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Kim E, Lewin AA. Breast Density: Where Are We Now? Radiol Clin North Am 2024; 62:593-605. [PMID: 38777536 DOI: 10.1016/j.rcl.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.
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Affiliation(s)
- Eric Kim
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Alana A Lewin
- Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA; New York University Grossman School of Medicine, New York University Langone Health, Laura and Isaac Perlmutter Cancer Center, 160 East 34th Street 3rd Floor, New York, NY 10016, USA.
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2
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Stibbards-Lyle M, Malinovska J, Badawy S, Schedin P, Rinker KD. Status of breast cancer detection in young women and potential of liquid biopsy. Front Oncol 2024; 14:1398196. [PMID: 38835377 PMCID: PMC11148378 DOI: 10.3389/fonc.2024.1398196] [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: 03/11/2024] [Accepted: 05/01/2024] [Indexed: 06/06/2024] Open
Abstract
Young onset breast cancer (YOBC) is an increasing demographic with unique biology, limited screening, and poor outcomes. Further, women with postpartum breast cancers (PPBCs), cancers occurring up to 10 years after childbirth, have worse outcomes than other young breast cancer patients matched for tumor stage and subtype. Early-stage detection of YOBC is critical for improving outcomes. However, most young women (under 45) do not meet current age guidelines for routine mammographic screening and are thus an underserved population. Other challenges to early detection in this population include reduced performance of standard of care mammography and reduced awareness. Women often face significant barriers in accessing health care during the postpartum period and disadvantaged communities face compounding barriers due to systemic health care inequities. Blood tests and liquid biopsies targeting early detection may provide an attractive option to help address these challenges. Test development in this area includes understanding of the unique biology involved in YOBC and in particular PPBCs that tend to be more aggressive and deadly. In this review, we will present the status of breast cancer screening and detection in young women, provide a summary of some unique biological features of YOBC, and discuss the potential for blood tests and liquid biopsy platforms to address current shortcomings in timely, equitable detection.
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Affiliation(s)
- Maya Stibbards-Lyle
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Cellular and Molecular Bioengineering Research Lab, University of Calgary, Calgary, AB, Canada
| | - Julia Malinovska
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Cellular and Molecular Bioengineering Research Lab, University of Calgary, Calgary, AB, Canada
| | - Seleem Badawy
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Cellular and Molecular Bioengineering Research Lab, University of Calgary, Calgary, AB, Canada
| | - Pepper Schedin
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Kristina D Rinker
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
- Cellular and Molecular Bioengineering Research Lab, University of Calgary, Calgary, AB, Canada
- Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB, Canada
- Department of Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada
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3
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Tejani AS, Ng YS, Xi Y, Rayan JC. Understanding and Mitigating Bias in Imaging Artificial Intelligence. Radiographics 2024; 44:e230067. [PMID: 38635456 DOI: 10.1148/rg.230067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024]
Abstract
Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model development, with potential for exacerbating health disparities. However, bias in imaging AI is a complex topic that encompasses multiple coexisting definitions. Bias may refer to unequal preference to a person or group owing to preexisting attitudes or beliefs, either intentional or unintentional. However, cognitive bias refers to systematic deviation from objective judgment due to reliance on heuristics, and statistical bias refers to differences between true and expected values, commonly manifesting as systematic error in model prediction (ie, a model with output unrepresentative of real-world conditions). Clinical decisions informed by biased models may lead to patient harm due to action on inaccurate AI results or exacerbate health inequities due to differing performance among patient populations. However, while inequitable bias can harm patients in this context, a mindful approach leveraging equitable bias can address underrepresentation of minority groups or rare diseases. Radiologists should also be aware of bias after AI deployment such as automation bias, or a tendency to agree with automated decisions despite contrary evidence. Understanding common sources of imaging AI bias and the consequences of using biased models can guide preventive measures to mitigate its impact. Accordingly, the authors focus on sources of bias at stages along the imaging machine learning life cycle, attempting to simplify potentially intimidating technical terminology for general radiologists using AI tools in practice or collaborating with data scientists and engineers for AI tool development. The authors review definitions of bias in AI, describe common sources of bias, and present recommendations to guide quality control measures to mitigate the impact of bias in imaging AI. Understanding the terms featured in this article will enable a proactive approach to identifying and mitigating bias in imaging AI. Published under a CC BY 4.0 license. Test Your Knowledge questions for this article are available in the supplemental material. See the invited commentary by Rouzrokh and Erickson in this issue.
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Affiliation(s)
- Ali S Tejani
- From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
| | - Yee Seng Ng
- From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
| | - Yin Xi
- From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
| | - Jesse C Rayan
- From the Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390
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Kwon MR, Chang Y, Ham SY, Cho Y, Kim EY, Kang J, Park EK, Kim KH, Kim M, Kim TS, Lee H, Kwon R, Lim GY, Choi HR, Choi J, Kook SH, Ryu S. Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection. Breast Cancer Res 2024; 26:68. [PMID: 38649889 PMCID: PMC11036604 DOI: 10.1186/s13058-024-01821-w] [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: 09/05/2023] [Accepted: 04/08/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. METHODS We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI's performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A-D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. RESULTS Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7-77.3] vs. 67.1% [95% CI, 58.8-74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9-93.2] vs. 77.6% [95% CI, 61.7-77.9]), PPV (1.5% [95% CI, 1.2-1.9] vs. 0.5% [95% CI, 0.4-0.6]), recall rate (7.1% [95% CI, 6.9-7.2] vs. 22.5% [95% CI, 22.2-22.7]), and AUC values (0.8 [95% CI, 0.76-0.84] vs. 0.74 [95% CI, 0.7-0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. CONCLUSIONS AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.
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Affiliation(s)
- Mi-Ri Kwon
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Yoosoo Chang
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, 04514, Seoul, South Korea.
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.
| | - Soo-Youn Ham
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Yoosun Cho
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, 04514, Seoul, South Korea
| | - Eun Young Kim
- Department of Surgery, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jeonggyu Kang
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, 04514, Seoul, South Korea
| | | | | | - Minjeong Kim
- Lunit Inc, Seoul, Republic of Korea
- Department of Statistics, Ewha Womans University, Seoul, Republic of Korea
| | | | | | - Ria Kwon
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, 04514, Seoul, South Korea
- Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - Ga-Young Lim
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, 04514, Seoul, South Korea
- Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - Hye Rin Choi
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, 04514, Seoul, South Korea
- Institute of Medical Research, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - JunHyeok Choi
- School of Mechanical Engineering, Sunkyungkwan University, Seoul, Republic of Korea
| | - Shin Ho Kook
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Seungho Ryu
- Center for Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, 04514, Seoul, South Korea.
- Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Clinical Research Design & Evaluation, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea.
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Pleasant V. A Public Health Emergency: Breast Cancer Among Black Communities in the United States. Obstet Gynecol Clin North Am 2024; 51:69-103. [PMID: 38267132 DOI: 10.1016/j.ogc.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
While Black people have a similar incidence of breast cancer compared to White people, they have a 40% increased death rate. Black people are more likely to be diagnosed with aggressive subtypes such as triple-negative breast cancer. However, despite biological factors, systemic racism and social determinants of health create delays in care and barriers to treatment. While genetic testing holds incredible promise for Black people, uptake remains low and results may be challenging to interpret. There is a need for more robust, multidisciplinary, and antiracist interventions to reverse breast cancer-related racial disparities.
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Affiliation(s)
- Versha Pleasant
- Department of Obstetrics and Gynecology, Cancer Genetics & Breast Health Clinic, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA.
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6
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Khara G, Trivedi H, Newell MS, Patel R, Rijken T, Kecskemethy P, Glocker B. Generalisable deep learning method for mammographic density prediction across imaging techniques and self-reported race. COMMUNICATIONS MEDICINE 2024; 4:21. [PMID: 38374436 PMCID: PMC10876691 DOI: 10.1038/s43856-024-00446-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 01/31/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Breast density is an important risk factor for breast cancer complemented by a higher risk of cancers being missed during screening of dense breasts due to reduced sensitivity of mammography. Automated, deep learning-based prediction of breast density could provide subject-specific risk assessment and flag difficult cases during screening. However, there is a lack of evidence for generalisability across imaging techniques and, importantly, across race. METHODS This study used a large, racially diverse dataset with 69,697 mammographic studies comprising 451,642 individual images from 23,057 female participants. A deep learning model was developed for four-class BI-RADS density prediction. A comprehensive performance evaluation assessed the generalisability across two imaging techniques, full-field digital mammography (FFDM) and two-dimensional synthetic (2DS) mammography. A detailed subgroup performance and bias analysis assessed the generalisability across participants' race. RESULTS Here we show that a model trained on FFDM-only achieves a 4-class BI-RADS classification accuracy of 80.5% (79.7-81.4) on FFDM and 79.4% (78.5-80.2) on unseen 2DS data. When trained on both FFDM and 2DS images, the performance increases to 82.3% (81.4-83.0) and 82.3% (81.3-83.1). Racial subgroup analysis shows unbiased performance across Black, White, and Asian participants, despite a separate analysis confirming that race can be predicted from the images with a high accuracy of 86.7% (86.0-87.4). CONCLUSIONS Deep learning-based breast density prediction generalises across imaging techniques and race. No substantial disparities are found for any subgroup, including races that were never seen during model development, suggesting that density predictions are unbiased.
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Affiliation(s)
| | - Hari Trivedi
- Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Mary S Newell
- Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Ravi Patel
- Kheiron Medical Technologies, London, UK
| | | | | | - Ben Glocker
- Kheiron Medical Technologies, London, UK.
- Department of Computing, Imperial College London, London, UK.
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7
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Wilkerson AD, Gentle CK, Ortega C, Al-Hilli Z. Disparities in Breast Cancer Care-How Factors Related to Prevention, Diagnosis, and Treatment Drive Inequity. Healthcare (Basel) 2024; 12:462. [PMID: 38391837 PMCID: PMC10887556 DOI: 10.3390/healthcare12040462] [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: 12/16/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/24/2024] Open
Abstract
Breast cancer survival has increased significantly over the last few decades due to more effective strategies for prevention and risk modification, advancements in imaging detection, screening, and multimodal treatment algorithms. However, many have observed disparities in benefits derived from such improvements across populations and demographic groups. This review summarizes published works that contextualize modern disparities in breast cancer prevention, diagnosis, and treatment and presents potential strategies for reducing disparities. We conducted searches for studies that directly investigated and/or reported disparities in breast cancer prevention, detection, or treatment. Demographic factors, social determinants of health, and inequitable healthcare delivery may impede the ability of individuals and communities to employ risk-mitigating behaviors and prevention strategies. The disparate access to quality screening and timely diagnosis experienced by various groups poses significant hurdles to optimal care and survival. Finally, barriers to access and inequitable healthcare delivery patterns reinforce inequitable application of standards of care. Cumulatively, these disparities underlie notable differences in the incidence, severity, and survival of breast cancers. Efforts toward mitigation will require collaborative approaches and partnerships between communities, governments, and healthcare organizations, which must be considered equal stakeholders in the fight for equity in breast cancer care and outcomes.
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Affiliation(s)
- Avia D Wilkerson
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Corey K Gentle
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Camila Ortega
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zahraa Al-Hilli
- Department of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Breast Center, Integrated Surgical Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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8
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Pleasant V. Patient counseling for breast cancer screening: Taking changes to USPSTF recommendations into account. OBG MANAGEMENT 2023; 35:43-49. [PMID: 38835939 PMCID: PMC11148851 DOI: 10.12788/obgm.0334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
The US Preventive Services Task Force now recommends mammography screening every other year starting at age 40 to decrease the risk of dying from breast cancer, but questions remain regarding biennial versus annual screening as well as disparities in risk factors and outcomes among Black people.
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Affiliation(s)
- Versha Pleasant
- Department of Obstetrics and Gynecology, University of Michigan, Ann Arbor
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9
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McCarthy AM, Fernandez Perez C, Beidas RS, Bekelman JE, Blumenthal D, Mack E, Bauer AM, Ehsan S, Conant EF, Wheeler BC, Guerra CE, Nunes LW, Gabriel P, Doucette A, Wileyto EP, Buttenheim AM, Asch DA, Rendle KA, Shelton RC, Fayanju OM, Ware S, Plag M, Hyland S, Gionta T, Shulman LN, Schnoll R. Protocol for a pragmatic stepped wedge cluster randomized clinical trial testing behavioral economic implementation strategies to increase supplemental breast MRI screening among patients with extremely dense breasts. Implement Sci 2023; 18:65. [PMID: 38001506 PMCID: PMC10668465 DOI: 10.1186/s13012-023-01323-x] [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: 09/19/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Increased breast density augments breast cancer risk and reduces mammography sensitivity. Supplemental breast MRI screening can significantly increase cancer detection among women with dense breasts. However, few women undergo this exam, and screening is consistently lower among racially minoritized populations. Implementation strategies informed by behavioral economics ("nudges") can promote evidence-based practices by improving clinician decision-making under conditions of uncertainty. Nudges directed toward clinicians and patients may facilitate the implementation of supplemental breast MRI. METHODS Approximately 1600 patients identified as having extremely dense breasts after non-actionable mammograms, along with about 1100 clinicians involved with their care at 32 primary care or OB/GYN clinics across a racially diverse academically based health system, will be enrolled. A 2 × 2 randomized pragmatic trial will test nudges to patients, clinicians, both, or neither to promote supplemental breast MRI screening. Before implementation, rapid cycle approaches informed by clinician and patient experiences and behavioral economics and health equity frameworks guided nudge design. Clinicians will be clustered into clinic groups based on existing administrative departments and care patterns, and these clinic groups will be randomized to have the nudge activated at different times per a stepped wedge design. Clinicians will receive nudges integrated into the routine mammographic report or sent through electronic health record (EHR) in-basket messaging once their clinic group (i.e., wedge) is randomized to receive the intervention. Independently, patients will be randomized to receive text message nudges or not. The primary outcome will be defined as ordering or scheduling supplemental breast MRI. Secondary outcomes include MRI completion, cancer detection rates, and false-positive rates. Patient sociodemographic information and clinic-level variables will be examined as moderators of nudge effectiveness. Qualitative interviews conducted at the trial's conclusion will examine barriers and facilitators to implementation. DISCUSSION This study will add to the growing literature on the effectiveness of behavioral economics-informed implementation strategies to promote evidence-based interventions. The design will facilitate testing the relative effects of nudges to patients and clinicians and the effects of moderators of nudge effectiveness, including key indicators of health disparities. The results may inform the introduction of low-cost, scalable implementation strategies to promote early breast cancer detection. TRIAL REGISTRATION ClinicalTrials.gov NCT05787249. Registered on March 28, 2023.
