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Ray KM. Interval Cancers in Understanding Screening Outcomes. Radiol Clin North Am 2024; 62:559-569. [PMID: 38777533 DOI: 10.1016/j.rcl.2023.12.012] [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
Interval breast cancers are not detected at routine screening and are diagnosed in the interval between screening examinations. A variety of factors contribute to interval cancers, including patient and tumor characteristics as well as the screening technique and frequency. The interval cancer rate is an important metric by which the effectiveness of screening may be assessed and may serve as a surrogate for mortality benefit.
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Affiliation(s)
- Kimberly M Ray
- Department of Radiology and Biomedical Sciences, University of California, San Francisco, UCSF Medical Center, 1825 4th Street, L3185, Box 4034, San Francisco, CA 94107, USA.
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Hopper JL, Li S, MacInnis RJ, Dowty JG, Nguyen TL, Bui M, Dite GS, Esser VFC, Ye Z, Makalic E, Schmidt DF, Goudey B, Alpen K, Kapuscinski M, Win AK, Dugué PA, Milne RL, Jayasekara H, Brooks JD, Malta S, Calais-Ferreira L, Campbell AC, Young JT, Nguyen-Dumont T, Sung J, Giles GG, Buchanan D, Winship I, Terry MB, Southey MC, Jenkins MA. Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies. Genet Epidemiol 2024. [PMID: 38504141 DOI: 10.1002/gepi.22555] [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: 08/30/2023] [Revised: 01/29/2024] [Accepted: 02/23/2024] [Indexed: 03/21/2024]
Abstract
Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.
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Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Genetic Technologies Ltd., Fitzroy, Victoria, Australia
| | - Vivienne F C Esser
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Daniel F Schmidt
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Benjamin Goudey
- ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, Victoria, Australia
- The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Karen Alpen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Miroslaw Kapuscinski
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia
- Genetic Medicine, Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Pierre-Antoine Dugué
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Harindra Jayasekara
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Jennifer D Brooks
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Sue Malta
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Lucas Calais-Ferreira
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
| | - Alexander C Campbell
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia
| | - Jesse T Young
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Centre for Adolescent Health, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- School of Population and Global Health, The University of Western Australia, Perth, Western Australia, Australia
- Justice Health Group, Curtin School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul, South Korea
- Genome Medicine Institute, Seoul National University, Seoul, South Korea
- Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Daniel Buchanan
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Ingrid Winship
- Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Parkville, Victoria, Australia
| | - Mary Beth Terry
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA
| | - Melissa C Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
- Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne, Victoria, Australia
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Cao J, Li J, Zhang Z, Qin G, Pang Y, Wu M, Gu K, Xu H. Interaction between body mass index and family history of cancer on the risk of female breast cancer. Sci Rep 2024; 14:4927. [PMID: 38418549 PMCID: PMC10901816 DOI: 10.1038/s41598-024-54762-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: 10/12/2023] [Accepted: 02/16/2024] [Indexed: 03/01/2024] Open
Abstract
Both body mass index (BMI) and family history of cancer are established risk factors for female breast cancer. However, few studies explored the potential interaction between both factors. We assessed the association of BMI and its interaction with family cancer history on the risk of female breast cancer in Shanghai, China. Based on a population-based prospective cohort study started from 2008 to 2012 with 15,055 Chinese female participants in Minhang district, Shanghai. Cox regression models were used to estimate the association of BMI and its interaction with a family history of cancer on breast cancer risk. The additive interaction was evaluated by the relative excess risk due to interaction (RERI) and the attributable proportion due to interaction (AP), and the multiplicative interaction was assessed by the product term (BMI* family history of cancer) in the Cox regression model. Compared with BMI of < 24 kg/m2 and no family history of cancer, women with BMI of ≥ 24 kg/m2 and a family history of cancer had a higher risk for breast cancer with HR 2.06 (95% CI 1.39, 3.06). There was an additive interaction between BMI and family history of cancer on breast cancer incidence, with the RERI being 0.29 (95% CI 0.08, 0.51) and the AP being 0.37 (95% CI 0.08, 0.66). The coexistence of obesity and cancer family history may exacerbate breast cancer incidence risk, highlighting the importance of weight management in women with a family history of cancer.
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Affiliation(s)
- Jiamin Cao
- Shanghai Minhang Center for Disease Control and Prevention, No. 965, Zhongyi Road, Shanghai, 201101, China
- Shanghai Municipal Center for Disease Control and Prevention, No. 1380, Zhongshan West Road, Shanghai, 200336, China
| | - Jun Li
- Shanghai Minhang Center for Disease Control and Prevention, No. 965, Zhongyi Road, Shanghai, 201101, China
| | - Zuofeng Zhang
- Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Guoyou Qin
- School of Public Health, Fudan University, Shanghai, China
| | - Yi Pang
- Shanghai Municipal Center for Disease Control and Prevention, No. 1380, Zhongshan West Road, Shanghai, 200336, China
| | - Mengyin Wu
- Shanghai Municipal Center for Disease Control and Prevention, No. 1380, Zhongshan West Road, Shanghai, 200336, China
| | - Kai Gu
- Shanghai Municipal Center for Disease Control and Prevention, No. 1380, Zhongshan West Road, Shanghai, 200336, China.
| | - Huilin Xu
- Shanghai Minhang Center for Disease Control and Prevention, No. 965, Zhongyi Road, Shanghai, 201101, China.
