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1. Reproductive factors and mammographic density within the International Consortium of Mammographic Density: A cross-sectional study

Author

O’Driscoll, Jessica, Burton, Anya, Maskarinec, Gertraud, Perez-Gomez, Beatriz, Vachon, Celine, Miao, Hui, Lajous, Martín, López-Ridaura, Ruy, Eliassen, A. Heather, Pereira, Ana, Garmendia, Maria Luisa, Tamimi, Rulla M., Bertrand, Kimberly, Kwong, Ava, Ursin, Giske, Lee, Eunjung, Qureshi, Samera A., Ma, Huiyan, Vinnicombe, Sarah, Moss, Sue, Allen, Steve, Ndumia, Rose, Vinayak, Sudhir, Teo, Soo-Hwang, Mariapun, Shivaani, Fadzli, Farhana, Peplonska, Beata, Nagata, Chisato, Stone, Jennifer, Hopper, John L., Giles, Graham, Ozmen, Vahit, Aribal, Mustafa Erkin, Schüz, Joachim, Van Gils, Carla H., Wanders, Johanna O. P., Sirous, Reza, Sirous, Mehri, Hipwell, John, Kim, Jisun, Lee, Jong Won, Hartman, Mikael, Li, Jingmei, Scott, Christopher, Chiarelli, Anna M., Linton, Linda, Pollan, Marina, Flugelman, Anath Arzee, Salem, Dorria, Kamal, Rasha, Boyd, Norman, dos-Santos-Silva, Isabel, McCormack, Valerie, and Mullooly, Maeve

Published
2024
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3. The association of age at menarche and adult height with mammographic density in the International Consortium of Mammographic Density

Author

Ward, Sarah V., Burton, Anya, Tamimi, Rulla M., Pereira, Ana, Garmendia, Maria Luisa, Pollan, Marina, Boyd, Norman, dos-Santos-Silva, Isabel, Maskarinec, Gertraud, Perez-Gomez, Beatriz, Vachon, Celine, Miao, Hui, Lajous, Martín, López-Ridaura, Ruy, Bertrand, Kimberly, Kwong, Ava, Ursin, Giske, Lee, Eunjung, Ma, Huiyan, Vinnicombe, Sarah, Moss, Sue, Allen, Steve, Ndumia, Rose, Vinayak, Sudhir, Teo, Soo-Hwang, Mariapun, Shivaani, Peplonska, Beata, Bukowska-Damska, Agnieszka, Nagata, Chisato, Hopper, John, Giles, Graham, Ozmen, Vahit, Aribal, Mustafa Erkin, Schüz, Joachim, Van Gils, Carla H., Wanders, Johanna O. P., Sirous, Reza, Sirous, Mehri, Hipwell, John, Kim, Jisun, Lee, Jong Won, Dickens, Caroline, Hartman, Mikael, Chia, Kee-Seng, Scott, Christopher, Chiarelli, Anna M., Linton, Linda, Flugelman, Anath Arzee, Salem, Dorria, Kamal, Rasha, McCormack, Valerie, and Stone, Jennifer

Published
2022
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5. Updated breast cancer costs for women by disease stage and phase of care using population-based databases.

Author

Mittmann, Nicole, Soo Jin Seung, Ante, Zharmaine, Ning Liu, Yong, Jean H. E., Yusuf, Abeer, Chiarelli, Anna M., and Earle, Craig C.

Subjects
METASTATIC breast cancer, ECONOMIC aspects of diseases, BREAST cancer, MEDICAL care costs, DIRECT costing
Abstract