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Affiliation(s)
- Anne Marie McCarthy
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA.
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA.
| | | | - Rinad S Beidas
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Justin E Bekelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Daniel Blumenthal
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mack
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anna-Marika Bauer
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Ehsan
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emily F Conant
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Carmen E Guerra
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Linda W Nunes
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Peter Gabriel
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Abigail Doucette
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - E Paul Wileyto
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Alison M Buttenheim
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
- School of Nursing, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Asch
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Katharine A Rendle
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Blockley Hall, 423 Guardian Drive, Philadelphia, PA, 19104, USA
| | - Rachel C Shelton
- Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Oluwadamilola M Fayanju
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Sue Ware
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
| | - Martina Plag
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven Hyland
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tracy Gionta
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lawrence N Shulman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
| | - Robert Schnoll
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, USA
- Center for Interdisciplinary Research On Nicotine Addiction, University of Pennsylvania, Philadelphia, PA, USA
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10
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Kerlikowske K, Bissell MCS, Sprague BL, Tice JA, Tossas KY, Bowles EJA, Ho TQH, Keegan THM, Miglioretti DL. Impact of BMI on Prevalence of Dense Breasts by Race and Ethnicity. Cancer Epidemiol Biomarkers Prev 2023; 32:1524-1530. [PMID: 37284771 PMCID: PMC10701641 DOI: 10.1158/1055-9965.epi-23-0049] [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: 01/18/2023] [Revised: 03/18/2023] [Accepted: 04/25/2023] [Indexed: 06/08/2023] Open
Abstract
BACKGROUND Density notification laws require notifying women of dense breasts with dense breast prevalence varying by race/ethnicity. We evaluated whether differences in body mass index (BMI) account for differences in dense breasts prevalence by race/ethnicity. METHODS Prevalence of dense breasts (heterogeneously or extremely dense) according to Breast Imaging Reporting and Data System and obesity (BMI > 30 kg/m2) were estimated from 2,667,207 mammography examinations among 866,033 women in the Breast Cancer Surveillance Consortium (BCSC) from January 2005 through April 2021. Prevalence ratios (PR) for dense breasts relative to overall prevalence by race/ethnicity were estimated by standardizing race/ethnicity prevalence in the BCSC to the 2020 U.S. population, and adjusting for age, menopausal status, and BMI using logistic regression. RESULTS Dense breasts were most prevalent among Asian women (66.0%) followed by non-Hispanic/Latina (NH) White (45.5%), Hispanic/Latina (45.3%), and NH Black (37.0%) women. Obesity was most prevalent in Black women (58.4%) followed by Hispanic/Latina (39.3%), NH White (30.6%), and Asian (8.5%) women. The adjusted prevalence of dense breasts was 19% higher [PR = 1.19; 95% confidence interval (CI), 1.19-1.20] in Asian women, 8% higher (PR = 1.08; 95% CI, 1.07-1.08) in Black women, the same in Hispanic/Latina women (PR = 1.00; 95% CI, 0.99-1.01), and 4% lower (PR = 0.96; 95% CI, 0.96-0.97) in NH White women relative to the overall prevalence. CONCLUSIONS Clinically important differences in breast density prevalence are present across racial/ethnic groups after accounting for age, menopausal status, and BMI. IMPACT If breast density is the sole criterion used to notify women of dense breasts and discuss supplemental screening it may result in implementing inequitable screening strategies across racial/ethnic groups. See related In the Spotlight, p. 1479.
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Affiliation(s)
- Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- General Internal Medicine Section, Department of Veterans Affairs, University of California, San Francisco, CA, USA
| | - Michael C. S. Bissell
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA
| | - Brian L. Sprague
- Departments of Surgery and Radiology, Office of Health Promotion Research, Larner College of Medicine at the University of Vermont and University of Vermont Cancer Center, Burlington, VT, USA
| | - Jeffrey A. Tice
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, CA, USA
| | - Katherine Y. Tossas
- Department of Health Behavior and Policy, School of Medicine, and Massey Cancer Center, Virginia Commonwealth University, Richmond VA, USA
| | - Erin J. A. Bowles
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Thao-Quyen H. Ho
- Department of Training and Scientific Research, University Medical Center, Ho Chi Minh city, Vietnam
- Breast Imaging Unit, Diagnostic Imaging Center, Tam Anh General Hospital, Ho Chi Minh City, Vietnam
| | - Theresa H. M. Keegan
- Center for Oncology Hematology Outcomes Research and Training (COHORT) and Division of Hematology and Oncology, University of California Davis School of Medicine, Sacramento, CA, USA
| | - Diana L. Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA, USA
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
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11
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Brown AL, Vijapura C, Patel M, De La Cruz A, Wahab R. Breast Cancer in Dense Breasts: Detection Challenges and Supplemental Screening Opportunities. Radiographics 2023; 43:e230024. [PMID: 37792590 DOI: 10.1148/rg.230024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Dense breast tissue at mammography is associated with higher breast cancer incidence and mortality rates, which have prompted new considerations for breast cancer screening in women with dense breasts. The authors review the definition and classification of breast density, density assessment methods, breast cancer risk, current legislation, and future efforts and summarize trials and key studies that have affected the existing guidelines for supplemental screening. Cases of breast cancer in dense breasts are presented, highlighting a variety of modalities and specific imaging findings that can aid in cancer detection and staging. Understanding the current state of breast cancer screening in patients with dense breasts and its challenges is important to shape future considerations for care. Shifting the paradigm of breast cancer detection toward early diagnosis for women with dense breasts may be the answer to reducing the number of deaths from this common disease. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center. See the invited commentary by Yeh in this issue.
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Affiliation(s)
- Ann L Brown
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Charmi Vijapura
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Mitva Patel
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Alexis De La Cruz
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
| | - Rifat Wahab
- From the Department of Radiology, University of Cincinnati Medical Center, 3188 Bellevue Ave, Cincinnati, OH 45219-0772 (A.L.B., C.V., A.D.L.C., R.W.); and Department of Radiology, Ohio State University Medical Center, Columbus, Ohio (M.P.)
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12
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Vachon CM, Scott CG, Norman AD, Khanani SA, Jensen MR, Hruska CB, Brandt KR, Winham SJ, Kerlikowske K. Impact of Artificial Intelligence System and Volumetric Density on Risk Prediction of Interval, Screen-Detected, and Advanced Breast Cancer. J Clin Oncol 2023; 41:3172-3183. [PMID: 37104728 PMCID: PMC10256336 DOI: 10.1200/jco.22.01153] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 12/13/2022] [Accepted: 02/24/2023] [Indexed: 04/29/2023] Open
Abstract
PURPOSE Artificial intelligence (AI) algorithms improve breast cancer detection on mammography, but their contribution to long-term risk prediction for advanced and interval cancers is unknown. METHODS We identified 2,412 women with invasive breast cancer and 4,995 controls matched on age, race, and date of mammogram, from two US mammography cohorts, who had two-dimensional full-field digital mammograms performed 2-5.5 years before cancer diagnosis. We assessed Breast Imaging Reporting and Data System density, an AI malignancy score (1-10), and volumetric density measures. We used conditional logistic regression to estimate odds ratios (ORs), 95% CIs, adjusted for age and BMI, and C-statistics (AUC) to describe the association of AI score with invasive cancer and its contribution to models with breast density measures. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance. RESULTS On mammograms between 2-5.5 years prior to cancer, a one unit increase in AI score was associated with 20% greater odds of invasive breast cancer (OR, 1.20; 95% CI, 1.17 to 1.22; AUC, 0.63; 95% CI, 0.62 to 0.64) and was similarly predictive of interval (OR, 1.20; 95% CI, 1.13 to 1.27; AUC, 0.63) and advanced cancers (OR, 1.23; 95% CI, 1.16 to 1.31; AUC, 0.64) and in dense (OR, 1.18; 95% CI, 1.15 to 1.22; AUC, 0.66) breasts. AI score improved prediction of all cancer types in models with density measures (PLRT values < .001); discrimination improved for advanced cancer (ie, AUC for dense volume increased from 0.624 to 0.679, Δ AUC 0.065, P = .01) but did not reach statistical significance for interval cancer. CONCLUSION AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, in particular, advanced cancer.
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Affiliation(s)
- Celine M. Vachon
- Division of Epidemiology, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Christopher G. Scott
- Division of Clinical Trials and Biostatistics, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Aaron D. Norman
- Division of Epidemiology, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Sadia A. Khanani
- Division of Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, MN
| | - Matthew R. Jensen
- Division of Clinical Trials and Biostatistics, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
| | - Carrie B. Hruska
- Division of Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, MN
| | - Kathleen R. Brandt
- Division of Breast Imaging, Department of Radiology, Mayo Clinic, Rochester, MN
| | - Stacey J. Winham
- Division of Computational Biology, Department Quantitative Sciences, Mayo Clinic, Rochester, MN
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13
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Verma SS, Guare L, Ehsan S, Gastounioti A, Scales G, Ritchie MD, Kontos D, McCarthy AM. Genome-Wide Association Study of Breast Density among Women of African Ancestry. Cancers (Basel) 2023; 15:2776. [PMID: 37345113 DOI: 10.3390/cancers15102776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/03/2023] [Accepted: 05/11/2023] [Indexed: 06/23/2023] Open
Abstract
Breast density, the amount of fibroglandular versus fatty tissue in the breast, is a strong breast cancer risk factor. Understanding genetic factors associated with breast density may help in clarifying mechanisms by which breast density increases cancer risk. To date, 50 genetic loci have been associated with breast density, however, these studies were performed among predominantly European ancestry populations. We utilized a cohort of women aged 40-85 years who underwent screening mammography and had genetic information available from the Penn Medicine BioBank to conduct a Genome-Wide Association Study (GWAS) of breast density among 1323 women of African ancestry. For each mammogram, the publicly available "LIBRA" software was used to quantify dense area and area percent density. We identified 34 significant loci associated with dense area and area percent density, with the strongest signals in GACAT3, CTNNA3, HSD17B6, UGDH, TAAR8, ARHGAP10, BOD1L2, and NR3C2. There was significant overlap between previously identified breast cancer SNPs and SNPs identified as associated with breast density. Our results highlight the importance of breast density GWAS among diverse populations, including African ancestry populations. They may provide novel insights into genetic factors associated with breast density and help in elucidating mechanisms by which density increases breast cancer risk.
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Affiliation(s)
- Shefali Setia Verma
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lindsay Guare
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah Ehsan
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aimilia Gastounioti
- Washington University School of Medicine in St. Louis, St. Louis, MO 63130, USA
| | | | - Marylyn D Ritchie
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Despina Kontos
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Anne Marie McCarthy
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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14
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Hunt JT, Kamat R, Yao M, Sharma N, Batur P. Effect of contraceptive hormonal therapy on mammographic breast density: A longitudinal cohort study. Clin Imaging 2023; 97:62-67. [PMID: 36893493 DOI: 10.1016/j.clinimag.2023.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 02/25/2023] [Accepted: 03/01/2023] [Indexed: 03/08/2023]
Abstract
PURPOSE Evaluate the longitudinal relationship between mammographic density and hormonal contraceptive use in late reproductive-aged women. METHODS Patients aged 35-50 years old who underwent 5 or more screening mammograms within a 7.5-year period between 2004 and 2019 in a single urban tertiary care center were randomly selected. Patients were categorized into four cohorts based on hormonal contraceptive exposure during a 2-year lead-in period and a 7.5-year study period: 1) never exposed, 2) always exposed, 3) interval hormonal contraceptive start, and 4) interval hormonal contraceptive stop. The primary outcome was difference in BI-RADS breast density category between initial and final mammograms. RESULTS Of the 708 patients included, long-term use of combined oral contraceptives or a levonorgestrel intrauterine device were not associated with an increase in breast density category over the 7.5-year study period, compared to those with no hormonal contraceptive exposure. Initiation of combined oral contraceptives was associated with an increase in breast density category (β = 0.31, P = 0.045); however, no difference in initial density category was noted between those exposed and those never exposed to combined oral contraceptives during the 2-year lead-in period, and discontinuation was not associated with a decrease in breast density category when compared to those with continuous exposure. CONCLUSION(S) Long-term use of combined oral contraceptives or a levonorgestrel intrauterine device was not associated with an increase in BI-RADS breast density category. Initiation of a combined oral contraceptive was associated with an increase in breast density category, although this may be a transient effect.