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Sayed SF, Dailah HG, Nagarajan S, Abdelwahab SI, Abadi SSH, Akhtar N, Khuwaja G, Malham WADA. Knowledge of Non-Invasive Biomarkers of Breast Cancer, Risk Factors, and BSE Practices Among Nursing Undergraduates in Farasan Island, KSA. SAGE Open Nurs 2024; 10:23779608241248519. [PMID: 38681865 PMCID: PMC11055480 DOI: 10.1177/23779608241248519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/18/2024] [Accepted: 04/03/2024] [Indexed: 05/01/2024] Open
Abstract
Background of the Study Mammograms are sometimes met with issues of overdiagnosis and underdiagnosis; therefore, they are less reliable in identifying cancer in women with dense breasts. As a result, it is critical to be aware of other sensitive screening techniques for the early diagnosis of breast cancer. Aim The ultimate objective of this study was to assess the knowledge of nursing undergraduates regarding non-invasive biomarkers, such as volatile organic compounds in breath, nipple aspirate fluid, sweat, urine, and tears, for the early detection of breast cancer to help improve patient care, determine the risk factors, and encourage practice of breast self-examination. Methods Cross-sectional research was done in the Department of Nursing at Farasan campus using a self-structured questionnaire as the study tool. A total of 260 students willingly participated. The study tool had evaluation questions focused on the non-invasive biomarkers of breast cancer, risk factors, and breast self-examination practices to collect data. The data were subjected to descriptive and inferential statistics. The statistical significance was calculated at P < .05. Data analyses were done using Microsoft Excel (2013). Results A significant knowledge gap existed among the study participants about the non-invasive biomarkers of breast cancer. A lesser percentage of students (25%) stated that they do breast self-examination on a monthly basis. The most common reasons for not doing the breast self-examination were "not knowing how to do the breast self-examination" (77.3%), fear of a positive diagnosis (53.9%), thinking that they are not at risk as all were in their teens and hence not required (44.7%), and lack of time (48.7%). Age and frequency of breast self-examination were significantly associated (P < .05) as those few students (22.7%) who were doing breast self-examination practices every 2-4 months belonged to a higher study year. Furthermore, knowledge regarding incidence rates and health care expenditure by the government on breast cancer was also significantly low (P < .05). Conclusions Outcomes would help prioritize actions to help future nurses better understand breast cancer, allowing them to extend patient care in the best way possible.
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Affiliation(s)
| | - Hamad G. Dailah
- Department of Nursing, College of Nursing, Jazan University, Jazan, Saudi Arabia
| | - Sumathi Nagarajan
- Department of Nursing, Farasan University College, Jazan University, Jazan, KSA
| | | | | | - Nida Akhtar
- Department of Nursing, Al-Dayer College, Jazan University, Jazan, Saudi Arabia
| | - Gulrana Khuwaja
- Department of Pharmaceutical Chemistry, College of Pharmacy, Jazan University, Jazan, Saudi Arabia
| | - Wadeah Ali DA Malham
- Department of Nursing, Farasan University College, Jazan University, Jazan, Saudi Arabia
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Hopper JL, Dowty JG, Nguyen TL, Li S, Dite GS, MacInnis RJ, Makalic E, Schmidt DF, Bui M, Stone J, Sung J, Jenkins MA, Giles GG, Southey MC, Mathews JD. Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks. Int J Epidemiol 2023; 52:1557-1568. [PMID: 37349888 PMCID: PMC10655167 DOI: 10.1093/ije/dyad086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 06/16/2023] [Indexed: 06/24/2023] Open
Abstract
BACKGROUND The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered. DEVELOPMENT We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors. APPLICATION For female breast cancer, VALID quantified how much variance in risk is explained-at different ages-by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors. CONCLUSION VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk.
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Affiliation(s)
- John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - James G Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Tuong L Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Gillian S Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Genetic Technologies Ltd., Fitzroy, VIC, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Enes Makalic
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Daniel F Schmidt
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
- Faculty of Information Technology, Monash University, Clayton, VIC, Australia
| | - Minh Bui
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Jennifer Stone
- School of Population and Global Health, University of Western Australia, Perth, WA, Australia
| | - Joohon Sung
- Division of Genome and Health Big Data, Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Melissa C Southey
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - John D Mathews
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
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Ambinder EB, Lee E, Nguyen DL, Gong AJ, Haken OJ, Visvanathan K. Interval Breast Cancers Versus Screen Detected Breast Cancers: A Retrospective Cohort Study. Acad Radiol 2023; 30 Suppl 2:S154-S160. [PMID: 36739227 DOI: 10.1016/j.acra.2023.01.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 12/28/2022] [Accepted: 01/05/2023] [Indexed: 02/05/2023]
Abstract
RATIONALE AND OBJECTIVE Mammographic screening detects most breast cancers but there are still women diagnosed with breast cancer between annual mammograms. We aim to identify features that differentiate screen detected breast cancers from interval breast cancer. MATERIALS AND METHODS All screening mammograms (n = 211,517) performed 7/1/2013-6/30/2020 at our institution were reviewed. Patients with breast cancer diagnosed within one year of screening were included and divided into two distinct groups: screen detected cancer group and interval cancer group. Characteristics in these groups were compared using the chi square test, fisher test, and student's T test. RESULTS A total of 1,232 patients were included (mean age 64 +/- 11). Sensitivity of screening mammography was 92% (1,136 screen detected cancers, 96 interval cancers). Patient age, race, and personal history of breast cancer were similar between the groups (p > 0.05). Patients with interval cancers more often had dense breast tissue (75/96 = 78% versus 694/1136 = 61%, p < 0.001). Compared to screen detected cancers, interval cancers were more often primary tumor stage two or higher (41/96 = 43% versus 139/1136 = 12%, p < 0.001) and regional lymph node stage one or higher (21/96 = 22% versus 132/1136 = 12%, p = 0.003). Interval cancers were more often triple negative (16/77 = 21% versus [48/813 = 6%], p < 0.001) with high Ki67 proliferation indices (28/45 = 62% versus 188/492 = 38%, p = 0.002). CONCLUSION Mammographic screening had high sensitivity for breast cancer detection (92%). Interval cancers were associated with dense breast tissue and had higher stage with less favorable molecular features compared to screen detected cancers.
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Affiliation(s)
- Emily B Ambinder
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 601 N. Caroline St., Baltimore, Maryland, 21287; Johns Hopkins Sidney Kimmel Cancer Center, Baltimore MD.