Background This study assessed health care system costs and resource utilization for adult women with breast cancer in Ontario, Canada. The goal was to update costs by stage, age, and phase of care from a health care system perspective. Data and methods A retrospective analysis was conducted using linked population-based administrative data. The study included women diagnosed with breast cancer from 2017 to 2021, with follow-up data until 2022. Cases were matched with controls in a 1:5 ratio using birth year, local health integrative network, income quintile, and resource utilization band at baseline. Incremental costs were estimated using linear regression. The modified income quintile was the neighbourhood- or area-level income quintile. Results Among the 37,133 cases matched with 185,665 controls, the average age at diagnosis was 62 years. For the entire study duration, cases incurred an additional cost of $27,485 per year, compared with controls. Costs rose with disease severity, ranging from $15,588 for stage I to $137,319 for stage IV. The highest incremental costs occurred during the first 12 months after diagnosis (initial: $43,408), followed by the last 12 months before death (terminal: $25,940), and then interim years (continuous: $9,533 per year). Additionally, the incremental cost of breast cancer was higher when diagnosis was before age 70 ($28,415), compared with diagnosis at age 70 and older ($25,254). Interpretation The findings align with previous studies on breast cancer costs for the health care system. Additionally, variations in costs based on disease severity, care phase, and age were emphasized, highlighting higher costs for metastatic breast cancer cases, women younger than 70 years, and the initial 12 months following diagnosis. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Evaluating Real World Health System Resource Utilization and Costs for a Risk-Based Breast Cancer Screening Approach in the Canadian PERSPECTIVE Integration and Implementation Project.

Author

Seung, Soo-Jin, Mittmann, Nicole, Ante, Zharmaine, Liu, Ning, Blackmore, Kristina M., Richard, Emilie S., Wong, Anisia, Walker, Meghan J., Earle, Craig C., Simard, Jacques, and Chiarelli, Anna M.

Subjects
BREAST tumor diagnosis, BREAST tumor treatment, MEDICAL care use, RISK assessment, LIFESTYLES, HUMAN services programs, RESEARCH funding, COST effectiveness, EARLY detection of cancer, CANCER patient medical care, PRESUMPTIONS (Law), FAMILY history (Medicine), GENETIC counseling, MAGNETIC resonance imaging, DESCRIPTIVE statistics, LONGITUDINAL method, GENETIC risk score, BREAST physiology, MAMMOGRAMS, DATA analysis software, MEDICAL care costs
Abstract

Simple Summary: There is a current gap in understanding the costs and healthcare resources used related to breast cancer risk assessment and screening. Risk-stratified breast screening overcomes various limitations of age-based screening, and participants are classified based on personalized differences in breast cancer risk. The overall goal of the PERSPECTIVE I&I project was to inform the implementation of more effective strategies of risk assessment for risk-stratified screening and earlier detection of breast cancer. This study appears to be the first to examine the healthcare utilization and costs stratified by the three breast cancer risk levels, determining the economic burden and resource needs linked with different risk categories. This study demonstrated that despite higher screening-related costs for high-risk individuals, overall healthcare costs were comparable across risk categories. Background: A prospective cohort study was undertaken within the PERSPECTIVE I&I project to evaluate healthcare resource utilization and costs associated with breast cancer risk assessment and screening and overall costs stratified by risk level, in Ontario, Canada. Methods: From July 2019 to December 2022, 1997 females aged 50 to 70 years consented to risk assessment and received their breast cancer risk level and personalized screening action plan in Ontario. The mean costs for risk-stratified screening-related activities included risk assessment, screening and diagnostic costs. The GETCOST macro from the Institute of Clinical Evaluative Sciences (ICES) assessed the mean overall healthcare system costs. Results: For the 1997 participants, 83.3%, 14.4% and 2.3% were estimated to be average, higher than average, and high risk, respectively (median age (IQR): 60 [56–64] years). Stratification into the three risk levels was determined using the validated multifactorial CanRisk prediction tool that includes family history information, a polygenic risk score (PRS), breast density and established lifestyle/hormonal risk factors. The mean number of genetic counseling visits, mammograms and MRIs per individual increased with risk level. High-risk participants incurred the highest overall mean risk-stratified screening-related costs in 2022 CAD (±SD) at CAD 905 (±269) followed by CAD 580 (±192) and CAD 521 (±163) for higher-than-average and average-risk participants, respectively. Among the breast screening-related costs, the greatest cost burden across all risk groups was the risk assessment cost, followed by total diagnostic and screening costs. The mean overall healthcare cost per participant (±SD) was the highest for the average risk participants with CAD 6311 (±19,641), followed by higher than average risk with CAD 5391 (±8325) and high risk with CAD 5169 (±7676). Conclusion: Although high-risk participants incurred the highest risk-stratified screening-related costs, their costs for overall healthcare utilization costs were similar to other risk levels. Our study underscored the importance of integrating risk stratification as part of the screening pathway to support breast cancer detection at an earlier and more treatable stage, thereby reducing costs and the overall burden on the healthcare system. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Comparison of wait times across the breast cancer treatment pathway among screened women undergoing organized breast assessment versus usual care