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Affiliation(s)
- Jonathan T Hunt
- Department of Obstetrics & Gynecology, Women's Health Institute, Cleveland Clinic, Desk A81, 9500 Euclid Avenue, Cleveland, OH 44195, United States.
| | - Rachel Kamat
- Department of Obstetrics & Gynecology, Women's Health Institute, Cleveland Clinic, Desk A81, 9500 Euclid Avenue, Cleveland, OH 44195, United States
| | - Meng Yao
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH 44195, United States
| | - Nidhi Sharma
- Austin Radiological Association Women's Imaging Center, Suite 100, 1600 West 38(th) Street, Austin, TX 78731, United States
| | - Pelin Batur
- Department of Obstetrics & Gynecology, Women's Health Institute, Cleveland Clinic, Desk A81, 9500 Euclid Avenue, Cleveland, OH 44195, United States
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15
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Gastounioti A, Cohen EA, Pantalone L, Ehsan S, Vasudevan S, Kurudi A, Conant EF, Chen J, Kontos D, McCarthy AM. Changes in mammographic density and risk of breast cancer among a diverse cohort of women undergoing mammography screening. Breast Cancer Res Treat 2023; 198:535-544. [PMID: 36800118 DOI: 10.1007/s10549-023-06879-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 02/01/2023] [Indexed: 02/18/2023]
Abstract
PURPOSE Mammographic density (MD) is a strong breast cancer risk factor. MD may change over time, with potential implications for breast cancer risk. Few studies have assessed associations between MD change and breast cancer in racially diverse populations. We investigated the relationships between MD and MD change over time and breast cancer risk in a large, diverse screening cohort. MATERIALS AND METHODS We retrospectively analyzed data from 8462 women who underwent ≥ 2 screening mammograms from Sept. 2010 to Jan. 2015 (N = 20,766 exams); 185 breast cancers were diagnosed 1-7 years after screening. Breast percent density (PD) and dense area (DA) were estimated from raw digital mammograms (Hologic Inc.) using LIBRA (v1.0.4). For each MD measure, we modeled breast density change between two sequential visits as a function of demographic and risk covariates. We used Cox regression to examine whether varying degrees of breast density change were associated with breast cancer risk, accounting for multiple exams per woman. RESULTS PD at any screen was significantly associated with breast cancer risk (hazard ratio (HR) for PD = 1.03 (95% CI [1.01, 1.05], p < 0.0005), but neither change in breast density nor more extreme than expected changes in breast density were associated with breast cancer risk. We found no evidence of differences in density change or breast cancer risk due to density change by race. Results using DA were essentially identical. CONCLUSIONS Using a large racially diverse cohort, we found no evidence of association between short-term change in MD and risk of breast cancer, suggesting that short-term MD change is not a strong predictor for risk.
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Affiliation(s)
- Aimilia Gastounioti
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Eric A Cohen
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lauren Pantalone
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah Ehsan
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sanjana Vasudevan
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Avinash Kurudi
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emily F Conant
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jinbo Chen
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Despina Kontos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne Marie McCarthy
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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16
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Ringel MJ, Richey WL, Heiselman JS, Meszoely IM, Miga MI. Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling. Clin Biomech (Bristol, Avon) 2023; 104:105927. [PMID: 36890069 PMCID: PMC10122703 DOI: 10.1016/j.clinbiomech.2023.105927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/10/2023]
Abstract
BACKGROUND Simulating soft-tissue breast deformations is of interest for many applications including image fusion, longitudinal registration, and image-guided surgery. For the surgical use case, positional changes cause breast deformations that compromise the use of preoperative imaging to inform tumor excision. Even when acquiring imaging in the supine position, which better reflects surgical presentation, deformations still occur due to arm motion and orientation changes. A biomechanical modeling approach to simulate supine breast deformations for surgical applications must be both accurate and compatible with the clinical workflow. METHODS A supine MR breast imaging dataset from n = 11 healthy volunteers was used to simulate surgical deformations by acquiring images in arm-down and arm-up positions. Three linear-elastic modeling approaches with varying levels of complexity were used to predict deformations caused by this arm motion: a homogeneous isotropic model, a heterogeneous isotropic model, and a heterogeneous anisotropic model using a transverse-isotropic constitutive model. FINDINGS The average target registration errors for subsurface anatomical features were 5.4 ± 1.5 mm for the homogeneous isotropic model, 5.3 ± 1.5 mm for the heterogeneous isotropic model, and 4.7 ± 1.4 mm for the heterogeneous anisotropic model. A statistically significant improvement in target registration error was observed between the heterogeneous anisotropic model and both the homogeneous and the heterogeneous isotropic models (P < 0.01). INTERPRETATION While a model that fully incorporates all constitutive complexities of anatomical structure likely achieves the best accuracy, a computationally tractable heterogeneous anisotropic model provided significant improvement and may be applicable for image-guided breast surgeries.
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Affiliation(s)
- Morgan J Ringel
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA.
| | - Winona L Richey
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA
| | - Jon S Heiselman
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA; Memorial Sloan-Kettering Cancer Center, Department of Surgery, NY, New York, USA
| | - Ingrid M Meszoely
- Vanderbilt University Medical Center, Division of Surgical Oncology, Nashville, TN, USA
| | - Michael I Miga
- Vanderbilt University, Department of Biomedical Engineering, Nashville, TN, USA; Vanderbilt Institute for Surgery and Engineering, Nashville, TN, USA; Vanderbilt University, Department of Radiology and Radiological Sciences, Nashville, TN, USA; Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, TN, USA; Vanderbilt University Medical Center, Department of Otolaryngology-Head and Neck Surgery, Nashville, TN, USA
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17
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Edmonds CE, O'Brien SR, Conant EF. Mammographic Breast Density: Current Assessment Methods, Clinical Implications, and Future Directions. Semin Ultrasound CT MR 2023; 44:35-45. [PMID: 36792272 DOI: 10.1053/j.sult.2022.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Mammographic breast density is widely accepted as an independent risk factor for the development of breast cancer. In addition, because dense breast tissue may mask breast malignancies, breast density is inversely related to the sensitivity of screening mammography. Given the risks associated with breast density, as well as ongoing efforts to stratify individual risk and personalize breast cancer screening and prevention, numerous studies have sought to better understand the factors that impact breast density, and to develop and implement reproducible, quantitative methods to assess mammographic density. Breast density assessments have been incorporated into risk assessment models to improve risk stratification. Recently, novel techniques for analyzing mammographic parenchymal complexity, or texture, have been explored as potential means of refining mammographic tissue-based risk assessment beyond breast density.
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Affiliation(s)
- Christine E Edmonds
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA.
| | - Sophia R O'Brien
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
| | - Emily F Conant
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA
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18
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Huq MR, Woodard N, Okwara L, Knott CL. Breast Cancer Educational Needs and Concerns of African American Women Below Screening Age. JOURNAL OF CANCER EDUCATION : THE OFFICIAL JOURNAL OF THE AMERICAN ASSOCIATION FOR CANCER EDUCATION 2022; 37:1677-1683. [PMID: 33932194 DOI: 10.1007/s13187-021-02012-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/13/2021] [Indexed: 06/12/2023]
Abstract
African American women are disproportionately impacted by breast cancer, including triple-negative disease, at a young age. Yet most public health research in breast cancer focuses on women of screening age. This study identified the specific breast cancer educational needs of African American women below the recommended screening age. Data were collected through 30 key informant interviews with young African American women breast cancer survivors (diagnosed between 18 and 45), family members of African American women diagnosed between 18-45 years, and community organization leaders and healthcare providers who work with young African American women impacted by breast cancer. Data were coded and analyzed by multiple team members using template analysis. The analysis identified four overarching themes reflecting breast cancer educational needs of young African American women. Although most Breast Cancer Knowledge and Perceived Risk educational needs were consistent with those of older women, there were specific needs involving Cultural Reluctance in Health Disclosures and Breast Cancer Risk Reduction. With regard to Healthcare Provider Relationships, participants stated a need for younger women to be particularly proactive in advocating for their health, as providers may be dismissive about cancer concerns due to young age. Though breast cancer in younger women is statistically improbable, there are educational needs specific to young African American women involving self-advocacy and family history. Findings have implications for developing interventions guiding young women to advocate for themselves in medical encounters and in their families, as well as for teaching medical providers how to counsel the young women regarding breast cancer.
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Affiliation(s)
- Maisha R Huq
- Department of Behavioral and Community Health, School of Public Health, University of Maryland-College Park, 4200 Valley Drive, College Park, MD, 20742, USA.
| | - Nathaniel Woodard
- Department of Behavioral and Community Health, School of Public Health, University of Maryland-College Park, 4200 Valley Drive, College Park, MD, 20742, USA
| | - Leonore Okwara
- Department of Behavioral and Community Health, School of Public Health, University of Maryland-College Park, 4200 Valley Drive, College Park, MD, 20742, USA
| | - Cheryl L Knott
- Department of Behavioral and Community Health, School of Public Health, University of Maryland-College Park, #1234W 4200 Valley Drive, College Park, MD, 20742, USA
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19
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Chalfant JS, Hoyt AC. Breast Density: Current Knowledge, Assessment Methods, and Clinical Implications. JOURNAL OF BREAST IMAGING 2022; 4:357-370. [PMID: 38416979 DOI: 10.1093/jbi/wbac028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Indexed: 03/01/2024]
Abstract
Breast density is an accepted independent risk factor for the future development of breast cancer, and greater breast density has the potential to mask malignancies on mammography, thus lowering the sensitivity of screening mammography. The risk associated with dense breast tissue has been shown to be modifiable with changes in breast density. Numerous studies have sought to identify factors that influence breast density, including age, genetic, racial/ethnic, prepubertal, adolescent, lifestyle, environmental, hormonal, and reproductive history factors. Qualitative, semiquantitative, and quantitative methods of breast density assessment have been developed, but to date there is no consensus assessment method or reference standard for breast density. Breast density has been incorporated into breast cancer risk models, and there is growing consciousness of the clinical implications of dense breast tissue in both the medical community and public arena. Efforts to improve breast cancer screening sensitivity for women with dense breasts have led to increased attention to supplemental screening methods in recent years, prompting the American College of Radiology to publish Appropriateness Criteria for supplemental screening based on breast density.
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Affiliation(s)
- James S Chalfant
- David Geffen School of Medicine at University of California, Los Angeles, Department of Radiological Sciences, Santa Monica, CA, USA
| | - Anne C Hoyt
- David Geffen School of Medicine at University of California, Los Angeles, Department of Radiological Sciences, Santa Monica, CA, USA
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20
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Barnard ME, Martheswaran T, Van Meter M, Buys SS, Curtin K, Doherty JA. Body Mass Index and Mammographic Density in a Multiracial and Multiethnic Population-Based Study. Cancer Epidemiol Biomarkers Prev 2022; 31:1313-1323. [PMID: 35511751 PMCID: PMC9250611 DOI: 10.1158/1055-9965.epi-21-1249] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 01/25/2022] [Accepted: 04/27/2022] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Mammographic density (MD) is strongly associated with breast cancer risk. We examined whether body mass index (BMI) partially explains racial and ethnic variation in MD. METHODS We used multivariable Poisson regression to estimate associations between BMI and binary MD [Breast Imaging Reporting and Database System (BI-RADS) A&B versus BI-RADS C&D] among 160,804 women in the Utah mammography cohort. We estimated associations overall and within racial and ethnic subgroups and calculated population attributable risk percents (PAR%). RESULTS We observed the lowest BMI and highest MD among Asian women, the highest BMI among Native Hawaiian and Pacific Islander women, and the lowest MD among American Indian and Alaska Native (AIAN) and Black women. BMI was inversely associated with MD [RRBMI≥30 vs. BMI<25 = 0.43; 95% confidence interval (CI), 0.42-0.44] in the full cohort, and estimates in all racial and ethnic subgroups were consistent with this strong inverse association. For women less than 45 years of age, although there was statistical evidence of heterogeneity in associations between BMI and MD by race and ethnicity (P = 0.009), magnitudes of association were similar across groups. PAR%s for BMI and MD among women less than 45 years were considerably higher in White women (PAR% = 29.2, 95% CI = 28.4-29.9) compared with all other groups with estimates ranging from PAR%Asain = 17.2%; 95% CI, 8.5 to 25.8 to PAR%Hispanic = 21.5%; 95% CI, 19.4 to 23.6. For women ≥55 years, PAR%s for BMI and MD were highest among AIAN women (PAR% = 37.5; 95% CI, 28.1-46.9). CONCLUSIONS While we observed substantial differences in the distributions of BMI and MD by race and ethnicity, associations between BMI and MD were generally similar across groups. IMPACT Distributions of BMI and MD may be important contributors to breast cancer disparities.