| | - Emerson Lee
- Johns Hopkins School of Medicine, Baltimore MD
| | | | - Anna J Gong
- Johns Hopkins School of Medicine, Baltimore MD
| | - Orli J Haken
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medicine, 601 N. Caroline St., Baltimore, Maryland, 21287
| | - Kala Visvanathan
- Johns Hopkins Sidney Kimmel Cancer Center, Baltimore MD; Departments of Epidemiology and Oncology, Johns Hopkins Bloomberg School of Public Health and Kimmel Cancer Center, Baltimore, MD
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Ma J, Dhiman P, Qi C, Bullock G, van Smeden M, Riley RD, Collins GS. Poor handling of continuous predictors in clinical prediction models using logistic regression: a systematic review. J Clin Epidemiol 2023; 161:140-151. [PMID: 37536504 DOI: 10.1016/j.jclinepi.2023.07.017] [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: 03/09/2023] [Revised: 07/20/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND AND OBJECTIVES When developing a clinical prediction model, assuming a linear relationship between the continuous predictors and outcome is not recommended. Incorrect specification of the functional form of continuous predictors could reduce predictive accuracy. We examine how continuous predictors are handled in studies developing a clinical prediction model. METHODS We searched PubMed for clinical prediction model studies developing a logistic regression model for a binary outcome, published between July 01, 2020, and July 30, 2020. RESULTS In total, 118 studies were included in the review (18 studies (15%) assessed the linearity assumption or used methods to handle nonlinearity, and 100 studies (85%) did not). Transformation and splines were commonly used to handle nonlinearity, used in 7 (n = 7/18, 39%) and 6 (n = 6/18, 33%) studies, respectively. Categorization was most often used method to handle continuous predictors (n = 67/118, 56.8%) where most studies used dichotomization (n = 40/67, 60%). Only ten models included nonlinear terms in the final model (n = 10/18, 56%). CONCLUSION Though widely recommended not to categorize continuous predictors or assume a linear relationship between outcome and continuous predictors, most studies categorize continuous predictors, few studies assess the linearity assumption, and even fewer use methodology to account for nonlinearity. Methodological guidance is provided to guide researchers on how to handle continuous predictors when developing a clinical prediction model.
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Affiliation(s)
- Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, United Kingdom.
| | - Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, United Kingdom
| | - Cathy Qi
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Singleton Park Swansea, SA2 8PP, Swansea, United Kingdom
| | - Garrett Bullock
- Department of Orthopaedic Surgery, Wake Forest School of Medicine, Winston-Salem, NC, USA; Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, United Kingdom
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Richard D Riley
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, United Kingdom
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Tari DU, Santonastaso R, De Lucia DR, Santarsiere M, Pinto F. Breast Density Evaluation According to BI-RADS 5th Edition on Digital Breast Tomosynthesis: AI Automated Assessment Versus Human Visual Assessment. J Pers Med 2023; 13:jpm13040609. [PMID: 37108994 PMCID: PMC10146726 DOI: 10.3390/jpm13040609] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/15/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
Background: The assessment of breast density is one of the main goals of radiologists because the masking effect of dense fibroglandular tissue may affect the mammographic identification of lesions. The BI-RADS 5th Edition has revised the mammographic breast density categories, focusing on a qualitative evaluation rather than a quantitative one. Our purpose is to compare the concordance of the automatic classification of breast density with the visual assessment according to the latest available classification. Methods: A sample of 1075 digital breast tomosynthesis images from women aged between 40 and 86 years (58 ± 7.1) was retrospectively analyzed by three independent readers according to the BI-RADS 5th Edition. Automated breast density assessment was performed on digital breast tomosynthesis images with the Quantra software version 2.2.3. Interobserver agreement was assessed with kappa statistics. The distributions of breast density categories were compared and correlated with age. Results: The agreement on breast density categories was substantial to almost perfect between radiologists (κ = 0.63–0.83), moderate to substantial between radiologists and the Quantra software (κ = 0.44–0.78), and the consensus of radiologists and the Quantra software (κ = 0.60–0.77). Comparing the assessment for dense and non-dense breasts, the agreement was almost perfect in the screening age range without a statistically significant difference between concordant and discordant cases when compared by age. Conclusions: The categorization proposed by the Quantra software has shown a good agreement with the radiological evaluations, even though it did not completely reflect the visual assessment. Thus, clinical decisions regarding supplemental screening should be based on the radiologist’s perceived masking effect rather than the data produced exclusively by the Quantra software.
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Kou K, Cameron J, Youl P, Pyke C, Chambers S, Dunn J, Aitken JF, Baade PD. Severity and risk factors of interval breast cancer in Queensland, Australia: a population-based study. Breast Cancer 2023; 30:466-477. [PMID: 36809492 PMCID: PMC10119209 DOI: 10.1007/s12282-023-01439-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/15/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND Interval breast cancers (BC) are those diagnosed within 24 months of a negative mammogram. This study estimates the odds of being diagnosed with high-severity BC among screen-detected, interval, and other symptom-detected BC (no screening history within 2 years); and explores factors associated with being diagnosed with interval BC. METHODS Telephone interviews and self-administered questionnaires were conducted among women (n = 3,326) diagnosed with BC in 2010-2013 in Queensland. Respondents were categorised into screen-detected, interval, and other symptom-detected BCs. Data were analysed using logistic regressions with multiple imputation. RESULTS Compared with screen-detected BC, interval BC had higher odds of late-stage (OR = 3.50, 2.9-4.3), high-grade (OR = 2.36, 1.9-2.9) and triple-negative cancers (OR = 2.55, 1.9-3.5). Compared with other symptom-detected BC, interval BC had lower odds of late stage (OR = 0.75, 0.6-0.9), but higher odds of triple-negative cancers (OR = 1.68, 1.2-2.3). Among women who had a negative mammogram (n = 2,145), 69.8% were diagnosed at their next mammogram, while 30.2% were diagnosed with an interval cancer. Those with an interval cancer were more likely to have healthy weight (OR = 1.37, 1.1-1.7), received hormone replacement therapy (2-10 years: OR = 1.33, 1.0-1.7; > 10 years: OR = 1.55, 1.1-2.2), conducted monthly breast self-examinations (BSE) (OR = 1.66, 1.2-2.3) and had previous mammogram in a public facility (OR = 1.52, 1.2-2.0). CONCLUSION These results highlight the benefits of screening even among those with an interval cancer. Women-conducted BSE were more likely to have interval BC which may reflect their increased ability to notice symptoms between screening intervals.
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Affiliation(s)
- Kou Kou
- Cancer Council Queensland, Spring Hill, PO Box 201, Brisbane, QLD, 4001, Australia
| | - Jessica Cameron
- Cancer Council Queensland, Spring Hill, PO Box 201, Brisbane, QLD, 4001, Australia.,School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Philippa Youl
- Cancer Alliance Queensland, Metro South Hospital and Health Service, Woolloongabba, Australia
| | - Chris Pyke
- Mater Hospitals South Brisbane, Brisbane, Australia
| | - Suzanne Chambers
- Faculty of Health, University of Technology Sydney, Sydney, Australia
| | - Jeff Dunn
- Prostate Cancer Foundation of Australia, Sydney, Australia
| | - Joanne F Aitken
- Cancer Council Queensland, Spring Hill, PO Box 201, Brisbane, QLD, 4001, Australia.,School of Public Health, The University of Queensland, Brisbane, Australia.,School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia.,Institute for Resilient Regions, University of Southern Queensland, Brisbane, Australia
| | - Peter D Baade
- Cancer Council Queensland, Spring Hill, PO Box 201, Brisbane, QLD, 4001, Australia. .,Centre for Data Science, Faculty of Science, Queensland University of Technology, Brisbane, Australia. .,Menzies Health Institute Queensland, Griffith University, Gold Coast Campus, Parklands Drive, Southport, QLD, Australia.