Author

Blackmore, Kristina M., Weerasinghe, Ashini, Holloway, Claire M. B., Majpruz, Vicky, Mirea, Lucia, O’Malley, Frances P., Harris, Cathy Paroschy, Hendry, Ashley, Hey, Amanda, Kornecki, Anat, Lougheed, George, Maier, Barbara-Anne, Marchand, Patricia, McCready, David, Rand, Carol, Raphael, Simon, Segal-Nadler, Roanne, Sehgal, Neelu, Muradali, Derek, and Chiarelli, Anna M.

Published
2019

9. The impact of organized breast assessment on survival by stage for screened women diagnosed with invasive breast cancer

Author

Smith, Courtney R., Chiarelli, Anna M., Holloway, Claire MB., Mirea, Lucia, O'Malley, Frances P., Blackmore, Kristina M., Pandya, Anjali, Majpruz, Vicky, Harris, Cathy Paroschy, Hendry, Ashley, Hey, Amanda, Kornecki, Anat, Lougheed, George, Maier, Barbara-Anne, Marchand, Patricia, McCready, David, Rand, Carol, Raphael, Simon, Segal-Nadler, Roanne, Sehgal, Neelu, and Muradali, Derek

Published
2018
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15. International Consortium on Mammographic Density: Methodology and population diversity captured across 22 countries

Author

McCormack, Valerie A., Burton, Anya, dos-Santos-Silva, Isabel, Hipwell, John H., Dickens, Caroline, Salem, Dorria, Kamal, Rasha, Hartman, Mikael, Lee, Charmaine Pei Ling, Chia, Kee-Seng, Ozmen, Vahit, Aribal, Mustafa Erkin, Flugelman, Anath Arzee, Lajous, Martín, Lopez-Riduara, Ruy, Rice, Megan, Romieu, Isabelle, Ursin, Giske, Qureshi, Samera, Ma, Huiyan, Lee, Eunjung, van Gils, Carla H., Wanders, Johanna O.P., Vinayak, Sudhir, Ndumia, Rose, Allen, Steve, Vinnicombe, Sarah, Moss, Sue, Won Lee, Jong, Kim, Jisun, Pereira, Ana, Garmendia, Maria Luisa, Sirous, Reza, Sirous, Mehri, Peplonska, Beata, Bukowska, Agnieszka, Tamimi, Rulla M., Bertrand, Kimberly, Nagata, Chisato, Kwong, Ava, Vachon, Celine, Scott, Christopher, Perez-Gomez, Beatriz, Pollan, Marina, Maskarinec, Gertraud, Giles, Graham, Hopper, John, Stone, Jennifer, Rajaram, Nadia, Teo, Soo-Hwang, Mariapun, Shivaani, Yaffe, Martin J., Schüz, Joachim, Chiarelli, Anna M., Linton, Linda, and Boyd, Norman F.

Published
2016
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16. Incorporating Alternative Polygenic Risk Scores into the BOADICEA Breast Cancer Risk Prediction Model

Author

Mavaddat, Nasim, Ficorella, Lorenzo, Carver, Tim, Lee, Andrew, Cunningham, Alex P., Lush, Michael, Dennis, Joe, Tischkowitz, Marc, Downes, Kate, Hu, Donglei, Hahnen, Eric, Schmutzler, Rita K., Stockley, Tracy L., Downs, Gregory S., Zhang, Tong, Chiarelli, Anna M., Bojesen, Stig E., Liu, Cong, Chung, Wendy K., Pardo, Monica, Feliubadalo, Lidia, Balmaña, Judith, Simard, Jacques, Antoniou, Antonis C., Easton, Douglas F., Mavaddat, Nasim, Ficorella, Lorenzo, Carver, Tim, Lee, Andrew, Cunningham, Alex P., Lush, Michael, Dennis, Joe, Tischkowitz, Marc, Downes, Kate, Hu, Donglei, Hahnen, Eric, Schmutzler, Rita K., Stockley, Tracy L., Downs, Gregory S., Zhang, Tong, Chiarelli, Anna M., Bojesen, Stig E., Liu, Cong, Chung, Wendy K., Pardo, Monica, Feliubadalo, Lidia, Balmaña, Judith, Simard, Jacques, Antoniou, Antonis C., and Easton, Douglas F.