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Affiliation(s)
- Mollie E Barnard
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| | - Tarun Martheswaran
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| | | | - Saundra S Buys
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah
| | - Karen Curtin
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah
- Pedigree and Population Resource, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
| | - Jennifer Anne Doherty
- Department of Population Health Sciences, Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah
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21
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Patel BK, Pepin K, Brandt KR, Mazza GL, Pockaj BA, Chen J, Zhou Y, Northfelt DW, Anderson K, Kling JM, Vachon CM, Swanson KR, Nikkhah M, Ehman R. Association of breast cancer risk, density, and stiffness: global tissue stiffness on breast MR elastography (MRE). Breast Cancer Res Treat 2022; 194:79-89. [PMID: 35501423 PMCID: PMC9538705 DOI: 10.1007/s10549-022-06607-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 04/05/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE Quantify in vivo biomechanical tissue properties in various breast densities and in average risk and high-risk women using Magnetic Resonance Imaging (MRI)/MRE and examine the association between breast biomechanical properties and cancer risk based on patient demographics and clinical data. METHODS Patients with average risk or high-risk of breast cancer underwent 3.0 T breast MR imaging and elastography. Breast parenchymal enhancement (BPE), density (from most recent mammogram), stiffness, elasticity, and viscosity were recorded. Within each breast density group (non-dense versus dense), stiffness, elasticity, and viscosity were compared across risk groups (average versus high). Separately for stiffness, elasticity, and viscosity, a multivariable logistic regression model was used to evaluate whether the MRE parameter predicted risk status after controlling for clinical factors. RESULTS 50 average risk and 86 high-risk patients were included. Risk groups were similar in age, density, and menopausal status. Among patients with dense breasts, mean stiffness, elasticity, and viscosity were significantly higher in high-risk patients (N = 55) compared to average risk patients (N = 34; all p < 0.001). Stiffness remained a significant predictor of risk status (OR = 4.26, 95% CI [1.96, 9.25]) even after controlling for breast density, BPE, age, and menopausal status. Similar results were seen for elasticity and viscosity. CONCLUSION A structurally based, quantitative biomarker of tissue stiffness obtained from MRE is associated with differences in breast cancer risk in dense breasts. Tissue stiffness could provide a novel prognostic marker to help identify high-risk women with dense breasts who would benefit from increased surveillance and/or risk reduction measures.
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Affiliation(s)
- Bhavika K Patel
- Diagnostic Radiology, Mayo Clinic, 5777 E. Mayo Blvd., Phoenix, AZ, 85054, USA.
| | - Kay Pepin
- Diagnostic Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Gina L Mazza
- Department of Biostatistics, Mayo Clinic, Phoenix, AZ, USA
| | | | - Jun Chen
- Diagnostic Radiology, Mayo Clinic, Rochester, MN, USA
| | - Yuxiang Zhou
- Diagnostic Radiology, Mayo Clinic, 5777 E. Mayo Blvd., Phoenix, AZ, 85054, USA
| | | | | | - Juliana M Kling
- Department of Internal Medicine, Mayo Clinic, Phoenix, AZ, USA
| | | | | | - Mehdi Nikkhah
- School of Biological and Health Systems Engineering, Arizona State University, Phoenix, AZ, USA
- Biodesign Virginia G. Piper Center for Personalized Diagnostics, Arizona State University, Tempe, AZ, USA
| | - Richard Ehman
- Diagnostic Radiology, Mayo Clinic, Rochester, MN, USA
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22
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Rujchanarong D, Scott D, Park Y, Brown S, Mehta AS, Drake R, Sandusky GE, Nakshatri H, Angel PM. Metabolic Links to Socioeconomic Stresses Uniquely Affecting Ancestry in Normal Breast Tissue at Risk for Breast Cancer. Front Oncol 2022; 12:876651. [PMID: 35832545 PMCID: PMC9273232 DOI: 10.3389/fonc.2022.876651] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
A primary difference between black women (BW) and white women (WW) diagnosed with breast cancer is aggressiveness of the tumor. Black women have higher mortalities with similar incidence of breast cancer compared to other race/ethnicities, and they are diagnosed at a younger age with more advanced tumors with double the rate of lethal, triple negative breast cancers. One hypothesis is that chronic social and economic stressors result in ancestry-dependent molecular responses that create a tumor permissive tissue microenvironment in normal breast tissue. Altered regulation of N-glycosylation of proteins, a glucose metabolism-linked post-translational modification attached to an asparagine (N) residue, has been associated with two strong independent risk factors for breast cancer: increased breast density and body mass index (BMI). Interestingly, high body mass index (BMI) levels have been reported to associate with increases of cancer-associated N-glycan signatures. In this study, we used matrix assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) to investigate molecular pattern changes of N-glycosylation in ancestry defined normal breast tissue from BW and WW with significant 5-year risk of breast cancer by Gail score. N-glycosylation was tested against social stressors including marital status, single, education, economic status (income), personal reproductive history, the risk factors BMI and age. Normal breast tissue microarrays from the Susan G. Komen tissue bank (BW=43; WW= 43) were used to evaluate glycosylation against socioeconomic stress and risk factors. One specific N-glycan (2158 m/z) appeared dependent on ancestry with high sensitivity and specificity (AUC 0.77, Brown/Wilson p-value<0.0001). Application of a linear regression model with ancestry as group variable and socioeconomic covariates as predictors identified a specific N-glycan signature associated with different socioeconomic stresses. For WW, household income was strongly associated to certain N-glycans, while for BW, marital status (married and single) was strongly associated with the same N-glycan signature. Current work focuses on understanding if combined N-glycan biosignatures can further help understand normal breast tissue at risk. This study lays the foundation for understanding the complexities linking socioeconomic stresses and molecular factors to their role in ancestry dependent breast cancer risk.
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Affiliation(s)
- Denys Rujchanarong
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston, SC, United States
| | - Danielle Scott
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston, SC, United States
| | - Yeonhee Park
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States
| | - Sean Brown
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston, SC, United States
| | - Anand S. Mehta
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston, SC, United States
| | - Richard Drake
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston, SC, United States
| | - George E. Sandusky
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Harikrishna Nakshatri
- Department of Surgery, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Peggi M. Angel
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Bruker-MUSC Center of Excellence, Clinical Glycomics, Medical University of South Carolina, Charleston, SC, United States
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23
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Abstract
Cancer is a complex disease and a significant cause of mortality worldwide. Over the course of nearly all cancer types, collagen within the tumor microenvironment influences emergence, progression, and metastasis. This review discusses collagen regulation within the tumor microenvironment, pathological involvement of collagen, and predictive values of collagen and related extracellular matrix components in main cancer types. A survey of predictive tests leveraging collagen assays using clinical cohorts is presented. A conclusion is that collagen has high predictive value in monitoring cancer processes and stratifying by outcomes. New approaches should be considered that continue to define molecular facets of collagen related to cancer.
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24
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Martinez KA, Deshpande A, Lipold L, Rothberg MB. Change in individual physicians' screening mammography completion rates following the updated USPSTF guideline supporting shared decision making: An observational cohort study. PATIENT EDUCATION AND COUNSELING 2022; 105:166-172. [PMID: 33992485 DOI: 10.1016/j.pec.2021.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 04/23/2021] [Accepted: 05/05/2021] [Indexed: 06/12/2023]
Abstract
OBJECTIVE To understand changes in physician screening practices in response to the 2009 U.S. Preventive Services Task Force recommendation supporting shared decision making (SDM) for mammography in women aged 40-49 years. METHODS We assessed screening completion rates for physicians in the Cleveland Clinic Health System pre-2009 (2006-2008) and post-2009 (2010-2015), and rates for physicians new to the system post-2009. We used mixed effects logistic regression to estimate the odds of a woman receiving screening post-2009. If physicians practiced SDM, we hypothesized their screening rates would change after 2009. To test this, we included each physician's pre-2009 screening rate as a predictor in the model. RESULTS Among 125 physicians, the screening rate increased from 40% to 45% from pre-2009 to post-2009. For physicians new to the health system post-2009 the rate was 32%. In the mixed effects model (N = 17,007), the strongest predictor of mammography receipt among patients post-2009 was their physician's pre-2009 screening rate (aOR:3.57 per 10% increase in pre-2009 rate; 95%CI:1.69-7.50). CONCLUSIONS Whether a woman received a mammogram post-2009 was highly associated with her physicians' pre-2009 screening rate, suggesting physicians are not individualizing screening decisions via SDM. PRACTICE IMPLICATIONS Physicians may need support to effectively practice SDM.
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Affiliation(s)
- Kathryn A Martinez
- Cleveland Clinic Center for Value-Based Care Research, 9500 Euclid Ave, G10, Cleveland, OH 44195, USA.
| | - Abhishek Deshpande
- Cleveland Clinic Center for Value-Based Care Research, 9500 Euclid Ave, G10, Cleveland, OH 44195, USA
| | - Laura Lipold
- Cleveland Clinic, Family Medicine, 26900 Cedar Rd, Beachwood, OH 44122, USA
| | - Michael B Rothberg
- Cleveland Clinic Center for Value-Based Care Research, 9500 Euclid Ave, G10, Cleveland, OH 44195, USA
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25
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Yu T, Ye DM. The epidemiologic factors associated with breast density: A review. JOURNAL OF RESEARCH IN MEDICAL SCIENCES 2022; 27:53. [PMID: 36092490 PMCID: PMC9450246 DOI: 10.4103/jrms.jrms_962_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 01/14/2022] [Accepted: 01/26/2022] [Indexed: 11/04/2022]
Abstract
In recent years, some studies have evaluated the epidemiologic factors associated with breast density. However, the variant and inconsistent results exist. In addition, breast density has been proved to be a significant risk factor associated with breast cancer. Our review summarized the published studies and emphasized the crucial factors including epidemiological factors associated with breast density. In addition, we also discussed the potential reasons for the discrepant results with risk factors. To decrease the incidence and mortality rates for breast cancer, in clinical practice, breast density should be included for clinical risk models in addition to epidemiological factors, and physicians should get more concentrate on those women with risk factors and provide risk-based breast cancer screening regimens.
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26
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Shen C, Klein RW, Moss JL, Dodge DG, Chetlen AL, Stahl KA, Zhou S, Leslie DL, Ruffin MT, Lengerich EJ. Association Between Dense Breast Legislation and Cancer Stage at Diagnosis. Am J Prev Med 2021; 61:890-899. [PMID: 34376293 DOI: 10.1016/j.amepre.2021.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 05/17/2021] [Accepted: 05/20/2021] [Indexed: 11/26/2022]
Abstract
INTRODUCTION Many states have mandated breast density notification and insurance coverage for additional screening; yet, the association between such legislation and stage of diagnosis for breast cancer is unclear. This study investigates this association and examines the differential impacts among different age and race/ethnicity subgroups. METHODS The Surveillance, Epidemiology, and End Results database was queried to identify patients with breast cancer aged 40-74 years diagnosed between 2005 and 2016. Using a difference-in-differences multinomial logistic model, the odds of being diagnosed at different stages of cancer relative to the localized stage depending on legislation and individual characteristics were examined. Analyses were conducted in 2020-2021. RESULTS The study included 689,641 cases. Overall, the impact of notification legislation was not significant, whereas insurance coverage legislation was associated with 6% lower odds (OR=0.94, 95% CI=0.91, 0.96) of being diagnosed at the regional stage. The association between insurance coverage legislation and stage of diagnosis was even stronger among women aged 40-49 years, with 11% lower odds (OR=0.89, 95% CI=0.82, 0.96) of being diagnosed at the regional stage and 12% lower odds (OR=0.88, 95% CI=0.81, 0.96) of being diagnosed at the distant stage. Hispanic women benefited from notification laws, with 11% lower odds (OR=0.89, 95% CI=0.82, 0.97) of being diagnosed at distant stage. Neither notification nor supplemental screening insurance coverage legislation showed a substantial impact on Black women. CONCLUSIONS The findings imply that improving insurance coverage is more important than being notified overall. Raising awareness is important among Hispanic women; improving communication about dense breasts and access to screening might be more important than legislation among Black women.
