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Stout NK, Miglioretti DL, Su YR, Lee CI, Abraham L, Alagoz O, de Koning HJ, Hampton JM, Henderson L, Lowry KP, Mandelblatt JS, Onega T, Schechter CB, Sprague BL, Stein S, Trentham-Dietz A, van Ravesteyn NT, Wernli KJ, Kerlikowske K, Tosteson ANA. Breast Cancer Screening Using Mammography, Digital Breast Tomosynthesis, and Magnetic Resonance Imaging by Breast Density. JAMA Intern Med 2024:2822381. [PMID: 39186304 PMCID: PMC11348087 DOI: 10.1001/jamainternmed.2024.4224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 07/01/2024] [Indexed: 08/27/2024]
Abstract
Importance Information on long-term benefits and harms of screening with digital breast tomosynthesis (DBT) with or without supplemental breast magnetic resonance imaging (MRI) is needed for clinical and policy discussions, particularly for patients with dense breasts. Objective To project long-term population-based outcomes for breast cancer mammography screening strategies (DBT or digital mammography) with or without supplemental MRI by breast density. Design, Setting, and Participants Collaborative modeling using 3 Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer simulation models informed by US Breast Cancer Surveillance Consortium data. Simulated women born in 1980 with average breast cancer risk were included. Modeling analyses were conducted from January 2020 to December 2023. Intervention Annual or biennial mammography screening with or without supplemental MRI by breast density starting at ages 40, 45, or 50 years through age 74 years. Main outcomes and Measures Lifetime breast cancer deaths averted, false-positive recall and false-positive biopsy recommendations per 1000 simulated women followed-up from age 40 years to death summarized as means and ranges across models. Results Biennial DBT screening for all simulated women started at age 50 vs 40 years averted 7.4 vs 8.5 breast cancer deaths, respectively, and led to 884 vs 1392 false-positive recalls and 151 vs 221 false-positive biopsy recommendations, respectively. Biennial digital mammography had similar deaths averted and slightly more false-positive test results than DBT screening. Adding MRI for women with extremely dense breasts to biennial DBT screening for women aged 50 to 74 years increased deaths averted (7.6 vs 7.4), false-positive recalls (919 vs 884), and false-positive biopsy recommendations (180 vs 151). Extending supplemental MRI to women with heterogeneously or extremely dense breasts further increased deaths averted (8.0 vs 7.4), false-positive recalls (1088 vs 884), and false-positive biopsy recommendations (343 vs 151). The same strategy for women aged 40 to 74 years averted 9.5 deaths but led to 1850 false-positive recalls and 628 false-positive biopsy recommendations. Annual screening modestly increased estimated deaths averted but markedly increased estimated false-positive results. Conclusions and relevance In this model-based comparative effectiveness analysis, supplemental MRI for women with dense breasts added to DBT screening led to greater benefits and increased harms. The balance of this trade-off for supplemental MRI use was more favorable when MRI was targeted to women with extremely dense breasts who comprise approximately 10% of the population.