Published
2023

17. Incorporating alternative Polygenic Risk Scores into the BOADICEA breast cancer risk prediction model

Author

Mavaddat, Nasim, Ficorella, Lorenzo, Carver, Tim, Lee, Andrew, Cunningham, Alex P, Lush, Michael, Dennis, Joe, Tischkowitz, Marc, Downes, Kate, Hu, Donglei, Hahnen, Eric, Schmutzler, Rita K, Stockley, Tracy L, Downs, Gregory S, Zhang, Tong, Chiarelli, Anna M, Bojesen, Stig E, Liu, Cong, Chung, Wendy K, Pardo, Monica, Feliubadaló, Lidia, Balmaña, Judith, Simard, Jacques, Antoniou, Antonis C, Easton, Douglas F, Mavaddat, Nasim [0000-0003-0307-055X], Ficorella, Lorenzo [0000-0002-0577-1571], Carver, Tim [0000-0003-1508-3091], Lee, Andrew [0000-0003-0677-0252], Cunningham, Alex P [0000-0002-3737-9611], Lush, Michael [0000-0001-5945-3440], Dennis, Joe [0000-0003-4591-1214], Tischkowitz, Marc [0000-0002-7880-0628], Downes, Kate [0000-0003-0366-1579], Hu, Donglei [0000-0002-0351-001X], Hahnen, Eric [0000-0002-1152-8367], Schmutzler, Rita K [0000-0001-8160-4348], Stockley, Tracy L [0000-0002-4476-9722], Downs, Gregory S [0000-0002-5622-9010], Zhang, Tong [0000-0001-7108-0974], Chiarelli, Anna M [0000-0002-7382-513X], Bojesen, Stig E [0000-0002-4061-4133], Liu, Cong [0000-0001-6024-3037], Chung, Wendy K [0000-0003-3438-5685], Pardo, Monica [0000-0003-2015-9564], Feliubadaló, Lidia [0000-0002-1736-0112], Balmaña, Judith [0000-0002-0762-6415], Simard, Jacques [0000-0001-6906-3390], Antoniou, Antonis C [0000-0001-9223-3116], Easton, Douglas F [0000-0003-2444-3247], Apollo - University of Cambridge Repository, Institut Català de la Salut, [Mavaddat N, Ficorella L, Carver T, Lee A, Cunningham AP, Lush M] Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom. [Pardo M] Hereditary Cancer Genetics Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. [Balmaña J] Hereditary Cancer Genetics Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. Servei d’Oncologia Mèdica, Vall d’Hebron Hospital Universitari, Barcelona, Spain, and Vall d'Hebron Barcelona Hospital Campus

Subjects
neoplasias::neoplasias por localización::neoplasias de la mama [ENFERMEDADES], Epidemiology, Otros calificadores::Otros calificadores::/genética [Otros calificadores], Neoplasms::Neoplasms by Site::Breast Neoplasms [DISEASES], Genetic Phenomena::Genotype::Genetic Predisposition to Disease [PHENOMENA AND PROCESSES], Breast Neoplasms, técnicas de investigación::métodos epidemiológicos::estadística como asunto::probabilidad::riesgo::factores de riesgo [TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS], Risk Assessment, Polymorphism, Single Nucleotide, Mama - Càncer - Factors de risc, Oncology, Risk Factors, Mama - Càncer - Diàtesi, Other subheadings::Other subheadings::/genetics [Other subheadings], Mama - Càncer - Aspectes genètics, Humans, Female, Genetic Predisposition to Disease, Investigative Techniques::Epidemiologic Methods::Statistics as Topic::Probability::Risk::Risk Factors [ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT], fenómenos genéticos::genotipo::predisposición genética a la enfermedad [FENÓMENOS Y PROCESOS], Retrospective Studies
Abstract