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Affiliation(s)
- Chan Shen
- Department of Surgery, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Division of Health Services and Behavioral Research, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Penn State Cancer Institute, Hershey, Pennsylvania.
| | - Roger W Klein
- Department of Economics, Rutgers University, New Brunswick, New Jersey
| | - Jennifer L Moss
- Division of Health Services and Behavioral Research, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Penn State Cancer Institute, Hershey, Pennsylvania; Department of Family and Community Medicine, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Daleela G Dodge
- Department of Surgery, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Penn State Cancer Institute, Hershey, Pennsylvania
| | - Alison L Chetlen
- Penn State Cancer Institute, Hershey, Pennsylvania; Department of Radiology, Milton S. Hershey Medical Center, Penn State Health, Hershey, Pennsylvania
| | - Kelly A Stahl
- Department of Surgery, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Shouhao Zhou
- Division of Health Services and Behavioral Research, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Penn State Cancer Institute, Hershey, Pennsylvania; Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Douglas L Leslie
- Division of Health Services and Behavioral Research, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Mack T Ruffin
- Penn State Cancer Institute, Hershey, Pennsylvania; Department of Family and Community Medicine, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania
| | - Eugene J Lengerich
- Division of Health Services and Behavioral Research, Department of Public Health Sciences, Penn State College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania; Penn State Cancer Institute, Hershey, Pennsylvania
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27
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Holmes MD, Peng C. Vitamin A: A Potential Intervention for Breast Cancer Racial Disparities. J Nutr 2021; 151:3602-3603. [PMID: 34753173 PMCID: PMC8643599 DOI: 10.1093/jn/nxab359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
| | - Cheng Peng
- Channing Division of Network Medicine, Harvard Medical School, Boston, MA, USA
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28
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Risk factors for breast cancer subtypes among Black women undergoing screening mammography. Breast Cancer Res Treat 2021; 189:827-835. [PMID: 34342765 DOI: 10.1007/s10549-021-06340-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/24/2021] [Indexed: 01/16/2023]
Abstract
PURPOSE Black women are more likely than non-Hispanic White women to be diagnosed with triple negative breast cancer (TNBC), an aggressive subtype with limited treatment options. The study objective was to evaluate the associations of known breast cancer risk factors, including breast density, with TNBC among Black women. METHODS This study included Black women who underwent screening mammography between the ages of 40-84 years at a University of Pennsylvania Health System between 2010 and 2015. Cox proportional hazard models using multiple imputation with chained equations were used to estimate hazard ratios and 95% confidence intervals for risk factors for ER/PR+/HER2- and TNBC. RESULTS Among 25,013 Black women, there were 330 incident breast cancers (1.3%) during a mean follow-up of 5.8 years; 218 (66.1%) ER/PR+ HER- and 61 (18.1%) TNBC. Having dense breasts (heterogeneously dense or extremely dense) vs. non-dense breasts (almost entirely fatty or scattered areas of fibroglandular density) increased risk of ER/PR+/HER2- breast cancer almost 80% (HR 1.79, 95% CI 1.32-2.43) and TNBC more than twofold (HR 2.53, 1.45-4.44). Older age was associated with an increased risk for ER/PR+/HER2- (HR 1.04, 1.03-1.06) and TNBC (HR 1.03, 1.00-1.05). Having a BMI of > 30 kg/m2 was associated with an increased risk (HR 2.77, 1.05-7.30) for TNBC and an increased risk of ERPR+/HER2- breast cancer in postmenopausal but not pre-menopausal women (p-interaction = 0.016). CONCLUSION Our results suggest that breast density and obesity are strong risk factors for TNBC among Black women. Understanding breast cancer subtype specific risk factors among Black women can help improve risk assessment.
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29
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Maghsoudi OH, Gastounioti A, Scott C, Pantalone L, Wu FF, Cohen EA, Winham S, Conant EF, Vachon C, Kontos D. Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment. Med Image Anal 2021; 73:102138. [PMID: 34274690 PMCID: PMC8453099 DOI: 10.1016/j.media.2021.102138] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 04/29/2021] [Accepted: 06/16/2021] [Indexed: 02/06/2023]
Abstract
Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast cancer screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast density from digital mammograms. Our method leverages deep learning using two convolutional neural network architectures to accurately segment the breast area. An AI algorithm combining superpixel generation and radiomic machine learning is then applied to differentiate dense from non-dense tissue regions within the breast, from which breast density is estimated. Our method was trained and validated on a multi-racial, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent matched case-control dataset of 6368 digital mammograms (414 cases; 1178 controls) for both breast density estimation and case-control discrimination. On the independent dataset, breast percent density (PD) estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, in a model adjusted for age and BMI, Deep-LIBRA yielded a higher case-control discrimination performance (area under the ROC curve, AUC = 0.612 [95% confidence interval (CI): 0.584, 0.640]) compared to four other widely-used research and commercial breast density assessment methods (AUCs = 0.528 to 0.599). Our results suggest a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods.
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Affiliation(s)
- Omid Haji Maghsoudi
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA,
| | - Aimilia Gastounioti
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Christopher Scott
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Lauren Pantalone
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Fang-Fang Wu
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Eric A. Cohen
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Stacey Winham
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Emily F. Conant
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA
| | - Celine Vachon
- Department of Health Sciences Research, Mayo Clinic, Rochester, 55905, MN, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, Philadelphia, 19104, PA, USA,
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30
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Austin JD, Agovino M, Rodriguez CB, Terry MB, Shelton RC, Wei Y, Desperito E, Schmitt KM, Kukafka R, Tehranifar P. Breast Density Awareness and Knowledge in a Mammography Screening Cohort of Predominantly Hispanic Women: Does Breast Density Notification Matter? Cancer Epidemiol Biomarkers Prev 2021; 30:1913-1920. [PMID: 34348958 DOI: 10.1158/1055-9965.epi-21-0172] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/10/2021] [Accepted: 07/29/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND New York State law mandates that women with dense breasts receive a written notification of their breast density (BD) and its implications, but data on the impact of dense breast notification (DBN) on BD awareness and knowledge in diverse populations remain limited. METHODS Between 2016 and 2018, we collected survey and mammographic data from 666 women undergoing screening mammography in New York City (ages 40-60, 80% Hispanic, 69% Spanish-speaking) to examine the impact of prior DBN on BD awareness by sociodemographic and breast cancer risk factors, and describe BD knowledge by sources of information. RESULTS Only 24.8% of the overall sample and 34.9% of women receiving DBN had BD awareness. In multivariable models adjusting for DBN, awareness was significantly lower in women who were Spanish-speaking [OR, 0.16; 95% confidence interval (CI), 0.09-0.30 vs. English speakers], were foreign-born (OR, 0.31; 95% CI, 0.16-0.58 vs. U.S.-born), and had lower educational attainment (e.g., high school degree or less; OR, 0.14; 95% CI, 0.08-0.26 vs. college or higher degree). Women receiving DBN were more likely to be aware of BD (OR, 2.61; 95% CI, 1.59-4.27) but not more knowledgeable about the impact of BD on breast cancer risk and detection. However, women reporting additional communication about their BD showed greater knowledge in these areas. CONCLUSIONS DBN increases BD awareness disproportionately across sociodemographic groups. IMPACT Efforts to improve communication of DBN must focus on addressing barriers in lower socioeconomic and racially and ethnically diverse women, including educational and language barriers.
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Affiliation(s)
- Jessica D Austin
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York.,Department of Sociomedical Sciences, Columbia University Mailman School of Public Health, New York, New York
| | - Mariangela Agovino
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York
| | - Carmen B Rodriguez
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York
| | - Mary Beth Terry
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York.,Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York
| | - Rachel C Shelton
- Department of Sociomedical Sciences, Columbia University Mailman School of Public Health, New York, New York.,Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York
| | - Ying Wei
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York
| | - Elise Desperito
- Department of Radiology, Columbia University Irving Medical Center, New York, New York
| | - Karen M Schmitt
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York.,Division of Academics, Columbia University School of Nursing, New York, New York
| | - Rita Kukafka
- Department of Sociomedical Sciences, Columbia University Mailman School of Public Health, New York, New York.,Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York.,Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York
| | - Parisa Tehranifar
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York. .,Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York
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31
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Abstract
ABSTRACT Triple-negative breast cancer accounted for 12% of breast cancers diagnosed in the United States from 2012 to 2016, with a 5-year survival 8% to 16% lower than hormone receptor-positive disease. However, preventive and screening strategies remain tailored to the demographics of less lethal luminal cancers. This review examines the ethnic, genetic, and modifiable risk factors associated with triple-negative breast cancer, which providers must recognize to address the societal disparities of this deadly disease. Most notable is that triple-negative cancers disproportionately affect African American women and carriers of germline BRCA and PALB2 mutations. Even controlling for treatment delays, stage, and socioeconomic factors, African Americans with triple-negative breast cancer remain nearly twice as likely to die of their disease. To level the playing field, we must integrate genomic predictors of disease and epidemiologic characteristics of molecular breast cancer subtypes to provide personalized risk assessment, screening, and treatment for each patient.
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32
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Henderson LM, Marsh MW, Earnhardt K, Pritchard M, Benefield TS, Agans R, Lee SS. Understanding the response of mammography facilities to breast density notification. Cancer 2020; 126:5230-5238. [PMID: 32926413 PMCID: PMC7944399 DOI: 10.1002/cncr.33198] [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: 06/15/2020] [Revised: 07/31/2020] [Accepted: 08/14/2020] [Indexed: 11/10/2022]
Abstract
BACKGROUND State-specific breast density notification legislation requires that women undergoing mammography be informed about breast density, with variation among states. Because mammography facilities are among the main points of contact for women undergoing mammography, research is needed to understand how facilities communicate information on breast density, cancer risk, and supplemental screening to women. METHODS A cross-sectional, 50-item, mailed survey of 156 American College of Radiology-certified mammography facilities in North Carolina was conducted in 2017 via the Tailored Design Method. Breast density notification practices, supplemental screening services, and patient educational materials were compared by supplemental screening availability via t tests and chi-square tests. RESULTS All responding facilities (n = 94; 60.3% response rate) notified women of their breast density in the mammography results letter. Breast cancer risk assessments were performed by 36.2% of the facilities, with risk information communicated in the final radiology report for the referring provider to discuss with the woman (79.4%) or in the results letter (58.8%). Supplemental breast cancer screening was offered by 63.8% of the facilities, with use based on multiple factors, including recommendations from the referring physician (63.3%) or reading radiologist (63.3%), breast density (48.3%), other risk factors (48.3%), and patient request (40.0%). Although 75.0% of the facilities offered breast density educational materials, only 36.6% offered educational materials on supplemental screening. CONCLUSIONS In a state with a breast density notification law, mammography facilities communicate breast density, cancer risk, and supplemental screening information to women through various approaches. When supplemental screening is offered, facilities use multiple decision-making criteria rather than breast density alone.
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Affiliation(s)
| | - Mary W. Marsh
- Radiology Department, University of North Carolina, Chapel Hill, NC
| | | | | | | | - Robert Agans
- Biostatistics Department, University of North Carolina, Chapel Hill, NC
| | - Sheila S. Lee
- Radiology Department, University of North Carolina, Chapel Hill, NC
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33
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Wernli KJ, Bowles EA, Knerr S, Leppig KA, Ehrlich K, Gao H, Schwartz MD, O’Neill SC. Characteristics Associated with Participation in ENGAGED 2 - A Web-based Breast Cancer Risk Communication and Decision Support Trial. Perm J 2020; 24:1-4. [PMID: 33482952 PMCID: PMC7849258 DOI: 10.7812/tpp/19.205] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 10/14/2020] [Accepted: 03/01/2020] [Indexed: 11/30/2022]
Abstract
PURPOSE We evaluated demographic and clinical characteristics associated with participation in a clinical trial testing the efficacy of an online tool to support breast cancer risk communication and decision support for risk mitigation to determine the generalizability of trial results. METHODS Eligible women were members of Kaiser Permanente Washington aged 40-69 years with a recent normal screening mammogram, heterogeneously or extremely dense breasts and a calculated risk of > 1.67% based on the Breast Cancer Surveillance Consortium 5-year breast cancer risk model. Trial outcomes were chemoprevention and breast magnetic resonance imaging by 12-months post-baseline. Women were recruited via mail with phone follow-up using plain language materials notifying them of their density status and higher than average breast cancer risk. Multivariable logistic regression calculated independent odds ratios (ORs) for associations between demographic and clinical characteristics with trial participation. RESULTS Of 2,569 eligible women contacted, 995 (38.7%) participated. Women with some college (OR = 1.99, 95% confidence interval [CI] 1.34-2.96) or college degree (OR = 3.35, 95% CI 2.29-4.90) were more likely to participate than high school-educated women. Race/ethnicity also was associated with participation (African-American OR = 0.50, 95% CI 0.29-0.87; Asian OR = 0.22, 95% CI 0.12-0.41). Multivariate adjusted ORs for family history of breast/ovarian cancer were not associated with trial participation. DISCUSSION Use of plain language and potential access to a website providing personal breast cancer risk information and education were insufficient in achieving representative participation in a breast cancer prevention trial. Additional methods of targeting and tailoring, potentially facilitated by clinical and community outreach, are needed to facilitate equitable engagement for all women.
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Affiliation(s)
- Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Erin A Bowles
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | | | | | - Kelly Ehrlich
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Hongyuan Gao
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Marc D Schwartz
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC
| | - Suzanne C O’Neill
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC
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Adedigba JA, Idowu BM, Hermans SP, Ibitoye BO, Pahwa S. Relationship between Mammographic Findings and Breast Abnormalities in a Nigerian Population. J Natl Med Assoc 2020; 113:77-87. [PMID: 32768243 DOI: 10.1016/j.jnma.2020.07.012] [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: 02/10/2020] [Revised: 07/05/2020] [Accepted: 07/11/2020] [Indexed: 11/26/2022]
Abstract
PURPOSE This study aims to describe the mammographic findings in a population of Nigerian women and to explore the relationships between abnormal mammographic findings, breast malignancy, and breast composition. METHODOLOGY This was a retrospective study of consecutive mammograms carried out at Union Diagnostics and Clinical Services in Lagos, Nigeria from 2016 to 2018. Demographic information, indications for and findings on mammographic evaluation were obtained. A logistic regression fit model was used to establish the correlation between mammographic findings, breast density, and suspicion for breast malignancy (higher BIRADS scores). P ≤ 0.05 represented a statistically significant result. RESULTS A total of 304 patients were involved in this study (age range 20-80 years, mean age 49.0 ± 10.5 years). The patients between 40 and 49 years formed the largest age group with 128 patients (42.4%). Most patients were referred for a breast mass/lump (115/304-38.6%); 56 patients (18.8%) presenting for routine screening. The most common finding on the mammograms was BIRADS 4 in both breasts in 96 patients (31.6%). Most patients had heterogeneous breast density (195 patients - 64.1%). Multivariate logistic regression analysis showed a significant correlation between history of mass, poorly defined margins, and suspicion of malignancy. There was no statistically significant association between abnormal mammographic findings and higher breast density. CONCLUSION Poorly defined margins were positively correlated with BIRADS ratings suspicious for malignancy. The presence of a breast mass was positively correlated with a higher BIRADS score when other possible cofounding variables were not accounted for. Patient age did not correlate with breast density in this study.