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10
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Weigel S, Heindel W, Hense HW, Decker T, Gerß J, Kerschke L. Breast Density and Breast Cancer Screening with Digital Breast Tomosynthesis: A TOSYMA Trial Subanalysis. Radiology 2023; 306:e221006. [PMID: 36194110 DOI: 10.1148/radiol.221006] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background Digital breast tomosynthesis (DBT) plus synthesized mammography (SM) reduces the diagnostic pitfalls of tissue superimposition, which is a limitation of digital mammography (DM). Purpose To compare the invasive breast cancer detection rate (iCDR) of DBT plus SM versus DM screening for different breast density categories. Materials and Methods An exploratory subanalysis of the TOmosynthesis plus SYnthesized MAmmography (TOSYMA) study, a randomized, controlled, multicenter, parallel-group trial recruited within the German mammography screening program from July 2018 to December 2020. Women aged 50-69 years were randomly assigned (1:1) to DBT plus SM or DM screening examination. Breast density categories A-D were visually assessed according to the Breast Imaging Reporting and Data System Atlas. Exploratory analyses were performed of the iCDR in both study arms and stratified by breast density, and odds ratios and 95% CIs were determined. Results A total of 49 762 women allocated to DBT plus SM and 49 796 allocated to DM (median age, 57 years [IQR, 53-62 years]) were included. In the DM arm, the iCDR was 3.6 per 1000 screening examinations in category A (almost entirely fatty) (16 of 4475 screenings), 4.3 in category B (102 of 23 534 screenings), 6.1 in category C (116 of 19 051 screenings), and 2.3 in category D (extremely dense breasts) (six of 2629 screenings). The iCDR in the DBT plus SM arm was 2.7 per 1000 screening examinations in category A (12 of 4439 screenings), 6.9 in category B (154 of 22 328 screenings), 8.3 in category C (156 of 18 772 screenings), and 8.1 in category D (32 of 3940 screenings). The odds ratio for DM versus DBT plus SM in category D was 3.8 (95% CI: 1.5, 11.1). The invasive cancers detected with DBT plus SM were most often grade 2 tumors; in category C, it was 58% (91 of 156 invasive cancers), and in category D, it was 47% (15 of 32 invasive cancers). Conclusion The TOmosynthesis plus SYnthesized MAmmography trial revealed higher invasive cancer detection rates with digital breast tomosynthesis plus synthesized mammography than digital mammography in dense breasts, relatively and absolutely most marked among women with extremely dense breasts. ClinicalTrials.gov registration no.: NCT03377036 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Lee and Moy in this issue.
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Affiliation(s)
- Stefanie Weigel
- From the Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, D-48149 Münster, Germany (S.W., W.H., T.D.); Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany (H.W.H.); and Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany (J.G., L.K.)
| | - Walter Heindel
- From the Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, D-48149 Münster, Germany (S.W., W.H., T.D.); Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany (H.W.H.); and Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany (J.G., L.K.)
| | - Hans-Werner Hense
- From the Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, D-48149 Münster, Germany (S.W., W.H., T.D.); Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany (H.W.H.); and Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany (J.G., L.K.)
| | - Thomas Decker
- From the Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, D-48149 Münster, Germany (S.W., W.H., T.D.); Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany (H.W.H.); and Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany (J.G., L.K.)
| | - Joachim Gerß
- From the Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, D-48149 Münster, Germany (S.W., W.H., T.D.); Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany (H.W.H.); and Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany (J.G., L.K.)
| | - Laura Kerschke
- From the Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, D-48149 Münster, Germany (S.W., W.H., T.D.); Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany (H.W.H.); and Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany (J.G., L.K.)
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- From the Clinic for Radiology and Reference Center for Mammography Münster, University of Münster and University Hospital Münster, Albert-Schweitzer-Campus 1, Building A1, D-48149 Münster, Germany (S.W., W.H., T.D.); Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany (H.W.H.); and Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany (J.G., L.K.)
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11
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Li S, Nguyen TL, Nguyen-Dumont T, Dowty JG, Dite GS, Ye Z, Trinh HN, Evans CF, Tan M, Sung J, Jenkins MA, Giles GG, Hopper JL, Southey MC. Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk. Cancers (Basel) 2022; 14:cancers14112767. [PMID: 35681745 PMCID: PMC9179294 DOI: 10.3390/cancers14112767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 04/27/2022] [Accepted: 05/30/2022] [Indexed: 11/26/2022] Open
Abstract
Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05−0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the “white but not bright area” (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer.
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Affiliation(s)
- Shuai Li
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
| | - Tuong L. Nguyen
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Tu Nguyen-Dumont
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
| | - James G. Dowty
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Gillian S. Dite
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Genetic Technologies Limited, Fitzroy, VIC 3065, Australia
| | - Zhoufeng Ye
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Ho N. Trinh
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Christopher F. Evans
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Maxine Tan
- Electrical and Computer Systems Engineering Discipline, School of Engineering, Monash University Malaysia, Bandar Sunway 47500, Malaysia;
- School of Electrical and Computer Engineering, The University of Oklahoma, Norman, OK 73019, USA
| | - Joohon Sung
- Department of Public Health Sciences, Division of Genome and Health Big Data, Graduate School of Public Health, Seoul National University, Seoul 08826, Korea;
| | - Mark A. Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
| | - Graham G. Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3051, Australia; (S.L.); (T.L.N.); (J.G.D.); (G.S.D.); (Z.Y.); (H.N.T.); (C.F.E.); (M.A.J.); (G.G.G.)
- Correspondence:
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia; (T.N.-D.); (M.C.S.)
- Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3051, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
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12
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Familial Aspects of Mammographic Density Measures Associated with Breast Cancer Risk. Cancers (Basel) 2022; 14:cancers14061483. [PMID: 35326633 PMCID: PMC8946826 DOI: 10.3390/cancers14061483] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 02/22/2022] [Accepted: 03/08/2022] [Indexed: 01/10/2023] Open
Abstract
Simple Summary Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) that predict a woman’s risk of breast cancer based on mammographically dense areas when defined by different levels of brightness. We measured these MRS for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated how much these MRSs were correlated in relatives (ρ), how much of the differences between women were due to genetic factors (heritability), and how much these MRS explained why breast cancer runs in families. The ρ estimates ranged from: 0.41 to 0.60 for MZ pairs, 0.16 to 0.26 for DZ pairs, and 0.19 to 0.29 sister pairs, respectively. Heritability estimates were 36% to 69%. Genetic factors explain most of why twins and sisters are similar in their MRS, and these genetic factors explain one-quarter to one-half as much breast cancer risk as to the current best genetic risk score. Abstract Cumulus, Cumulus-percent, Altocumulus, Cirrocumulus, and Cumulus-white are mammogram risk scores (MRSs) for breast cancer based on mammographic density defined in effect by different levels of pixel brightness and adjusted for age and body mass index. We measured these MRS from digitized film mammograms for 593 monozygotic (MZ) and 326 dizygotic (DZ) female twin pairs and 1592 of their sisters. We estimated the correlations in relatives (r) and the proportion of variance due to genetic factors (heritability) using the software FISHER and predicted the familial risk ratio (FRR) associated with each MRS. The ρ estimates ranged from: 0.41 to 0.60 (standard error [SE] 0.02) for MZ pairs, 0.16 to 0.26 (SE 0.05) for DZ pairs, and 0.19 to 0.29 (SE 0.02) for sister pairs (including pairs of a twin and her non-twin sister), respectively. Heritability estimates were 39% to 69% under the classic twin model and 36% to 56% when allowing for shared non-genetic factors specific to MZ pairs. The FRRs were 1.08 to 1.17. These MRSs are substantially familial, due mostly to genetic factors that explain one-quarter to one-half as much of the familial aggregation of breast cancer that is explained by the current best polygenic risk score.
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13
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Wang J, Greuter MJW, Vermeulen KM, Brokken FB, Dorrius MD, Lu W, de Bock GH. Cost-effectiveness of abbreviated-protocol MRI screening for women with mammographically dense breasts in a national breast cancer screening program. Breast 2021; 61:58-65. [PMID: 34915447 PMCID: PMC8683595 DOI: 10.1016/j.breast.2021.12.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/07/2021] [Accepted: 12/09/2021] [Indexed: 12/25/2022] Open
Abstract
Introduction Magnetic resonance imaging (MRI) has shown the potential to improve the screening effectiveness among women with dense breasts. The introduction of fast abbreviated protocols (AP) makes MRI more feasible to be used in a general population. We aimed to investigate the cost-effectiveness of AP-MRI in women with dense breasts (heterogeneously/extremely dense) in a population-based screening program. Methods A previously validated model (SiMRiSc) was applied, with parameters updated for women with dense breasts. Breast density was assumed to decrease with increased age. The base scenarios included six biennial AP-MRI strategies, with biennial mammography from age 50–74 as reference. Fourteen alternative scenarios were performed by varying screening interval (triennial and quadrennial) and by applying a combined strategy of mammography and AP-MRI. A 3% discount rate for both costs and life years gained (LYG) was applied. Model robustness was evaluated using univariate and probabilistic sensitivity analyses. Results The six biennial AP-MRI strategies ranged from 132 to 562 LYG per 10,000 women, where more frequent application of AP-MRI was related to higher LYG. The optimal strategy was biennial AP-MRI screening from age 50–65 for only women with extremely dense breasts, producing an incremental cost-effectiveness ratio of € 18,201/LYG. At a threshold of € 20,000/LYG, the probability that the optimal strategy was cost-effective was 79%. Conclusion Population-based biennial breast cancer screening with AP-MRI from age 50–65 for women with extremely dense breasts might be a cost-effective alternative to mammography, but is not an option for women with heterogeneously dense breasts. AP-MRI can be cost-effective for screening women with extremely dense breast. The more frequent the use of AP-MRI, the more life years will be gained. Biennial AP-MRI for women with extremely dense breast up to age 65 is optimal.
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Affiliation(s)
- Jing Wang
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands.
| | - Marcel J W Greuter
- University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands.
| | - Karin M Vermeulen
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands.
| | - Frank B Brokken
- University of Groningen, Department of Computing Science, Groningen, the Netherlands.
| | - Monique D Dorrius
- University of Groningen, University Medical Center Groningen, Department of Radiology, Groningen, the Netherlands.
| | - Wenli Lu
- Department of Epidemiology and Health Statistics, Tianjin Medical University, Tianjin, China.
| | - Geertruida H de Bock
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, the Netherlands.
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14
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Lei S, Zheng R, Zhang S, Wang S, Chen R, Sun K, Zeng H, Zhou J, Wei W. Global patterns of breast cancer incidence and mortality: A population-based cancer registry data analysis from 2000 to 2020. Cancer Commun (Lond) 2021; 41:1183-1194. [PMID: 34399040 PMCID: PMC8626596 DOI: 10.1002/cac2.12207] [Citation(s) in RCA: 329] [Impact Index Per Article: 109.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/22/2021] [Accepted: 08/03/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Breast cancer is the most commonly diagnosed cancer and leading cause of cancer death among women worldwide but has patterns and trends which vary in different countries. This study aimed to evaluate the global patterns of breast cancer incidence and mortality and analyze its temporal trends for breast cancer prevention and control. METHODS Breast cancer incidence and mortality data in 2020 were obtained from the GLOBOCAN online database. Continued data from the Cancer Incidence in Five Continents Time Trends, the International Agency for Research on cancer mortality and China National Central Cancer Registry were used to analyze the time trends from 2000 to 2015 through Joinpoint regression, and annual average percent changes of breast cancer incidence and mortality were calculated. Association between Human Development Index and breast cancer incidence and mortality were estimated by linear regression. RESULTS There were approximately 2.3 million new breast cancer cases and 685,000 breast cancer deaths worldwide in 2020. Its incidence and mortality varied among countries, with the age-standardized incidence ranging from the highest of 112.3 per 100,000 population in Belgium to the lowest of 35.8 per 100,000 population in Iran, and the age-standardized mortality from the highest of 41.0 per 100,000 population in Fiji to the lowest of 6.4 per 100,000 population in South Korea. The peak age of breast cancer in some Asian and African countries were over 10 years earlier than in European or American countries. As for the trends of breast cancer, the age-standardized incidence rates significantly increased in China and South Korea but decreased in the United States of America (USA) during 2000-2012. Meanwhile, the age-standardized mortality rates significantly increased in China and South Korea but decreased in the United Kingdom, the USA, and Australia during 2000 and 2015. CONCLUSIONS The global burden of breast cancer is rising fast and varies greatly among countries. The incidence and mortality rates of breast cancer increased rapidly in China and South Korea but decreased in the USA. Increased health awareness, effective prevention strategies, and improved access to medical treatment are extremely important to curb the snowballing breast cancer burden, especially in the most affected countries.
