in the IGF1 gene was found to be associated with mammographic density. Within the IGF2 gene one haplotype variant was associated with levels of both IGF1 and IGFBP3. Two haplotype variants in the IGF2R were associated with the level of IGF1. Both variants of the IGFBP3 haplotype were associated with the IGFBP3 level and indicate regulation in cis. CONCLUSION Polymorphisms within the IGF1 gene and related genes were associated with plasma levels of IGF1, IGFBP3 and mammographic density in this study of postmenopausal women.
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Affiliation(s)
- Margarethe Biong
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, Montebello 0310, Oslo, Norway
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Can mammographic assessments lead to consider density as a risk factor for breast cancer? Eur J Radiol 2010; 82:404-11. [PMID: 20133095 DOI: 10.1016/j.ejrad.2010.01.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2009] [Revised: 01/09/2010] [Accepted: 01/11/2010] [Indexed: 11/22/2022]
Abstract
Admitting that mammographic breast density is an important independent risk factor for breast cancer in the general population, has a crucial economical health care impact, since it might lead to increasing screening frequency or reinforcing additional modalities. Thus, the impact of density as a risk factor has to be carefully investigated and might be debated. Some authors suggested that high density would be either a weak factor or confused with a masking effect. Others concluded that most of the studies have methodological biases in basic physics to quantify percentage of breast density, as well as in mammographic acquisition parameters. The purpose of this review is to evaluate mammographic procedures and density assessments in published studies regarding density as a breast cancer risk. No standardization was found in breast density assessments and compared density categories. High density definitions varied widely from 25 to 75% of dense tissues on mammograms. Some studies showed an insufficient follow-up to reveal masking effect related to mammographic false negatives. Evaluating breast density impact needs thorough studies with consensual mammographic procedures, methods of density measurement, breast density classification as well as a standardized definition of high breast density. Digital mammography, more effective in dense breasts, should help to re-evaluate the issue of density as a risk factor for breast cancer.
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Maskarinec G, Verheus M, Tice JA. Epidemiologic studies of isoflavones & mammographic density. Nutrients 2010; 2:35-48. [PMID: 22253990 PMCID: PMC3257610 DOI: 10.3390/nu2010035] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2009] [Accepted: 01/15/2010] [Indexed: 12/28/2022] Open
Abstract
Isoflavones, phytoestrogens in soy beans with estrogen-like properties, have been examined for their cancer protective effects. Mammographic density is a strong predictor of breast cancer. This review summarizes studies that have examined the association between isoflavones and breast density. Observational investigations in Hawaii and Singapore suggest slightly lower breast density among women of Asian descent with regular soy intake, but two larger studies from Japan and Singapore did not observe a protective effect. The findings from seven randomized trials with primarily Caucasian women indicate that soy or isoflavones do not modify mammographic density. Soy foods and isoflavone supplements within a nutritional range do not appear to modify breast cancer risk as assessed by mammographic density.
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Affiliation(s)
- Gertraud Maskarinec
- Cancer Research Center of Hawaii, 1236 Lauhala Street, Honolulu, HI 96813, USA;
| | - Martijn Verheus
- Cancer Research Center of Hawaii, 1236 Lauhala Street, Honolulu, HI 96813, USA;
| | - Jeffrey A. Tice
- Department of Medicine, University of California, San Francisco, 1701 Divisadero Street, San Francisco, CA 94143, USA;
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Bakic PR, Carton AK, Kontos D, Zhang C, Troxel AB, Maidment ADA. Breast percent density: estimation on digital mammograms and central tomosynthesis projections. Radiology 2009; 252:40-9. [PMID: 19420321 DOI: 10.1148/radiol.2521081621] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE To evaluate inter- and intrareader agreement in breast percent density (PD) estimation on clinical digital mammograms and central digital breast tomosynthesis (DBT) projection images. MATERIALS AND METHODS This HIPAA-compliant study had institutional review board approval; all patients provided informed consent. Breast PD estimation was performed on the basis of anonymized digital mammograms and central DBT projections in 39 women (mean age, 51 years; range, 31-80 years). All women had recently detected abnormalities or biopsy-proved cancers. PD was estimated by three experienced readers on the mediolateral oblique views of the contralateral breasts by using software; each reader repeated the estimation after 2 months. Spearman correlations of inter- and intrareader and intermodality PD estimates, as well as kappa statistics between categoric PD estimates, were computed. RESULTS High correlation (rho = 0.91) was observed between PD estimates on digital mammograms and those on central DBT projections, averaged over all estimations; the corresponding kappa coefficient (0.79) indicated substantial agreement. Mean interreader agreement for PD estimation on central DBT projections (rho = 0.85 +/- 0.05 [standard deviation]) was significantly higher (P < .01) than that for PD estimation on digital mammograms (rho = 0.75 +/- 0.05); the corresponding kappa coefficients indicated substantial (kappa = 0.65 +/- 0.12) and moderate (kappa = 0.55 +/- 0.14) agreement for central DBT projections and digital mammograms, respectively. CONCLUSION High correlation between PD estimates on digital mammograms and those on central DBT projections suggests the latter could be used until a method for PD estimation based on three-dimensional reconstructed images is introduced. Moreover, clinical PD estimation is possible with reduced radiation dose, as each DBT projection was acquired by using about 22% of the dose for a single mammographic projection.