Polygenic risk; Prediction; Breast cancer Riesgo poligénico; Predicción; Cáncer de mama Risc poligènic; Predicció; Càncer de mama Background: The multifactorial risk prediction model BOADICEA enables identification of women at higher or lower risk of developing breast cancer. BOADICEA models genetic susceptibility in terms of the effects of rare variants in breast cancer susceptibility genes and a polygenic component, decomposed into an unmeasured and a measured component - the polygenic risk score (PRS). The current version was developed using a 313 SNP PRS. Here, we evaluated approaches to incorporating this PRS and alternative PRS in BOADICEA. Methods: The mean, SD, and proportion of the overall polygenic component explained by the PRS (α2) need to be estimated. α was estimated using logistic regression, where the age-specific log-OR is constrained to be a function of the age-dependent polygenic relative risk in BOADICEA; and using a retrospective likelihood (RL) approach that models, in addition, the unmeasured polygenic component. Results: Parameters were computed for 11 PRS, including 6 variations of the 313 SNP PRS used in clinical trials and implementation studies. The logistic regression approach underestimates α⁠, as compared with the RL estimates. The RL α estimates were very close to those obtained by assuming proportionality to the OR per 1 SD, with the constant of proportionality estimated using the 313 SNP PRS. Small variations in the SNPs included in the PRS can lead to large differences in the mean. Conclusions: BOADICEA can be readily adapted to different PRS in a manner that maintains consistency of the model. This work has been supported by grants from Cancer Research UK (PPRPGM-Nov20\100002); the European Union's Horizon 2020 Research and Innovation Programme under grant agreement numbers 633784 (B-CAST) and 634935 (BRIDGES); the PERSPECTIVE I&I project which is funded by the Government of Canada through Genome Canada (#13529) and the Canadian Institutes of Health Research (#155865), the Ministère de l’Économie et de l'Innovation du Québec through Genome Québec, the Quebec Breast Cancer Foundation, the CHU de Quebec Foundation and the Ontario Research Fund; and by the NIHR Cambridge Biomedical Research Centre (BRC-1215–20014). BCAC is funded by the European Union's Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and the PERSPECTIVE I&I project. Additional funding for BCAC is provided via the Confluence project which is funded with intramural funds from the NCI Intramural Research Program, NIH. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer Research UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. MT was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215–20014) and Cancer Research UK C22770/A31523 (International Alliance for Cancer Early Detection programme). The PRISMA study has been funded by Instituto de Salud Carlos III through the project " PI19/01195″ (Co-funded by European Regional Development Fund "A way to make Europe") and it received the institutional support of CERCA Program (Generalitat de Catalunya). The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Published
2022
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18. Incorporating alternative Polygenic Risk Scores into the BOADICEA breast cancer risk prediction model

Author

Mavaddat, Nasim, primary, Ficorella, Lorenzo, additional, Carver, Tim, additional, Lee, Andrew, additional, Cunningham, Alex P., additional, Lush, Michael, additional, Dennis, Joe, additional, Tischkowitz, Marc, additional, Downes, Kate, additional, Hu, Donglei, additional, Hahnen, Eric, additional, Schmutzler, Rita K., additional, Stockley, Tracy L., additional, Downs, Gregory S., additional, Zhang, Tong, additional, Chiarelli, Anna M., additional, Bojesen, Stig E., additional, Liu, Cong, additional, Chung, Wendy K., additional, Pardo, Monica, additional, Feliubadaló, Lidia, additional, Balmaña, Judith, additional, Simard, Jacques, additional, Antoniou, Antonis C., additional, and Easton, Douglas F., additional

Published
2023
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30. Additional file 1 of The association of age at menarche and adult height with mammographic density in the International Consortium of Mammographic Density