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Affiliation(s)
- Shaoyuan Lei
- Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Rongshou Zheng
- Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Siwei Zhang
- Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Shaoming Wang
- Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Ru Chen
- Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Kexin Sun
- Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Hongmei Zeng
- Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
| | - Jiachen Zhou
- Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
- Department of Epidemiology and BiostatisticsSchool of Public HealthXi'an Jiaotong University Health Science CenterXi'anShaanxi710061P. R. China
| | - Wenqiang Wei
- Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100021P. R. China
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15
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Bonelli LA, Calabrese M, Belli P, Corcione S, Losio C, Montemezzi S, Pediconi F, Petrillo A, Zuiani C, Camera L, Carbonaro LA, Cozzi A, De Falco Alfano D, Gristina L, Panzeri M, Poirè I, Schiaffino S, Tosto S, Trecate G, Trimboli RM, Valdora F, Viganò S, Sardanelli F. MRI versus Mammography plus Ultrasound in Women at Intermediate Breast Cancer Risk: Study Design and Protocol of the MRIB Multicenter, Randomized, Controlled Trial. Diagnostics (Basel) 2021; 11:diagnostics11091635. [PMID: 34573983 PMCID: PMC8469187 DOI: 10.3390/diagnostics11091635] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 08/27/2021] [Accepted: 08/31/2021] [Indexed: 12/28/2022] Open
Abstract
In women at high/intermediate lifetime risk of breast cancer (BC-LTR), contrast-enhanced magnetic resonance imaging (MRI) added to mammography ± ultrasound (MX ± US) increases sensitivity but decreases specificity. Screening with MRI alone is an alternative and potentially more cost-effective strategy. Here, we describe the study protocol and the characteristics of enrolled patients for MRIB feasibility, multicenter, randomized, controlled trial, which aims to compare MRI alone versus MX+US in women at intermediate breast cancer risk (aged 40-59, with a 15-30% BC-LTR and/or extremely dense breasts). Two screening rounds per woman were planned in ten centers experienced in MRI screening, the primary endpoint being the rate of cancers detected in the 2 arms after 5 years of follow-up. From July 2013 to November 2015, 1254 women (mean age 47 years) were enrolled: 624 were assigned to MX+US and 630 to MRI. Most of them were aged below 50 (72%) and premenopausal (45%), and 52% used oral contraceptives. Among postmenopausal women, 15% had used hormone replacement therapy. Breast and/or ovarian cancer in mothers and/or sisters were reported by 37% of enrolled women, 79% had extremely dense breasts, and 41% had a 15-30% BC-LTR. The distribution of the major determinants of breast cancer risk profiles (breast density and family history of breast and ovarian cancer) of enrolled women varied across centers.
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Affiliation(s)
- Luigina Ada Bonelli
- Unit of Clinical Epidemiology, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy;
- Correspondence: ; Tel.: +39-010-5558502
| | - Massimo Calabrese
- Unit of Diagnostic Senology, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; (M.C.); (L.G.); (S.T.); (F.V.)
| | - Paolo Belli
- Department of Radiological, Radiotherapic and Hematological Sciences, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168 Roma, Italy;
| | - Stefano Corcione
- Breast Imaging Unit, Arcispedale Sant’Anna, Azienda Ospedaliero-Universitaria di Ferrara, 44124 Cona, Italy; (S.C.); (D.D.F.A.)
| | - Claudio Losio
- Unit of Senology, IRCCS Ospedale San Raffaele, 20132 Milano, Italy; (C.L.); (M.P.)
| | - Stefania Montemezzi
- Unit of Radiology BT, Azienda Ospedaliera Universitaria Integrata, 37126 Verona, Italy; (S.M.); (L.C.)
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences, Università degli Studi “La Sapienza”, 00161 Roma, Italy;
| | - Antonella Petrillo
- Radiology Unit, Istituto Nazionale dei Tumori IRCCS Fondazione G. Pascale, 80131 Napoli, Italy;
| | - Chiara Zuiani
- Institute of Radiology, Azienda Ospedaliera Universitaria “Santa Maria della Misericordia”, Università degli Studi di Udine, 33100 Udine, Italy;
| | - Lucia Camera
- Unit of Radiology BT, Azienda Ospedaliera Universitaria Integrata, 37126 Verona, Italy; (S.M.); (L.C.)
| | - Luca Alessandro Carbonaro
- Unit of Radiology, IRCCS Policlinico San Donato, 20097 San Donato Milanese, Italy; (L.A.C.); (S.S.); (F.S.)
- Department of Radiology, Grande Ospedale Metropolitano Niguarda, 20162 Milano, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, 20122 Milano, Italy
| | - Andrea Cozzi
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, 20133 Milano, Italy; (A.C.); (R.M.T.)
| | - Daniele De Falco Alfano
- Breast Imaging Unit, Arcispedale Sant’Anna, Azienda Ospedaliero-Universitaria di Ferrara, 44124 Cona, Italy; (S.C.); (D.D.F.A.)
- Mammography Center, Radiology Unit, Policlinico Sant’Orsola–Malpighi, 40138 Bologna, Italy
| | - Licia Gristina
- Unit of Diagnostic Senology, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; (M.C.); (L.G.); (S.T.); (F.V.)
| | - Marta Panzeri
- Unit of Senology, IRCCS Ospedale San Raffaele, 20132 Milano, Italy; (C.L.); (M.P.)
| | - Ilaria Poirè
- Unit of Clinical Epidemiology, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy;
| | - Simone Schiaffino
- Unit of Radiology, IRCCS Policlinico San Donato, 20097 San Donato Milanese, Italy; (L.A.C.); (S.S.); (F.S.)
| | - Simona Tosto
- Unit of Diagnostic Senology, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; (M.C.); (L.G.); (S.T.); (F.V.)
| | - Giovanna Trecate
- Department of Diagnostic Imaging, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milano, Italy; (G.T.); (S.V.)
| | - Rubina Manuela Trimboli
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, 20133 Milano, Italy; (A.C.); (R.M.T.)