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Affiliation(s)
- Predrag R Bakic
- Department of Radiology, University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104, USA.
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Masala G, Assedi M, Ambrogetti D, Sera F, Salvini S, Bendinelli B, Ermini I, Giorgi D, Rosselli del Turco M, Palli D. Physical activity and mammographic breast density in a Mediterranean population: the EPIC Florence longitudinal study. Int J Cancer 2009; 124:1654-61. [PMID: 19085933 DOI: 10.1002/ijc.24099] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
A protective effect of physical activity (PA) on breast cancer (BC) risk has been suggested. Few studies have examined the influence of PA on mammographic breast density (MBD), a strong risk factor for BC. In a prospective study in Florence, Italy, we identified 2,000 healthy women with a mammogram taken 5 years after enrollment. Individual mammograms were retrieved (83%) and MBD assessed according to Wolfe's classification. Detailed information on PA at work and during leisure time, reproductive history, lifestyle and anthropometric measurements at enrollment were available for 1,666 women. Information on hormone replacement therapy (HRT) was also obtained at mammogram. Women with high-MBD (P2 + DY Wolfe's patterns) were compared with women with low-MBD (N1 + P1) by multivariate logistic models. Overall, high-MBD was inversely associated with increasing levels of leisure time PA (p for trend = 0.04) and among peri-/postmenopausal women, also with increasing levels of recreational activities (p for trend = 0.02). An interaction between PA and HRT emerged, with a stronger inverse association of highest level of recreational activity with MBD among HRT nonusers (p for interaction = 0.02). A modifying effect by body mass index (BMI) was evident among 1,025 peri-/postmenopausal women who did not use HRT at the time of mammogram, with a stronger inverse association between recreational PA and MBD in the highest BMI tertile (OR = 0.34; 95% CI 0.20-0.57; p for interaction = 0.03). This large study carried out in Mediterranean women suggests that leisure time PA may play a role in modulating MBD, particularly in overweight/obese peri-/postmenopausal women.
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Affiliation(s)
- Giovanna Masala
- Molecular and Nutritional Epidemiology Unit, Cancer Research and Prevention Institute, Florence, Italy
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Mammographic density estimation: Comparison among BI-RADS categories, a semi-automated software and a fully automated one. Breast 2009; 18:35-40. [DOI: 10.1016/j.breast.2008.09.005] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2008] [Revised: 09/25/2008] [Accepted: 09/26/2008] [Indexed: 01/11/2023] Open
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Ding J, Warren R, Warsi I, Day N, Thompson D, Brady M, Tromans C, Highnam R, Easton D. Evaluating the effectiveness of using standard mammogram form to predict breast cancer risk: case-control study. Cancer Epidemiol Biomarkers Prev 2008; 17:1074-81. [PMID: 18483328 DOI: 10.1158/1055-9965.epi-07-2634] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Breast density is a well-known breast cancer risk factor. Most current methods of measuring breast density are area based and subjective. Standard mammogram form (SMF) is a computer program using a volumetric approach to estimate the percent density in the breast. The aim of this study is to evaluate the current implementation of SMF as a predictor of breast cancer risk by comparing it with other widely used density measurement methods. The case-control study comprised 634 cancers with 1,880 age-matched controls combined from the Cambridge and Norwich Breast Screening Programs. Data collection involved assessing the films based both on Wolfe's parenchymal patterns and on visual estimation of percent density and then digitizing the films for computer analysis (interactive threshold technique and SMF). Logistic regression was used to produce odds ratios associated with increasing categories of breast density. Density measures from all four methods were strongly associated with breast cancer risk in the overall population. The stepwise rises in risk associated with increasing density as measured by the threshold method were 1.37 [95% confidence interval (95% CI), 1.03-1.82], 1.80 (95% CI, 1.36-2.37), and 2.45 (95% CI, 1.86-3.23). For each increasing quartile of SMF density measures, the risks were 1.11 (95% CI, 0.85-1.46), 1.31 (95% CI, 1.00-1.71), and 1.92 (95% CI, 1.47-2.51). After the model was adjusted for SMF results, the threshold readings maintained the same strong stepwise increase in density-risk relationship. On the contrary, once the model was adjusted for threshold readings, SMF outcome was no longer related to cancer risk. The available implementation of SMF is not a better cancer risk predictor compared with the thresholding method.