Author

Ward, Sarah V., Burton, Anya, Tamimi, Rulla M., Pereira, Ana, Garmendia, Maria Luisa, Pollan, Marina, Boyd, Norman, dos-Santos-Silva, Isabel, Maskarinec, Gertraud, Perez-Gomez, Beatriz, Vachon, Celine, Miao, Hui, Lajous, Martín, López-Ridaura, Ruy, Bertrand, Kimberly, Kwong, Ava, Ursin, Giske, Lee, Eunjung, Ma, Huiyan, Vinnicombe, Sarah, Moss, Sue, Allen, Steve, Ndumia, Rose, Vinayak, Sudhir, Teo, Soo-Hwang, Mariapun, Shivaani, Peplonska, Beata, Bukowska-Damska, Agnieszka, Nagata, Chisato, Hopper, John, Giles, Graham, Ozmen, Vahit, Aribal, Mustafa Erkin, Schüz, Joachim, Van Gils, Carla H., Wanders, Johanna O. P., Sirous, Reza, Sirous, Mehri, Hipwell, John, Kim, Jisun, Lee, Jong Won, Dickens, Caroline, Hartman, Mikael, Chia, Kee-Seng, Scott, Christopher, Chiarelli, Anna M., Linton, Linda, Flugelman, Anath Arzee, Salem, Dorria, Kamal, Rasha, McCormack, Valerie, and Stone, Jennifer

Abstract

Additional file 1. Tables S1 and S2: Supplementary data providing results from sensitivity analyses of pooled models adjusted for a population-specific weight-for-height index instead of BMI

Published
2022
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34. List of Contributors

Author

Astley, Susan, primary, Barratt, Alexandra L., additional, Broeders, Mireille J.M., additional, Cameron, David, additional, Carter, Stacy M., additional, Chiarelli, Anna M., additional, de Bock, Geertruida H., additional, Dixon, J. Michael, additional, Elmore, Joann G., additional, Evans, D. Gareth, additional, Fu, Rongwei, additional, Harris, Russell P., additional, Hersch, Jolyn, additional, Houssami, Nehmat, additional, Howell, Anthony, additional, Hubbard, Rebecca A., additional, Humphrey, Linda L., additional, Jacklyn, Gemma L., additional, Jansen, Jesse, additional, Kerlikowske, Karla, additional, Lee, Janie M., additional, Maxwell, Anthony J., additional, McCaffery, Kirsten, additional, Miglioretti, Diana L., additional, Nelson, Heidi D., additional, Parker, Lisa M., additional, Phi, Xuan-Anh, additional, Ripping, Theodora M., additional, Schechter, Clyde B., additional, Schonberg, Mara A., additional, Tice, Jeffrey A., additional, van Ravesteyn, Nicolien T., additional, and Zackrisson, Sophia, additional

Published
2016
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40. The impact of pregnancy on breast cancer survival in women who carry a BRCA1 or BRCA2 mutation

Author

Valentini, Adriana, Lubinski, Jan, Byrski, Tomasz, Ghadirian, Parviz, Moller, Pal, Lynch, Henry T., Ainsworth, Peter, Neuhausen, Susan L., Weitzel, Jeffrey, Singer, Christian F., Olopade, Olufunmilayo I., Saal, Howard, Lyonnet, Dominique Stoppa, Foulkes, William D., Kim-Sing, Charmaine, Manoukian, Siranoush, Zakalik, Dana, Armel, Susan, Senter, Leigha, Eng, Charis, Grunfeld, Eva, Chiarelli, Anna M., Poll, Aletta, Sun, Ping, Narod, Steven A., and The Hereditary Breast Cancer Clinical Study Group

Published
2013
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41. Adherence to guidance for prioritizing higher risk groups for breast cancer screening during the COVID-19 pandemic in the Ontario Breast Screening Program: a descriptive study

Author

Chiarelli, Anna M., primary, Walker, Meghan J., additional, Espino-Hernandez, Gabriela, additional, Gray, Natasha, additional, Salleh, Ayesha, additional, Adhihetty, Chamila, additional, Gao, Julia, additional, Fienberg, Samantha, additional, Rey, Michelle A., additional, and Rabeneck, Linda, additional

Published
2021
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42. Should Age-Dependent Absolute Risk Thresholds Be Used for Risk Stratification in Risk-Stratified Breast Cancer Screening?