- Breast Imaging and Screening Unit, Department of Radiology, Humanitas Clinical and Research Center—IRCCS, 20089 Rozzano, Italy
| | - Francesca Valdora
- Unit of Diagnostic Senology, IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy; (M.C.); (L.G.); (S.T.); (F.V.)
| | - Sara Viganò
- Department of Diagnostic Imaging, Fondazione IRCCS Istituto Nazionale dei Tumori, 20133 Milano, Italy; (G.T.); (S.V.)
| | - Francesco Sardanelli
- Unit of Radiology, IRCCS Policlinico San Donato, 20097 San Donato Milanese, Italy; (L.A.C.); (S.S.); (F.S.)
- Department of Biomedical Sciences for Health, Università degli Studi di Milano, 20133 Milano, Italy; (A.C.); (R.M.T.)
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16
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McCarthy AM, Ehsan S, Appel S, Welch M, He W, Bahl M, Chen J, Lehman CD, Armstrong K. Risk factors for an advanced breast cancer diagnosis within 2 years of a negative mammogram. Cancer 2021; 127:3334-3342. [PMID: 34061353 DOI: 10.1002/cncr.33661] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 11/11/2022]
Abstract
BACKGROUND Identifying women at risk for advanced interval cancers would allow better targeting of mammography and supplemental screening. The authors assessed risk factors for advanced breast cancer within 2 years of a negative mammogram. METHODS The authors included 293,520 negative mammograms performed from 2006 to 2015 among 74,736 women. Breast cancers were defined as advanced if they were >2 cm, were >1 cm and triple-negative or human epidermal growth factor receptor 2-positive, had positive lymph nodes, or were metastatic. Cox proportional hazards modeling was used to evaluate associations of age, breast density, menopause, mammogram type, prior breast biopsy, body mass index (BMI), and a family history of breast cancer with a cancer diagnosis within 2 years of a negative mammogram. Models were stratified by year since a negative mammogram. RESULTS Among 1345 breast cancers, 357 were advanced (26.5%), and 988 (73.5%) were at an early stage. Breast density, prior biopsy, and family history were associated with an increased risk of both advanced and early-stage cancers. Overweight and obese women had a 40% higher risk of early-stage cancer only in year 2 (overweight hazard ratio [HR], 1.41; 95% confidence interval [CI], 1.19-1.67; P < .001; obese HR, 1.41; 95% CI, 1.17-1.70; P < .001). Obese women had a 90% increased risk of advanced cancer in year 1 (HR, 1.90; 95% CI, 1.14-3.18; P = .014), and both overweight and obese women had a 40% or greater increased risk in year 2 (overweight HR, 1.55; 95% CI, 1.14-2.07; P = .005; obese HR, 1.42; 95% CI, 1.00-2.01; P = .051). CONCLUSIONS A higher BMI was associated with an advanced breast cancer diagnosis within 2 years of a negative mammogram. These results have important implications for risk assessment, screening intervals, and use of supplemental screening.
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Affiliation(s)
| | - Sarah Ehsan
- University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
| | - Scott Appel
- University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
| | | | - Wei He
- Massachusetts General Hospital, Boston, Massachusetts
| | - Manisha Bahl
- Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Jinbo Chen
- University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania
| | - Constance D Lehman
- Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Katrina Armstrong
- Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
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17
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Sehl ME, Henry JE, Storniolo AM, Horvath S, Ganz PA. The Effects of Lifetime Estrogen Exposure on Breast Epigenetic Age. Cancer Epidemiol Biomarkers Prev 2021; 30:1241-1249. [PMID: 33771849 DOI: 10.1158/1055-9965.epi-20-1297] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 11/16/2020] [Accepted: 03/19/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Estrogens are thought to contribute to breast cancer risk through cell cycling and accelerated breast aging. We hypothesize that lifetime estrogen exposure drives early epigenetic breast aging observed in healthy women. In this study, we examined associations between hormonal factors and epigenetic aging measures in healthy breast tissues. METHODS We extracted DNA from breast tissue specimens from 192 healthy female donors to the Susan G. Komen Tissue Bank at the Indiana University Simon Cancer Center. Methylation experiments were performed using the Illumina EPIC 850K array platform. Age-adjusted regression models were used to examine for associations between factors related to estrogen exposure and five DNA methylation-based estimates: Grim age, pan-tissue age, Hannum age, phenotypic age, and skin and blood clock age. RESULTS Women were aged 19-90 years, with 95 premenopausal, and 97 nulliparous women. The age difference (Grim age - chronologic age) was higher at earlier ages close to menarche. We found significant associations between earlier age at menarche and age-adjusted accelerations according to the Grim clock, the skin and blood clock, and between higher body mass index (BMI) and age-adjusted accelerations in the Grim clock, Hannum clock, phenotypic clock, and skin and blood clock. CONCLUSIONS Earlier age at menarche and higher BMI are associated with elevations in DNA methylation-based age estimates in healthy breast tissues, suggesting that cumulative estrogen exposure drives breast epigenetic aging. IMPACT Epigenetic clock measures may help advance inquiry into the relationship between accelerated breast tissue aging and an elevated incidence of breast cancer in younger women.
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Affiliation(s)
- Mary E Sehl
- Medicine, Hematology-Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California. .,Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Jill E Henry
- Susan G. Komen Tissue Bank at the Indiana University Simon Cancer Center, Indianapolis, Indiana
| | - Anna M Storniolo
- Susan G. Komen Tissue Bank at the Indiana University Simon Cancer Center, Indianapolis, Indiana
| | - Steve Horvath
- Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, California.,Department of Human Genetics, David Geffen School of Medicine, Gonda Research Center, University of California Los Angeles, California
| | - Patricia A Ganz
- Medicine, Hematology-Oncology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.,Health Policy and Management, Fielding School of Public Health, University of California Los Angeles, California
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18
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The P.I.N.K. Study Approach for Supporting Personalized Risk Assessment and Early Diagnosis of Breast Cancer. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18052456. [PMID: 33801528 PMCID: PMC7967589 DOI: 10.3390/ijerph18052456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 02/26/2021] [Accepted: 02/27/2021] [Indexed: 12/02/2022]
Abstract
Breast cancer is a clear example of excellent survival when it is detected and properly treated in the early stage. Currently, screening of this cancer relies on mammography, which may be integrated by new imaging techniques for more exhaustive evaluation. The Personalized, Integrated, Network, Knowledge (P.I.N.K.) study is a longitudinal multicentric study involving several diagnostic centres across Italy, co-ordinated by the Italian National Research Council and co-funded by the Umberto Veronesi Foundation. Aim of the study is to evaluate the increased diagnostic accuracy in detecting cancers obtained with different combinations of imaging technologies, and find the most effective diagnostic pathway matching the characteristics of an individual patient. The study foresees the enrolment of 50,000 women over the age of 40 years presenting for breast examination and providing informed consent to data handling. So far, the 15 participating centres across Italy have recruited a total of 22,848 patients. Based on the analyses of the first 175 histopathological-proven breast cancers, mammographic sensitivity was estimated to be 61.7% (n = 108 cancers), whereas diagnostic accuracy increased by 35.5% (n = 44 cancers) when mammography was integrated with other imaging modalities (ultrasound and/or digital breast tomosynthesis). Increase was mainly determined by ultrasound alone. Given the ongoing data collection and recruitment, the number of cancers detected is too low to allow any further in-depth analysis to explore links to patient characteristics. Past studies show that the uniform approach of population screening guidelines should be revised in favour of more personalised regimens, where known standards are integrated by imaging techniques most suitable for the individual’s characteristics. With the ultimate goal of identifying early breast cancer detection strategies, our preliminary results suggest that integrated diagnostic approach could lead to a paradigm shift from an age-based regimen toward more specific and effective risk-based personalised screening regimens, in order to reduce mortality from breast cancer.