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Affiliation(s)
- Jane Ding
- Department of Radiology, University of Cambridge, Addenbrooke's Hospital, Box 218, Hills Road, Cambridge CB2 0QQ, United Kingdom
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Abstract
Mammographic density has been strongly associated with increased risk of breast cancer. Furthermore, density is inversely correlated with the accuracy of mammography and, therefore, a measurement of density conveys information about the difficulty of detecting cancer in a mammogram. Initial methods for assessing mammographic density were entirely subjective and qualitative; however, in the past few years methods have been developed to provide more objective and quantitative density measurements. Research is now underway to create and validate techniques for volumetric measurement of density. It is also possible to measure breast density with other imaging modalities, such as ultrasound and MRI, which do not require the use of ionizing radiation and may, therefore, be more suitable for use in young women or where it is desirable to perform measurements more frequently. In this article, the techniques for measurement of density are reviewed and some consideration is given to their strengths and limitations.
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Affiliation(s)
- Martin J Yaffe
- Imaging Research Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada.
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Mammographic density using two computer-based methods in an isoflavone trial. Maturitas 2008; 59:350-7. [PMID: 18495387 DOI: 10.1016/j.maturitas.2008.03.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2007] [Revised: 03/16/2008] [Accepted: 03/20/2008] [Indexed: 11/20/2022]
Abstract
OBJECTIVES Mammographic density is a useful biomarker of breast cancer risk. Computer-based methods can provide continuous data suitable for analysis. This study aimed to compare a semi-automated computer-assisted method (Cumulus) and a fully automated volumetric computer method (standard mammogram form (SMF)) for assessing mammographic density using data from a previously conducted randomised placebo-controlled trial of an isoflavone supplement. METHODS Mammograms were obtained from participants in the intervention study. A total of 177 women completed the study. Baseline and follow-up mammograms were digitised and density was estimated using Cumulus (read by two readers) and SMF. Left-right correlation, changes in density over time, and difference between intervention and control groups were evaluated. Changes of density over time, and changes between intervention group and control group were examined using paired t-test and Student's t-test, respectively. RESULTS Inter-reader correlation coefficient by Cumulus was 0.90 for dense area, and 0.86 for percentage density. Left-right correlation of percent density was lower in SMF than in Cumulus. Among all women, percentage density by Cumulus decreased significantly over time, but no change was seen for SMF percentage density. The intervention group showed marginally significant greater reduction of percent density by Cumulus compared to controls (p=0.04), but the difference became weak after adjustment for baseline percent density (p=0.06). No other measurement demonstrated significant difference between intervention and control groups. CONCLUSIONS This comparison suggests that slightly different conclusions could be drawn from different methods used to assess breast density. The development of a more robust fully automated method is awaited.
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Garcia-Closas M, Troester MA, Qi Y, Langerød A, Yeager M, Lissowska J, Brinton L, Welch R, Peplonska B, Gerhard DS, Gram IT, Kristensen V, Børresen-Dale AL, Chanock S, Perou CM. Common genetic variation in GATA-binding protein 3 and differential susceptibility to breast cancer by estrogen receptor alpha tumor status. Cancer Epidemiol Biomarkers Prev 2008; 16:2269-75. [PMID: 18006915 DOI: 10.1158/1055-9965.epi-07-0449] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
GATA-binding protein 3 (GATA3) is a transcription factor and a putative tumor suppressor that is highly expressed in normal breast luminal epithelium and estrogen receptor alpha (ER)-positive breast tumors. We hypothesized that common genetic variation in GATA3 could influence breast carcinogenesis. Four tag single-nucleotide polymorphisms (SNP) in GATA3 and its 3' flanking gene FLJ4598 were genotyped in two case control studies in Norway and Poland (2,726 cases and 3,420 controls). Analyses of pooled data suggested a reduced risk of breast cancer associated with two intronic variants in GATA3 in linkage disequilibrium (rs3802604 in intron 3 and rs570613 in intron 4). Odds ratio (95% confidence interval) for rs570613 heterozygous and rare homozygous versus common homozygous were 0.85 (0.75-1.95) and 0.82 (0.62-0.96), respectively (P(trend)=0.004). Stronger associations were observed for subjects with ER-negative, than ER-positive, tumors (P(heterogeneity)=0.01 for rs3802604; P(heterogeneity)=0.09 for rs570613). Although no individual SNPs were associated with ER-positive tumors, two haplotypes (GGTC in 2% of controls and AATT in 7% of controls) showed significant and consistent associations with increased risk for these tumors when compared with the common haplotype (GATT in 46% of controls): 1.71 (1.27-2.32) and 1.26 (1.03-1.54), respectively. In summary, data from two independent study populations showed two intronic variants in GATA3 associated with overall decreases in breast cancer risk and suggested heterogeneity of these associations by ER status. These differential associations are consistent with markedly different levels of GATA3 protein by ER status. Additional epidemiologic studies are needed to clarify these intriguing relationships.