Author

Pashayan, Nora, primary, Antoniou, Antonis C., additional, Lee, Andrew, additional, Wolfson, Michael, additional, Chiquette, Jocelyne, additional, Eloy, Laurence, additional, Eisen, Andrea, additional, Stockley, Tracy L., additional, Nabi, Hermann, additional, Brooks, Jennifer D., additional, Dorval, Michel, additional, Easton, Douglas F., additional, Knoppers, Bartha Maria, additional, Chiarelli, Anna M., additional, and Simard, Jacques, additional

Published
2021
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44. Mammographic Density Phenotypes and Risk of Breast Cancer: A Meta-analysis

Author

Pettersson, Andreas, Graff, Rebecca E., Ursin, Giske, dos Santos Silva, Isabel, McCormack, Valerie, Baglietto, Laura, Vachon, Celine, Bakker, Marije F., Giles, Graham G., Chia, Kee Seng, Czene, Kamila, Eriksson, Louise, Hall, Per, Hartman, Mikael, Warren, Ruth M. L., Hislop, Greg, Chiarelli, Anna M., Hopper, John L., Krishnan, Kavitha, Li, Jingmei, Li, Qing, Pagano, Ian, Rosner, Bernard A., Wong, Chia Siong, Scott, Christopher, Stone, Jennifer, Maskarinec, Gertraud, Boyd, Norman F., van Gils, Carla H., and Tamimi, Rulla M.

Published
2014
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46. Breast Density and Risk of Interval Cancers: The Effect of Annual Versus Biennial Screening Mammography Policies in Canada

Author

Seely, Jean Morag, primary, Peddle, Susan Elizabeth, additional, Yang, Huiming, additional, Chiarelli, Anna M., additional, McCallum, Megan, additional, Narasimhan, Gopinath, additional, Zakaria, Dianne, additional, Earle, Craig C., additional, Fung, Sharon, additional, Bryant, Heather, additional, Nicholson, Erika, additional, Politis, Chris, additional, and Berg, Wendie A., additional

Published
2021
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47. Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide

Author

Burton, Anya, Maskarinec, Gertraud, Perez-Gomez, Beatriz, Vachon, Celine, Miao, Hui, Lajous, Martín, López-Ridaura, Ruy, Rice, Megan, Pereira, Ana, Garmendia, Maria Luisa, Tamimi, Rulla M., Bertrand, Kimberly, Kwong, Ava, Ursin, Giske, Lee, Eunjung, Qureshi, Samera A., Ma, Huiyan, Vinnicombe, Sarah, Moss, Sue, Allen, Steve, Ndumia, Rose, Vinayak, Sudhir, Teo, Soo-Hwang, Mariapun, Shivaani, Fadzli, Farhana, Peplonska, Beata, Bukowska, Agnieszka, Nagata, Chisato, Stone, Jennifer, Hopper, John, Giles, Graham, Ozmen, Vahit, Aribal, Mustafa Erkin, Schüz, Joachim, Van Gils, Carla H., Wanders, Johanna O. P., Sirous, Reza, Sirous, Mehri, Hipwell, John, Kim, Jisun, Lee, Jong Won, Dickens, Caroline, Hartman, Mikael, Chia, Kee-Seng, Scott, Christopher, Chiarelli, Anna M., Linton, Linda, Pollan, Marina, Flugelman, Anath Arzee, Salem, Dorria, Kamal, Rasha, Boyd, Norman, dos-Santos-Silva, Isabel, and McCormack, Valerie

Subjects
Usage, Measurement, Health aspects, Mammography -- Usage, Postmenopausal women -- Health aspects, Body mass index -- Measurement
Abstract