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Affiliation(s)
| | - Bukunmi M Idowu
- Union Diagnostics and Clinical Services, Lagos 220005, Nigeria
| | - Sarah P Hermans
- Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Bolanle O Ibitoye
- Department of Radiology, Obafemi Awolowo University, Ile-Ife 220005, Nigeria
| | - Shivani Pahwa
- Department of Radiology, University Hospital Cleveland Medical Center, Cleveland, OH, USA
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Bissell MCS, Kerlikowske K, Sprague BL, Tice JA, Gard CC, Tossas KY, Rauscher GH, Trentham-Dietz A, Henderson LM, Onega T, Keegan THM, Miglioretti DL. Breast Cancer Population Attributable Risk Proportions Associated with Body Mass Index and Breast Density by Race/Ethnicity and Menopausal Status. Cancer Epidemiol Biomarkers Prev 2020; 29:2048-2056. [PMID: 32727722 DOI: 10.1158/1055-9965.epi-20-0358] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/01/2020] [Accepted: 07/22/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Overweight/obesity and dense breasts are strong breast cancer risk factors whose prevalences vary by race/ethnicity. The breast cancer population attributable risk proportions (PARP) explained by these factors across racial/ethnic groups are unknown. METHODS We analyzed data collected from 3,786,802 mammography examinations (1,071,653 women) in the Breast Cancer Surveillance Consortium, associated with 21,253 invasive breast cancers during a median of 5.2 years follow-up. HRs for body mass index (BMI) and breast density, adjusted for age and registry were estimated using separate Cox regression models by race/ethnicity (White, Black, Hispanic, Asian) and menopausal status. HRs were combined with observed risk-factor proportions to calculate PARPs for shifting overweight/obese to normal BMI and shifting heterogeneously/extremely dense to scattered fibroglandular densities. RESULTS The prevalences and HRs for overweight/obesity and heterogeneously/extremely dense breasts varied across races/ethnicities and menopausal status. BMI PARPs were larger for postmenopausal versus premenopausal women (12.0%-28.3% vs. 1.0%-9.9%) and nearly double among postmenopausal Black women (28.3%) than other races/ethnicities (12.0%-15.4%). Breast density PARPs were larger for premenopausal versus postmenopausal women (23.9%-35.0% vs. 13.0%-16.7%) and lower among premenopausal Black women (23.9%) than other races/ethnicities (30.4%-35.0%). Postmenopausal density PARPs were similar across races/ethnicities (13.0%-16.7%). CONCLUSIONS Overweight/obesity and dense breasts account for large proportions of breast cancers in White, Black, Hispanic, and Asian women despite large differences in risk-factor distributions. IMPACT Risk prediction models should consider how race/ethnicity interacts with BMI and breast density. Efforts to reduce BMI could have a large impact on breast cancer risk reduction, particularly among postmenopausal Black women.
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Affiliation(s)
- Michael C S Bissell
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis, California.
| | - Karla Kerlikowske
- General Internal Medicine Section, Department of Veteran Affairs and Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California
| | - Brian L Sprague
- Department of Surgery, Office of Health Promotion Research, Larner College of Medicine at the University of Vermont and University of Vermont Cancer Center, Burlington, Vermont
| | - Jeffery A Tice
- Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California
| | - Charlotte C Gard
- Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, New Mexico
| | - Katherine Y Tossas
- Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University, Richmond, Virginia
| | - Garth H Rauscher
- Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, Illinois
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin
| | - Louise M Henderson
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Tracy Onega
- Department of Biomedical Data Science, Dartmouth College, Lebanon, New Hampshire
| | - Theresa H M Keegan
- Center for Oncology Hematology Outcomes Research and Training (COHORT) and Division of Hematology and Oncology, University of California Davis School of Medicine, Sacramento, California
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Sheller MJ, Edwards B, Reina GA, Martin J, Pati S, Kotrotsou A, Milchenko M, Xu W, Marcus D, Colen RR, Bakas S. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep 2020; 10:12598. [PMID: 32724046 PMCID: PMC7387485 DOI: 10.1038/s41598-020-69250-1] [Citation(s) in RCA: 265] [Impact Index Per Article: 66.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/23/2020] [Indexed: 12/15/2022] Open
Abstract
Several studies underscore the potential of deep learning in identifying complex patterns, leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse datasets, required for training, is a significant challenge in medicine and can rarely be found in individual institutions. Multi-institutional collaborations based on centrally-shared patient data face privacy and ownership challenges. Federated learning is a novel paradigm for data-private multi-institutional collaborations, where model-learning leverages all available data without sharing data between institutions, by distributing the model-training to the data-owners and aggregating their results. We show that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and evaluate generalizability on data from institutions outside the federation. We further investigate the effects of data distribution across collaborating institutions on model quality and learning patterns, indicating that increased access to data through data private multi-institutional collaborations can benefit model quality more than the errors introduced by the collaborative method. Finally, we compare with other collaborative-learning approaches demonstrating the superiority of federated learning, and discuss practical implementation considerations. Clinical adoption of federated learning is expected to lead to models trained on datasets of unprecedented size, hence have a catalytic impact towards precision/personalized medicine.
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Affiliation(s)
- Micah J Sheller
- Intel Corporation, 2200 Mission College Blvd., Santa Clara, CA, 95052, USA
| | - Brandon Edwards
- Intel Corporation, 2200 Mission College Blvd., Santa Clara, CA, 95052, USA
| | - G Anthony Reina
- Intel Corporation, 2200 Mission College Blvd., Santa Clara, CA, 95052, USA
| | - Jason Martin
- Intel Corporation, 2200 Mission College Blvd., Santa Clara, CA, 95052, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | - Aikaterini Kotrotsou
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX, 77030, USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, 1881 East Rd, 3SCRB4, Houston, TX, 77054, USA
| | - Mikhail Milchenko
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Weilin Xu
- Intel Corporation, 2200 Mission College Blvd., Santa Clara, CA, 95052, USA
| | - Daniel Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Rivka R Colen
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX, 77030, USA
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, 1881 East Rd, 3SCRB4, Houston, TX, 77054, USA
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, 15232, USA
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA.
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Hiatt RA, Engmann NJ, Balke K, Rehkopf DH. A Complex Systems Model of Breast Cancer Etiology: The Paradigm II Conceptual Model. Cancer Epidemiol Biomarkers Prev 2020; 29:1720-1730. [PMID: 32641370 DOI: 10.1158/1055-9965.epi-20-0016] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 03/09/2020] [Accepted: 06/04/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The etiology of breast cancer is a complex system of interacting factors from multiple domains. New knowledge about breast cancer etiology continues to be produced by the research community, and the communication of this knowledge to other researchers, practitioners, decision makers, and the public is a challenge. METHODS We updated the previously published Paradigm model (PMID: 25017248) to create a framework that describes breast cancer etiology in four overlapping domains of biologic, behavioral, environmental, and social determinants. This new Paradigm II conceptual model was part of a larger modeling effort that included input from multiple experts in fields from genetics to sociology, taking a team and transdisciplinary approach to the common problem of describing breast cancer etiology for the population of California women in 2010. Recent literature was reviewed with an emphasis on systematic reviews when available and larger epidemiologic studies when they were not. Environmental chemicals with strong animal data on etiology were also included. RESULTS The resulting model illustrates factors with their strength of association and the quality of the available data. The published evidence supporting each relationship is made available herein, and also in an online dynamic model that allows for manipulation of individual factors leading to breast cancer (https://cbcrp.org/causes/). CONCLUSIONS The Paradigm II model illustrates known etiologic factors in breast cancer, as well as gaps in knowledge and areas where better quality data are needed. IMPACT The Paradigm II model can be a stimulus for further research and for better understanding of breast cancer etiology.
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Affiliation(s)
- Robert A Hiatt
- Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, California. .,Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
| | | | - Kaya Balke
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California
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Gastounioti A, Kasi CD, Scott CG, Brandt KR, Jensen MR, Hruska CB, Wu FF, Norman AD, Conant EF, Winham SJ, Kerlikowske K, Kontos D, Vachon CM. Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction. Radiology 2020; 296:24-31. [PMID: 32396041 DOI: 10.1148/radiol.2020192509] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD (r = 0.77-0.84) and Volpara VPD (r = 0.85-0.90) (P < .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; P = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; P = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; P = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020 Online supplemental material is available for this article.
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Affiliation(s)
- Aimilia Gastounioti
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Christine Damases Kasi
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Christopher G Scott
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Kathleen R Brandt
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Matthew R Jensen
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Carrie B Hruska
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Fang F Wu
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Aaron D Norman
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Emily F Conant
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Stacey J Winham
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Karla Kerlikowske
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Despina Kontos
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
| | - Celine M Vachon
- From the Department of Radiology, University of Pennsylvania, Philadelphia, Pa (A.G., E.F.C., D.K.); Department of Radiology, University of Minnesota, Minneapolis, Minn (C.D.K.); Departments of Health Sciences Research (C.G.S., M.R.J., A.D.N., S.J.W., C.M.V.), Diagnostic Radiology (K.R.B., C.B.H.), Information Technology (F.F.W.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Department of Medicine and Epidemiology, University of California, San Francisco, San Francisco, Calif (K.K.)
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Borges VF, Lyons TR, Germain D, Schedin P. Postpartum Involution and Cancer: An Opportunity for Targeted Breast Cancer Prevention and Treatments? Cancer Res 2020; 80:1790-1798. [PMID: 32075799 PMCID: PMC8285071 DOI: 10.1158/0008-5472.can-19-3448] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 01/24/2020] [Accepted: 02/12/2020] [Indexed: 12/24/2022]
Abstract
Childbirth at any age confers a transient increased risk for breast cancer in the first decade postpartum and this window of adverse effect extends over two decades in women with late-age first childbirth (>35 years of age). Crossover to the protective effect of pregnancy is dependent on age at first pregnancy, with young mothers receiving the most benefit. Furthermore, breast cancer diagnosis during the 5- to 10-year postpartum window associates with high risk for subsequent metastatic disease. Notably, lactation has been shown to be protective against breast cancer incidence overall, with varying degrees of protection by race, multiparity, and lifetime duration of lactation. An effect for lactation on breast cancer outcome after diagnosis has not been described. We discuss the most recent data and mechanistic insights underlying these epidemiologic findings. Postpartum involution of the breast has been identified as a key mediator of the increased risk for metastasis in women diagnosed within 5-10 years of a completed pregnancy. During breast involution, immune avoidance, increased lymphatic network, extracellular matrix remodeling, and increased seeding to the liver and lymph node work as interconnected pathways, leading to the adverse effect of a postpartum diagnosis. We al discuss a novel mechanism underlying the protective effect of breastfeeding. Collectively, these mechanistic insights offer potential therapeutic avenues for the prevention and/or improved treatment of postpartum breast cancer.
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Affiliation(s)
- Virginia F Borges
- Young Women's Breast Cancer Translational Program, University of Colorado Cancer Center, Aurora, Colorado.
- Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Traci R Lyons
- Young Women's Breast Cancer Translational Program, University of Colorado Cancer Center, Aurora, Colorado
- Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Doris Germain
- Tisch Cancer Institute, Division of Hematology/Oncology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Pepper Schedin
- Young Women's Breast Cancer Translational Program, University of Colorado Cancer Center, Aurora, Colorado.
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, Portland, Oregon
- Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon
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Gunn C, Maschke A, Bickmore T, Kennedy M, Hopkins MF, Fishman MDC, Paasche-Orlow MK, Warner ET. Acceptability of an Interactive Computer-Animated Agent to Promote Patient-Provider Communication About Breast Density: a Mixed Method Pilot Study. J Gen Intern Med 2020; 35:1069-1077. [PMID: 31919723 PMCID: PMC7174461 DOI: 10.1007/s11606-019-05622-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 12/13/2019] [Indexed: 01/04/2023]
Abstract
BACKGROUND Half of women undergoing mammography have dense breasts. Mandatory dense breast notification and educational materials have been shown to confuse women, rather than empower them. OBJECTIVE This study used a mixed method, multi-stakeholder approach to assess acceptability of an interactive, computer-animated agent that provided breast density information to women and changes in knowledge, satisfaction, and informational needs. DESIGN A pre-post survey and qualitative focus groups assessed the acceptability of the computer-animated agent among women. An anonymous, online survey measuring acceptability was delivered to a multi-stakeholder group. PARTICIPANTS English-speaking, mammography-eligible women ages 40-74 were invited and 44 women participated in one of nine focus groups. In addition, 14 stakeholders representing primary care, radiology, patient advocates, public health practitioners, and researchers completed the online survey. INTERVENTIONS A prototype of a computer-animated agent was delivered to women in a group setting; stakeholders viewed the prototype independently. MAIN MEASURES Data collected included open-ended qualitative questions that guided discussion about the content and form of the computer-animated agent. Structured surveys included domains related to knowledge, acceptability, and satisfaction. Stakeholder acceptability was measured with a series of statements about aspects of the intervention and delivery approach and are reported as the proportion of respondents who endorsed each statement. KEY RESULTS Six of 12 knowledge items demonstrated improvement post-intervention, satisfaction with the agent was high (81%), but the number of unanswered questions did not improve (67% vs. 54%, p = 0.37). Understanding of the distinction between connective and fatty tissue in the breast did not increase (30% vs. 26%, p = 0.48). Results of the multi-stakeholder survey suggest broad acceptability of the approach and agent. CONCLUSIONS Findings highlight the benefits of a brief interactive educational exposure as well as misperceptions that persisted. Results demonstrate the need for an evidence-based, accessible intervention that is easy to understand for patients.