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Affiliation(s)
- Montserrat Garcia-Closas
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, 6120 Executive Boulevard, Room 7076, MSC 7234, Rockville, MD 20852-7234, USA.
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Bremnes Y, Ursin G, Bjurstam N, Gram IT. Different measures of smoking exposure and mammographic density in postmenopausal Norwegian women: a cross-sectional study. Breast Cancer Res 2008; 9:R73. [PMID: 17963507 PMCID: PMC2242671 DOI: 10.1186/bcr1782] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2007] [Revised: 10/01/2007] [Accepted: 10/26/2007] [Indexed: 11/17/2022] Open
Abstract
Background Recent cohort studies have suggested an increased risk of breast cancer with long duration of smoking, and with smoking initiation before first birth. Cigarette smoking may have both carcinogenic effects and antiestrogenic effects on the breast tissue. We decided to examine the relationship between different measures of smoking exposure and mammographic density. Methods Lifetime smoking history was collected through interview and questionnaires among 907 postmenopausal participants in the Tromsø Mammography and Breast Cancer study. The mammograms were obtained from the governmental Norwegian Breast Cancer Screening Program. Mammograms were classified according to the percentage and absolute mammographic densities using a previously validated computer-assisted method. Results Sixty-five percent of the women reported having ever smoked cigarettes, while 34% were current smokers. After adjustment for age, age at first birth, parity, age at menopause, postmenopausal hormone therapy use, and body mass index, smoking was inversely associated with both measures of mammographic density (both trends P < 0.01). Both current smokers and former smokers had significantly lower adjusted mean percentage mammographic density compared with never smokers (P = 0.003 and P = 0.006, respectively). An inverse dose–response relationship with mammographic density was found between both the number of cigarettes and the number of pack-years smoked among current smokers. Current smokers who smoked 11 cigarettes or more daily had a 3.7% absolute (36% relative difference) lower percentage mammographic density compared with current smokers who smoked seven cigarettes or less daily (P = 0.008). When former smokers were stratified according to time since smoking cessation, we found that women who had stopped smoking less than 24 years ago had a significantly lower mean percentage mammographic density compared with never smokers (P < 0.001). Conclusion We found modest inverse dose–response associations between numbers of cigarettes and of pack-years smoked and both measures of mammographic density among current smokers. Former smokers who had stopped smoking less than 24 years ago also had a statistically significantly lower mean percentage mammographic density when compared with never smokers. These findings are consistent with an antiestrogenic effect of cigarette smoking on the breast tissue.
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Affiliation(s)
- Yngve Bremnes
- Institute of Community Medicine, University of Tromsø, Tromsø, Norway.
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Perry NM, Allgood PC, Milner SE, Mokbel K, Duffy SW. Mammographic breast density by area of residence: possible evidence of higher density in urban areas. Curr Med Res Opin 2008; 24:365-8. [PMID: 18096111 DOI: 10.1185/030079908x260907] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
OBJECTIVES A comparison of mammographic breast densities of women living in London with those of women living in rural and suburban areas. DESIGN AND METHODS Using the standard four American College of Radiology Breast Imaging Reporting and Data System (BIRADS) categories of mammographic density, 318 mammograms of women from London and 654 mammograms of women from outside the capital aged 27-87 years who had received mammography at the Princess Grace Hospital, London, were assessed for density. The association between having any dense tissue and area of residence was assessed using both ordered and standard logistic regression, giving odds ratio estimates of relative risk of dense tissue adjusting for age. RESULTS Adjusting for age, London residents had significantly higher levels of density (OR = 1.32, 95% CI 1.04-1.70, p = 0.02). The major difference occurred in the age group 45-54 years and was most strongly manifested as a higher rate in London for density of 25% or more (BIRADS categories 2-4) as compared to almost entirely fatty (BIRADS 1) (OR = 2.22, 95% CI 1.05-4.68, p = 0.035). CONCLUSION The higher density is likely to be due to a different prevalence of risk factors in the London population. This study cannot ascertain the reason for the higher density in this urban population, but the result is a cause for concern given that screening uptake is lower in London. Increased attention to screening in urban areas and attention to screening quality for dense breast tissue might be prudent.
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Affiliation(s)
- N M Perry
- London Breast Institute, Princess Grace Hospital, 42-52 Nottingham Place, London, UK
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Mammography: Interobserver variability in breast density assessment. Breast 2007; 16:568-76. [PMID: 18035541 DOI: 10.1016/j.breast.2007.04.007] [Citation(s) in RCA: 96] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2006] [Revised: 04/18/2007] [Accepted: 04/20/2007] [Indexed: 11/24/2022] Open
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