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19
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Northey JJ, Barrett AS, Acerbi I, Hayward MK, Talamantes S, Dean IS, Mouw JK, Ponik SM, Lakins JN, Huang PJ, Wu J, Shi Q, Samson S, Keely PJ, Mukhtar RA, Liphardt JT, Shepherd JA, Hwang ES, Chen YY, Hansen KC, Littlepage LE, Weaver VM. Stiff stroma increases breast cancer risk by inducing the oncogene ZNF217. J Clin Invest 2021; 130:5721-5737. [PMID: 32721948 DOI: 10.1172/jci129249] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/14/2020] [Indexed: 12/14/2022] Open
Abstract
Women with dense breasts have an increased lifetime risk of malignancy that has been attributed to a higher epithelial density. Quantitative proteomics, collagen analysis, and mechanical measurements in normal tissue revealed that stroma in the high-density breast contains more oriented, fibrillar collagen that is stiffer and correlates with higher epithelial cell density. microRNA (miR) profiling of breast tissue identified miR-203 as a matrix stiffness-repressed transcript that is downregulated by collagen density and reduced in the breast epithelium of women with high mammographic density. Culture studies demonstrated that ZNF217 mediates a matrix stiffness- and collagen density-induced increase in Akt activity and mammary epithelial cell proliferation. Manipulation of the epithelium in a mouse model of mammographic density supported a causal relationship between stromal stiffness, reduced miR-203, higher levels of the murine homolog Zfp217, and increased Akt activity and mammary epithelial proliferation. ZNF217 was also increased in the normal breast epithelium of women with high mammographic density, correlated positively with epithelial proliferation and density, and inversely with miR-203. The findings identify ZNF217 as a potential target toward which preexisting therapies, such as the Akt inhibitor triciribine, could be used as a chemopreventive agent to reduce cancer risk in women with high mammographic density.
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Affiliation(s)
- Jason J Northey
- Department of Surgery.,Center for Bioengineering and Tissue Regeneration, UCSF, San Francisco, California, USA
| | - Alexander S Barrett
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colorado, USA
| | - Irene Acerbi
- Department of Surgery.,Center for Bioengineering and Tissue Regeneration, UCSF, San Francisco, California, USA
| | - Mary-Kate Hayward
- Department of Surgery.,Center for Bioengineering and Tissue Regeneration, UCSF, San Francisco, California, USA
| | - Stephanie Talamantes
- Department of Surgery.,Center for Bioengineering and Tissue Regeneration, UCSF, San Francisco, California, USA
| | - Ivory S Dean
- Department of Surgery.,Center for Bioengineering and Tissue Regeneration, UCSF, San Francisco, California, USA
| | - Janna K Mouw
- Department of Surgery.,Center for Bioengineering and Tissue Regeneration, UCSF, San Francisco, California, USA
| | - Suzanne M Ponik
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jonathon N Lakins
- Department of Surgery.,Center for Bioengineering and Tissue Regeneration, UCSF, San Francisco, California, USA
| | - Po-Jui Huang
- Department of Surgery.,Center for Bioengineering and Tissue Regeneration, UCSF, San Francisco, California, USA
| | - Junmin Wu
- Harper Cancer Research Institute, Department of Chemistry and Biochemistry, University of Notre Dame, South Bend, Indiana, USA
| | - Quanming Shi
- Department of Bioengineering, Stanford University, Palo Alto, California, USA
| | - Susan Samson
- Helen Diller Comprehensive Cancer Center, UCSF, San Francisco, California, USA
| | - Patricia J Keely
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | | | - Jan T Liphardt
- Department of Bioengineering, Stanford University, Palo Alto, California, USA
| | - John A Shepherd
- Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaii Cancer Center, University of Hawaii at Manoa, Manoa, Hawaii, USA
| | - E Shelley Hwang
- Department of Surgery, Duke University Medical Center, Durham, North Carolina, USA
| | - Yunn-Yi Chen
- Department of Pathology, UCSF, San Francisco, California, USA
| | - Kirk C Hansen
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Anschutz Medical Campus, Aurora, Colorado, USA.,Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Laurie E Littlepage
- Harper Cancer Research Institute, Department of Chemistry and Biochemistry, University of Notre Dame, South Bend, Indiana, USA
| | - Valerie M Weaver
- Department of Surgery.,Center for Bioengineering and Tissue Regeneration, UCSF, San Francisco, California, USA.,Helen Diller Comprehensive Cancer Center, UCSF, San Francisco, California, USA.,Population Sciences in the Pacific Program (Cancer Epidemiology), University of Hawaii Cancer Center, University of Hawaii at Manoa, Manoa, Hawaii, USA.,Radiation Oncology, Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, UCSF, San Francisco, California, USA
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20
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Hopper JL, Nguyen TL, Schmidt DF, Makalic E, Song YM, Sung J, Dite GS, Dowty JG, Li S. Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk. J Clin Med 2020; 9:jcm9030627. [PMID: 32110975 PMCID: PMC7141100 DOI: 10.3390/jcm9030627] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 02/15/2020] [Accepted: 02/17/2020] [Indexed: 12/12/2022] Open
Abstract
This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.
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