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Affiliation(s)
- Christine Gunn
- Department of Medicine, Section of General Internal Medicine, Women's Health Unit, Boston University School of Medicine, 801 Massachusetts Avenue, First Floor, Boston, MA, 02118, USA.
| | - Ariel Maschke
- Department of Medicine, Section of General Internal Medicine, Women's Health Unit, Boston University School of Medicine, 801 Massachusetts Avenue, First Floor, Boston, MA, 02118, USA
| | - Timothy Bickmore
- Khoury College of Computer Sciences, Northeastern University, Boston, USA
| | | | | | - Michael D C Fishman
- Department of Radiology, Boston Medical Center, Section of Breast Imaging, Boston University School of Medicine, Boston, USA
| | - Michael K Paasche-Orlow
- Department of Medicine, Section of General Internal Medicine, Boston Medical Center, Boston University School of Medicine, Boston, USA
| | - Erica T Warner
- Department of Medicine, Massachusetts General Hospital, Boston, USA
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Moore JX, Han Y, Appleton C, Colditz G, Toriola AT. Determinants of Mammographic Breast Density by Race Among a Large Screening Population. JNCI Cancer Spectr 2020; 4:pkaa010. [PMID: 32373777 PMCID: PMC7192029 DOI: 10.1093/jncics/pkaa010] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 01/14/2020] [Accepted: 02/18/2020] [Indexed: 01/10/2023] Open
Abstract
Background Because of the mixed reports from smaller studies, we examined associations of race with mammographic breast density and evaluated racial differences in the determinants of breast density. Methods Participants included 37 839 women (23 166 non-Hispanic white and 14 673 African American) receiving screening mammograms at the Joanne Knight Breast Health Center at Washington University School of Medicine from June 2010 to December 2015. Mammographic breast density was assessed using the Breast Imaging Reporting and Data System (5th edition). To determine the association of race and participant characteristics with mammographic breast density, we used multivariable polytomous logistic regression models (reference group: almost entirely fatty). Results African American women had increased odds of extremely dense (adjusted odds ratio = 1.31, 95% confidence interval = 1.13 to 1.52) and reduced odds of heterogeneously dense breasts (adjusted odds ratio = 0.91, 95% confidence interval = 0.84 to 0.99) compared with non-Hispanic white women. Altogether, race, parity and age at first birth, current age, current body mass index (BMI), BMI at age 18 years, menarche, family history of breast cancer, oral contraceptive use, alcohol use, and menopausal status explained 33% of the variation in mammographic breast density. Among African American and non-Hispanic white women, these factors explained nearly 28.6% and 33.6% of the variation in mammographic density, respectively. Current BMI provided the greatest explanation of breast density (26.2% overall, 22.2% in African American, and 26.2% in non-Hispanic white women). Conclusions The determinants of mammographic breast density were generally similar between African American women and non-Hispanic white women. After adjustments for confounders, African Americans had higher likelihood of extremely dense breasts but lower likelihood of heterogeneously dense breasts. The greatest explanation of breast density was provided by BMI, regardless of race.
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Affiliation(s)
- Justin X Moore
- Division of Public Health Sciences, Department of Surgery, and Siteman Cancer Center at Washington University School of Medicine, St. Louis, MO, USA.,Division of Epidemiology, Department of Population Health Sciences, Augusta University, Augusta, GA, USA.,Institute of Public and Preventive Health, Augusta University, Augusta, GA, USA
| | - Yunan Han
- Division of Public Health Sciences, Department of Surgery, and Siteman Cancer Center at Washington University School of Medicine, St. Louis, MO, USA.,Department of Breast Surgery, First Hospital of China Medical University, Shenyang, Liaoning Province, 110001, China
| | - Catherine Appleton
- Breast Imaging Division, Mallinckrodt Institute of Radiology, Washington University in Saint Louis School of Medicine, St. Louis, MO, USA
| | - Graham Colditz
- Division of Public Health Sciences, Department of Surgery, and Siteman Cancer Center at Washington University School of Medicine, St. Louis, MO, USA
| | - Adetunji T Toriola
- Division of Public Health Sciences, Department of Surgery, and Siteman Cancer Center at Washington University School of Medicine, St. Louis, MO, USA
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Johnson HM, Shivalingappa H, Irish W, Wong JH, Muzaffar M, Verbanac K, Vohra NA. Race May Not Impact Endocrine Therapy-Related Changes in Breast Density. Cancer Epidemiol Biomarkers Prev 2020; 29:1049-1057. [PMID: 32098892 DOI: 10.1158/1055-9965.epi-19-1066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 12/03/2019] [Accepted: 02/21/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Reduction in breast density may be a biomarker of endocrine therapy (ET) efficacy. Our objective was to assess the impact of race on ET-related changes in volumetric breast density (VBD). METHODS This retrospective cohort study assessed longitudinal changes in VBD measures in women with estrogen receptor-positive invasive breast cancer treated with ET. VBD, the ratio of fibroglandular volume (FGV) to breast volume (BV), was measured using Volpara software. Changes in measurements were evaluated using a multivariable linear mixed effects model. RESULTS Compared with white women (n = 191), black women (n = 107) had higher rates of obesity [mean ± SD body mass index (BMI) 34.5 ± 9.1 kg/m2 vs. 30.6 ± 7.0 kg/m2, P < 0.001] and premenopausal status (32.7% vs. 16.7%, P = 0.002). Age- and BMI-adjusted baseline FGV, BV, and VBD were similar between groups. Modeled longitudinal changes were also similar: During a follow-up of 30.7 ± 15.0 months (mean ± SD), FGV decreased over time in premenopausal women (slope = -0.323 cm3; SE = 0.093; P = 0.001), BV increased overall (slope = 2.475 cm3; SE = 0.483; P < 0.0001), and VBD decreased (premenopausal slope = -0.063%, SE = 0.011; postmenopausal slope = -0.016%, SE = 0.004; P < 0.0001). Race was not significantly associated with these longitudinal changes, nor did race modify the effect of time on these changes. Higher BMI was associated with lower baseline VBD (P < 0.0001). Among premenopausal women, VBD declined more steeply for women with lower BMI (time × BMI, P = 0.0098). CONCLUSIONS Race does not appear to impact ET-related longitudinal changes in VBD. IMPACT Racial disparities in estrogen receptor-positive breast cancer recurrence and mortality may not be explained by differential declines in breast density due to ET.
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Affiliation(s)
- Helen M Johnson
- Department of Surgery, East Carolina University Brody School of Medicine, Greenville, North Carolina
| | - Hitesh Shivalingappa
- Department of Surgery, East Carolina University Brody School of Medicine, Greenville, North Carolina.,Department of Anesthesiology and Perioperative Medicine, Penn State Health Milton S. Hershey Medical Center, Hershey, Pennsylvania
| | - William Irish
- Department of Surgery, East Carolina University Brody School of Medicine, Greenville, North Carolina
| | - Jan H Wong
- Department of Surgery, East Carolina University Brody School of Medicine, Greenville, North Carolina
| | - Mahvish Muzaffar
- Division of Hematology Oncology, Department of Internal Medicine, East Carolina University Brody School of Medicine, Greenville, North Carolina
| | - Kathryn Verbanac
- Department of Surgery, East Carolina University Brody School of Medicine, Greenville, North Carolina
| | - Nasreen A Vohra
- Department of Surgery, East Carolina University Brody School of Medicine, Greenville, North Carolina.
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Breast density measured volumetrically in a clinical environment: cross-sectional study with photon counting technology. Breast Cancer Res Treat 2019; 179:755-762. [DOI: 10.1007/s10549-019-05502-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 11/15/2019] [Indexed: 10/25/2022]
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Mizukoshi MM, Hossain SZ, Poulos A. Mammographic Breast Density of Japanese Women Living in Australia: Implications for Breast Screening Policy. Asian Pac J Cancer Prev 2019; 20:2811-2817. [PMID: 31554381 PMCID: PMC6976818 DOI: 10.31557/apjcp.2019.20.9.2811] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Indexed: 01/14/2023] Open
Abstract
Background: Mammographic Breast Density (MBD) increases breast cancer risk, lowers sensitivity of mammography and is related to ethnicity. This study compared the MBD of Australian women living in Australia (AW), Japanese women living in Japan (JWJ) and Japan-born women living in Australia (JWA). The outcomes have implications for breast screening policies in Australia. Methods: In this cross-sectional study, mammographic images were collected from a total of 677 women who were examined at BreastScreen NSW or Miyata Hospital, Japan. The images were retrospectively evaluated using the 5th edition Breast Imaging-Reporting and Data System (BI-RADS) to assess the level of MBD. Descriptive analyses and binary logistic regressions were performed. Results: More than 65% of AW had low MBD while the majority of JWJ had high MBD. Among AW, low MBD was found in women aged 40-49 and 50-59. The MBD of JWA showed a similar pattern to JWJ but with a slightly lower MBD. The great majority of JWA aged 40-49 were found to have high MBD compared to women aged 50-59. JWJ were categorised as high MBD regardless of their age. JWA were 5 times more likely and JWJ were 15 times more likely to have high MBD compared to AW. Conclusion: Mammography screening becomes more effective for JW after migration to Australia. For JWA aged 40-49 years, supplemental screening options are indicated to provide better cancer detection. For AW, screening mammography could be equally as effective for AW aged 40-49 years as for those aged 50-59.
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Affiliation(s)
- Miwa M Mizukoshi
- Discipline of Behavioural and Social Sciences in Health, The University of Sydney, Australia. ,Discipline of Medical Radiation Sciences, The University of Sydney, Australia
| | - Syeda Z Hossain
- Discipline of Behavioural and Social Sciences in Health, The University of Sydney, Australia.
| | - Ann Poulos
- Discipline of Medical Radiation Sciences, The University of Sydney, Australia
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Rebolj M, Blyuss O, Chia KS, Duffy SW. Long-term excess risk of breast cancer after a single breast density measurement. Eur J Cancer 2019; 117:41-47. [PMID: 31229948 PMCID: PMC6658627 DOI: 10.1016/j.ejca.2019.05.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 05/02/2019] [Accepted: 05/03/2019] [Indexed: 12/20/2022]
Abstract
AIM Breast density is a risk factor for breast cancer. As density changes across a woman's life span, we studied for how long a single density measurement taken in (post-)menopausal women remains informative. METHODS We used data from Singaporean women who underwent a single mammography screen at age 50-64 years. For each case with breast cancer diagnosed at screening or in the subsequent 10 years, whether screen detected or diagnosed following symptoms, two age-matched controls were selected. We studied the excess risk of breast cancer, calculated as an odds ratio (OR) with conditional logistic regression and adjusted for body mass index, associated with 26-50% and with 51-100% density compared with ≤25% density by time since screening. RESULTS In total, 490 women had breast cancer, of which 361 were diagnosed because of symptoms after screening. Women with 51-100% breast density had an excess risk of breast cancer that did not seem to attenuate with time. In 1-3 years after screening, the OR was 2.22 (95% confidence interval [CI]: 1.07-4.61); in 4-6 years after screening, the OR was 4.09 (95% CI: 2.21-7.58), and in 7-10 years after screening, the OR was 5.35 (95% CI: 2.57-11.15). Excess risk with a stable OR of about 2 was also observed for women with 26-50% breast density. These patterns were robust when the analyses were limited to post-menopausal women, non-users of hormonal replacement therapy and after stratification by age at density measurement. CONCLUSION A single breast density measurement identifies women with an excess risk of breast cancer during at least the subsequent 10 years.
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Affiliation(s)
- Matejka Rebolj
- Cancer Prevention Group, School of Cancer & Pharmaceutical Sciences, Faculty of Life Sciences & Medicine, King's College London, London SE1 9RT, UK; Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK.
| | - Oleg Blyuss
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK; Department of Paediatrics, Sechenov University, Moscow, Russia
| | - Kee Seng Chia
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Stephen W Duffy
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts & The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK.
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Engmann NJ, Scott CG, Jensen MR, Winham S, Miglioretti DL, Ma L, Brandt K, Mahmoudzadeh A, Whaley DH, Hruska C, Wu F, Norman AD, Hiatt RA, Heine J, Shepherd J, Pankratz VS, Vachon CM, Kerlikowske K. Combined effect of volumetric breast density and body mass index on breast cancer risk. Breast Cancer Res Treat 2019; 177:165-173. [PMID: 31129803 DOI: 10.1007/s10549-019-05283-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 05/16/2019] [Indexed: 01/16/2023]
Abstract
BACKGROUND Breast density and body mass index (BMI) are used for breast cancer risk stratification. We evaluate whether the positive association between volumetric breast density and breast cancer risk is strengthened with increasing BMI. METHODS The San Francisco Mammography Registry and Mayo Clinic Rochester identified 781 premenopausal and 1850 postmenopausal women with breast cancer diagnosed between 2007 and 2015 that had a screening digital mammogram at least 6 months prior to diagnosis. Up to three controls (N = 3535) were matched per case on age, race, date, mammography machine, and state. Volumetric percent density (VPD) and dense volume (DV) were measured with Volpara™. Breast cancer risk was assessed with logistic regression stratified by menopause status. Multiplicative interaction tests assessed whether the association of density measures was differential by BMI categories. RESULTS The increased risk of breast cancer associated with VPD was strengthened with higher BMI for both premenopausal (pinteraction = 0.01) and postmenopausal (pinteraction = 0.0003) women. For BMI < 25, 25-30, and ≥ 30 kg/m2, ORs for breast cancer for a 1 SD increase in VPD were 1.24, 1.65, and 1.97 for premenopausal, and 1.20, 1.55, and 2.25 for postmenopausal women, respectively. ORs for breast cancer for a 1 SD increase in DV were 1.39, 1.33, and 1.51 for premenopausal (pinteraction = 0.58), and 1.31, 1.34, and 1.65 (pinteraction = 0.03) for postmenopausal women for BMI < 25, 25-30 and ≥ 30 kg/m2, respectively. CONCLUSIONS The effect of volumetric percent density on breast cancer risk is strongest in overweight and obese women. These associations have clinical relevance for informing prevention strategies.