Author(s): Anya Burton 1, Gertraud Maskarinec 2, Beatriz Perez-Gomez 3,4, Celine Vachon 5, Hui Miao 6, Martín Lajous 7, Ruy López-Ridaura 7, Megan Rice 8, Ana Pereira 9, Maria Luisa [...], Background Mammographic density (MD) is one of the strongest breast cancer risk factors. Its age-related characteristics have been studied in women in western countries, but whether these associations apply to women worldwide is not known. Methods and findings We examined cross-sectional differences in MD by age and menopausal status in over 11,000 breast-cancer-free women aged 35-85 years, from 40 ethnicity- and location-specific population groups across 22 countries in the International Consortium on Mammographic Density (ICMD). MD was read centrally using a quantitative method (Cumulus) and its square-root metrics were analysed using meta-analysis of group-level estimates and linear regression models of pooled data, adjusted for body mass index, reproductive factors, mammogram view, image type, and reader. In all, 4,534 women were premenopausal, and 6,481 postmenopausal, at the time of mammography. A large age-adjusted difference in percent MD (PD) between post- and premenopausal women was apparent (-0.46 cm [95% CI: -0.53, -0.39]) and appeared greater in women with lower breast cancer risk profiles; variation across population groups due to heterogeneity (I.sup.2) was 16.5%. Among premenopausal women, the [square root]PD difference per 10-year increase in age was -0.24 cm (95% CI: -0.34, -0.14; I.sup.2 = 30%), reflecting a compositional change (lower dense area and higher non-dense area, with no difference in breast area). In postmenopausal women, the corresponding difference in [square root]PD (-0.38 cm [95% CI: -0.44, -0.33]; I.sup.2 = 30%) was additionally driven by increasing breast area. The study is limited by different mammography systems and its cross-sectional rather than longitudinal nature. Conclusions Declines in MD with increasing age are present premenopausally, continue postmenopausally, and are most pronounced over the menopausal transition. These effects were highly consistent across diverse groups of women worldwide, suggesting that they result from an intrinsic biological, likely hormonal, mechanism common to women. If cumulative breast density is a key determinant of breast cancer risk, younger ages may be the more critical periods for lifestyle modifications aimed at breast density and breast cancer risk reduction.

Published
2017
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49. Annual Mammographic Screening Reduces the Risk of Interval or Higher Stage Invasive Breast Cancers Among Postmenopausal Women in the Ontario Breast Screening Program.

Author

Blackmore, Kristina M, Chiarelli, Anna M, Mirea, Lucia, Mittmann, Nicole, Muradali, Derek, Rabeneck, Linda, and Done, Susan J

Subjects
*BREAST cancer prognosis, *BREAST tumor diagnosis, *BREAST tumor risk factors, *OVARIAN tumors, *CONFIDENCE intervals, *DIGITAL technology, *AGE distribution, *MAMMOGRAMS, *EARLY detection of cancer, *RETROSPECTIVE studies, *ESTROGEN, *TUMOR classification, *HUMAN services programs, *POSTMENOPAUSE, *DESCRIPTIVE statistics, *LOGISTIC regression analysis, *ODDS ratio, *DATA analysis software, *WOMEN'S health, *LONGITUDINAL method, *FAMILY history (Medicine), BREAST physiology
Abstract

Purpose: In the Ontario Breast Screening Program (OBSP) annual screening improved breast cancer detection for women 50–74 years with a family/personal history compared to biennial, while detection was equivalent for women screened annually for mammographic density ≥75%. This study compares the risk of interval or higher stage invasive cancers among postmenopausal women screened annually vs biennially by age and estrogen use. Methods: A retrospective design identified 4247 invasive breast cancers diagnosed among concurrent cohorts of women 50–74 screened in the OBSP with digital mammography between 2011 and 2014, followed until 2016. Polytomous logistic regression estimated the risk of interval or higher stage breast cancers by age and estrogen use between women screened annually because of first-degree relative with breast or ovarian cancer or personal history of ovarian cancer, or mammographic density ≥75%, and those screened biennially. Results: The risk of interval vs screen-detected cancers was significantly reduced in women screened annually for family/personal history (OR=.64; 95%CI:0.51–.80), particularly those 60–74 years (OR=.59; 95%CI:0.45–.77) or not currently using estrogen (OR=.66; 95%CI:0.52–.83) compared to those screened biennially. The risk of stage II–IV vs stage I tumors was also lower in women 60–74 years screened annually for family/personal history (OR=.79; 95%CI:0.64–.97) and in those screened annually for mammographic density ≥75% currently using estrogen (OR=.51; 95%CI:0.26–1.01) compared to women screened biennially. Conclusion: Postmenopausal women at increased risk screened annually had equivalent or reduced risks of interval or higher stage invasive breast cancers than those screened biennially, further supporting risk-based screening in this population. [ABSTRACT FROM AUTHOR]

Published
2022
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