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Affiliation(s)
| | | | | | | | - Diana L Miglioretti
- University of California, Davis, USA.,Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Lin Ma
- Kaiser Permanente Division of Research, Oakland, CA, USA
| | | | | | | | | | | | | | - Robert A Hiatt
- Department of Medicine and Epidemiology & Biostatistics, University of California, San Francisco, USA
| | | | | | - V Shane Pankratz
- University of New Mexico Health Sciences Center, Albuquerque, USA
| | | | - Karla Kerlikowske
- Department of Medicine and Epidemiology & Biostatistics, University of California, San Francisco, USA.
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Gastounioti A, McCarthy AM, Pantalone L, Synnestvedt M, Kontos D, Conant EF. Effect of Mammographic Screening Modality on Breast Density Assessment: Digital Mammography versus Digital Breast Tomosynthesis. Radiology 2019; 291:320-327. [PMID: 30888933 PMCID: PMC6493215 DOI: 10.1148/radiol.2019181740] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Revised: 01/24/2019] [Accepted: 01/28/2019] [Indexed: 01/14/2023]
Abstract
Background Breast Imaging Reporting and Data System (BI-RADS) breast density categories assigned by interpreting radiologists often influence decisions surrounding supplemental breast cancer screening and risk assessment. The landscape of mammographic screening continuously evolves, and different mammographic screening modalities may result in different perception of density, reflected in different assignment of BI-RADS density categories. Purpose To investigate the effect of screening mammography modality on BI-RADS breast density assessments. Materials and Methods Data were retrospectively analyzed from 24 736 individual women (42.3% [10 455 of 24 736] white women, 57.7% [14 281 of 24 736] black women; mean age, 56.3 years; age range, 40.0-74.9 years) who underwent from one to seven mammographic screening examinations from September 2010 through February 2017 (60 766 examinations). Three screening modalities were used: digital mammography alone (8935 examinations); digital mammography with digital breast tomosynthesis (DBT; 30 779 examinations); and synthetic mammography with DBT (21 052 examinations). Random-effects logistic regression analysis was performed to estimate the likelihood of assignment to high versus low BI-RADS density category according to each modality, adjusted for ethnicity, age, body mass index (BMI), and radiologist. The interactions of modality with ethnicity and BMI on density categorization were also tested with the model. Results Women screened with DBT versus digital mammography alone had lower likelihood regarding categorization of high density breasts (digital mammography and DBT vs digital mammography: odds ratio, 0.69 [95% confidence interval: 0.61, 0.80], P < .001; synthetic mammography and DBT vs digital mammography: odds ratio, 0.43 [95% confidence interval: 0.37, 0.50], P < .001). Lower likelihood of high density was also observed at synthetic mammography and DBT compared with digital mammography and DBT (odds ratio, 0.62; 95% confidence interval: 0.56, 0.69; P < .001). There were interactions of modality with ethnicity (P = .007) and BMI (P = .003) on breast density assessment, with greater differences in density categorization according to modality observed for black women than for white women and groups with higher BMI. Conclusion Breast density categorization may vary by screening mammographic modality, and this effect appears to vary by ethnicity and body mass index. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.
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Affiliation(s)
- Aimilia Gastounioti
- From the Department of Radiology, Perelman School of Medicine,
University of Pennsylvania, 3710 Hamilton Walk, Room G601E Goddard Building,
Philadelphia, PA 19104 (A.G., L.P., M.S., D.K., E.F.C.); and Department of
Medicine, Massachusetts General Hospital, Boston, Mass (A.M.M.)
| | - Anne Marie McCarthy
- From the Department of Radiology, Perelman School of Medicine,
University of Pennsylvania, 3710 Hamilton Walk, Room G601E Goddard Building,
Philadelphia, PA 19104 (A.G., L.P., M.S., D.K., E.F.C.); and Department of
Medicine, Massachusetts General Hospital, Boston, Mass (A.M.M.)
| | - Lauren Pantalone
- From the Department of Radiology, Perelman School of Medicine,
University of Pennsylvania, 3710 Hamilton Walk, Room G601E Goddard Building,
Philadelphia, PA 19104 (A.G., L.P., M.S., D.K., E.F.C.); and Department of
Medicine, Massachusetts General Hospital, Boston, Mass (A.M.M.)
| | - Marie Synnestvedt
- From the Department of Radiology, Perelman School of Medicine,
University of Pennsylvania, 3710 Hamilton Walk, Room G601E Goddard Building,
Philadelphia, PA 19104 (A.G., L.P., M.S., D.K., E.F.C.); and Department of
Medicine, Massachusetts General Hospital, Boston, Mass (A.M.M.)
| | - Despina Kontos
- From the Department of Radiology, Perelman School of Medicine,
University of Pennsylvania, 3710 Hamilton Walk, Room G601E Goddard Building,
Philadelphia, PA 19104 (A.G., L.P., M.S., D.K., E.F.C.); and Department of
Medicine, Massachusetts General Hospital, Boston, Mass (A.M.M.)
| | - Emily F. Conant
- From the Department of Radiology, Perelman School of Medicine,
University of Pennsylvania, 3710 Hamilton Walk, Room G601E Goddard Building,
Philadelphia, PA 19104 (A.G., L.P., M.S., D.K., E.F.C.); and Department of
Medicine, Massachusetts General Hospital, Boston, Mass (A.M.M.)
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Comparison of a personalized breast dosimetry method with standard dosimetry protocols. Sci Rep 2019; 9:5866. [PMID: 30971741 PMCID: PMC6458177 DOI: 10.1038/s41598-019-42144-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 03/20/2019] [Indexed: 11/24/2022] Open
Abstract
Average glandular dose (AGD) in digital mammography crucially depends on the estimation of breast glandularity. In this study we compared three different methods of estimating glandularities according to Wu, Dance and Volpara with respect to resulting AGDs. Exposure data from 3050 patient images, acquired with a GE Senographe Essential constituted the study population of this work. We compared AGD (1) according to Dance et al. applying custom g, c, and s factors using HVL, breast thickness, patient age and incident air kerma (IAK) from the DICOM headers; (2) according to Wu et al. as determined by the GE system; and (3) AGD derived with the Dance model with personalized c factors using glandularity determined with the Volpara (Volpara Solutions, Wellington, New Zealand) software (Volpare AGD). The ratios of the resulting AGDs were analysed versus parameters influencing dose. The highest deviation between the resulting AGDs was found in the ratio of GE AGD to Volpara AGD for breast thicknesses between 20 and 40 mm (ratio: 0.80). For thicker breasts this ratio is close to one (1 ± 0.02 for breast thicknesses >60 mm). The Dance to Volpara ratio was between 0.86 (breast thickness 20–40 mm) and 0.99 (>80 mm), and Dance/GE AGD was between 1.07 (breast thickness 20–40 mm) and 0.98 (41–60, and >80 mm). Glandularities by Volpara were generally smaller than the one calculated with the Dance method. This effect is most pronounced for small breast thickness and older ages. Taking the considerable divergences between the AGDs from different methods into account, the selection of the method should by done carefully. As the Volpara method provides an analysis of the individual breast tissue, while the Wu and the Dance methods use look up tables and custom parameter sets, the Volpara method might be more appropriate if individual ADG values are sought. For regulatory purposes and comparison with diagnostic reference values, the method to be used needs to be defined exactly and clearly be stated. However, it should be accepted that dose values calculated with standardized models, like AGD and also effective dose, are afflicted with a considerable uncertainty budgets that need to be accounted for in the interpretation of these values.
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Tapia KA, Garvey G, McEntee MF, Rickard M, Lydiard L, Brennan PC. Mammographic densities of Aboriginal and non-Aboriginal women living in Australia's Northern Territory. Int J Public Health 2019; 64:1085-1095. [PMID: 30941443 DOI: 10.1007/s00038-019-01237-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Revised: 03/21/2019] [Accepted: 03/23/2019] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVES To compare the mammographic densities and other characteristics of Aboriginal and non-Aboriginal women screened in Australia. METHODS Population screening programme data of Aboriginal (n = 857) and non-Aboriginal women (n = 3236) were used. Mann-Whitney U test compared ages at screening and Chi-square tests compared personal and clinical information. Logistic regression analysis was used for density groupings. OR and 95% CI were calculated for multivariate association for density. RESULTS Mammographic density was lower amongst Aboriginal women (P < 0.001). For non-Aboriginal women, higher density was associated with younger age (OR 2.4, 95% CI 2.1-2.8), recall to assessment (OR 2.2, 95% CI 1.6-3.0), family history of breast cancer (OR 1.4, 95% CI 1.2-1.6), English-speaking background (OR 1.4, 95% CI 1.2-1.6), and residence in remote areas (OR 1.2, 95% CI 1.1-1.4). For Aboriginal women, density was associated with younger age (OR 2.7, 95% CI 2.0-3.5; P < 0.001), and recall to assessment (OR 2.3, 95% CI 1.4-3.9; P < 0.05). CONCLUSIONS Significant differences between Aboriginal and non-Aboriginal women were found. There were more significant associations for dense breasts for non-Aboriginal women than for Aboriginal women.
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Affiliation(s)
- Kriscia A Tapia
- Faculty of Health Sciences, The University of Sydney, Room M504, M Block, 75 East Street, Lidcombe, NSW, 2141, Australia.
| | - Gail Garvey
- Faculty of Health Sciences, The University of Sydney, Room M504, M Block, 75 East Street, Lidcombe, NSW, 2141, Australia.,Menzies School of Health Research, Level 1, 147 Wharf Street, Spring Hill, QLD, 4000, Australia
| | - Mark F McEntee
- Department of Medicine, University College Cork, Brookfield Health Sciences Complex, College Road, Cork, T12 AK54, Ireland
| | - Mary Rickard
- Faculty of Health Sciences, The University of Sydney, Room M504, M Block, 75 East Street, Lidcombe, NSW, 2141, Australia.,BreastScreen Australia, Sydney, NSW, Australia
| | - Lorraine Lydiard
- BreastScreen Northern Territory, Level 1, 9 Scaturchio St., Casuarina, NT, 0810, Australia
| | - Patrick C Brennan
- Faculty of Health Sciences, The University of Sydney, Room M221, M Block, 75 East Street, Lidcombe, NSW, 2141, Australia
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Albeshan SM, Hossain SZ, Mackey MG, Peat JK, Al Tahan FM, Brennan PC. Preliminary investigation of mammographic density among women in Riyadh: association with breast cancer risk factors and implications for screening practices. Clin Imaging 2019; 54:138-147. [PMID: 30639525 DOI: 10.1016/j.clinimag.2019.01.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 12/10/2018] [Accepted: 01/04/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE Improved mammographic sensitivity is associated with reduced mammographic density. This study aims to: provide a preliminary report on mammographic density among women in Riyadh; identify risk factors associated with mammographic density; and consider the potential implications for screening practices. METHODS Based on a cross-sectional design, we examined a total of 792 women using an area-based mammographic density method (LIBRA). Spearman's correlation, Mann-Whitney U, Kruskal-Wallis and binary logistic regression were used for analyses. RESULTS The study population had a mean age of 49.6 years and a high proportion of participants were overweight or obese (90%). A large number of women had low mammographic density, with a mean dense breast area of 19.1 cm2 and percent density of 10.3 cm2. Slightly more than half of the variations in the dense breast area and percent density models were explained by BMI. In the adjusted analyses, BMI, menopausal status, age at menarche and number of children remained statistically significant predictors. CONCLUSION Given the high proportion of women with low mammographic density, our findings suggest that women living in Riyadh may not require additional imaging strategies beyond mammography to detect breast cancers. The high proportion of obese women reported here and across Saudi Arabia suggests that mammographic density is less likely to have an adverse impact on mammographic sensitivity. Thus and to improve clinical outcomes among Saudi women, annual mammography and commencing screening at a younger age are suggested. Additional studies are required to shed further light on our findings.
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Affiliation(s)
- Salman M Albeshan
- Medical Radiation Sciences, Medical Image Optimization and Perception Group (MIOPeG), Australia; Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University (KSU), Saudi Arabia.
| | - Syeda Z Hossain
- Discipline of Behavioral and Social Sciences in Health, Australia
| | | | - Jennifer K Peat
- Medical Radiation Sciences, Medical Image Optimization and Perception Group (MIOPeG), Australia
| | | | - Patrick C Brennan
- Medical Radiation Sciences, Medical Image Optimization and Perception Group (MIOPeG), Australia
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