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Affiliation(s)
- Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Diana L. Miglioretti
- Department of Public Health Sciences, University of California Davis School of Medicine, Davis
| | - Yu-Ru Su
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Christoph I. Lee
- Fred Hutchinson Cancer Center, University of Washington School of Medicine, Seattle
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering and Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison, Madison
| | - Harry J. de Koning
- Department of Public Health, Erasmus University Medical Center Rotterdam, the Netherlands
| | - John M. Hampton
- Department of Industrial and Systems Engineering and Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison, Madison
| | - Louise Henderson
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill
| | - Kathryn P. Lowry
- Fred Hutchinson Cancer Center University of Washington School of Medicine, Seattle
| | - Jeanne S. Mandelblatt
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Department of Oncology and Georgetown Lombardi Institute for Cancer and Aging REsearch (I-CARE), Georgetown University, Washington, DC
| | - Tracy Onega
- Department of Population Health Sciences, and the Huntsman Cancer Institute, University of Utah, Salt Lake City
| | - Clyde B. Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York
| | - Brian L. Sprague
- Department of Surgery, University of Vermont Cancer Center, Burlington, Vermont
- University of Vermont Larner College of Medicine, Burlington
- Department of Radiology, University of Vermont Cancer Center, Burlington, Vermont
| | - Sarah Stein
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison, Madison
| | | | - Karen J. Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, San Francisco
| | - Anna N. A. Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice, Departments of Medicine and of Community and Family Medicine, and Dartmouth Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
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Basmadjian RB, Ruan Y, Hutchinson JM, Warkentin MT, Alagoz O, Coldman A, Brenner DR. Examining breast cancer screening recommendations in Canada: The projected resource impact of screening among women aged 40-49. J Med Screen 2024:9691413241267845. [PMID: 39106352 DOI: 10.1177/09691413241267845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/09/2024]
Abstract
OBJECTIVE To quantify the resource use of revising breast cancer screening guidelines to include average-risk women aged 40-49 years across Canada from 2024 to 2043 using a validated microsimulation model. SETTING OncoSim-Breast microsimulation platform was used to simulate the entire Canadian population in 2015-2051. METHODS We compared resource use between current screening guidelines (biennial screening ages 50-74) and alternate screening scenarios, which included annual and biennial screening for ages 40-49 and ages 45-49, followed by biennial screening ages 50-74. We estimated absolute and relative differences in number of screens, abnormal screening recalls without cancer, total and negative biopsies, screen-detected cancers, stage of diagnosis, and breast cancer deaths averted. RESULTS Compared with current guidelines in Canada, the most intensive screening scenario (annual screening ages 40-49) would result in 13.3% increases in the number of screens and abnormal screening recalls without cancer whereas the least intensive scenario (biennial screening ages 45-49) would result in a 3.4% increase in number of screens and 3.8% increase in number of abnormal screening recalls without cancer. More intensive screening would be associated with fewer stage II, III, and IV diagnoses, and more breast cancer deaths averted. CONCLUSIONS Revising breast cancer screening in Canada to include average-risk women aged 40-49 would detect cancers earlier leading to fewer breast cancer deaths. To realize this potential clinical benefit, a considerable increase in screening resources would be required in terms of number of screens and screen follow-ups. Further economic analyses are required to fully understand cost and budget implications.
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Affiliation(s)
- Robert B Basmadjian
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Yibing Ruan
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - John M Hutchinson
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Matthew T Warkentin
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew Coldman
- British Columbia Cancer Control Research, Vancouver, British Columbia, Canada
| | - Darren R Brenner
- Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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3
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Hendrick RE, Monticciolo DL. USPSTF Recommendations and Overdiagnosis. JOURNAL OF BREAST IMAGING 2024:wbae028. [PMID: 38865364 DOI: 10.1093/jbi/wbae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Indexed: 06/14/2024]
Abstract
Overdiagnosis is the concept that some cancers detected at screening would never have become clinically apparent during a woman's lifetime in the absence of screening. This could occur if a woman dies of a cause other than breast cancer in the interval between mammographic detection and clinical detection (obligate overdiagnosis) or if a mammographically detected breast cancer fails to progress to clinical presentation. Overdiagnosis cannot be measured directly. Indirect methods of estimating overdiagnosis include use of data from randomized controlled trials (RCTs) designed to evaluate breast cancer mortality, population-based screening studies, or modeling. In each case, estimates of overdiagnosis must consider lead time, breast cancer incidence trends in the absence of screening, and accurate and predictable rates of tumor progression. Failure to do so has led to widely varying estimates of overdiagnosis. The U.S. Preventive Services Task Force (USPSTF) considers overdiagnosis a major harm of mammography screening. Their 2024 report estimated overdiagnosis using summary evaluations of 3 RCTs that did not provide screening to their control groups at the end of the screening period, along with Cancer Intervention and Surveillance Network modeling. However, there are major flaws in their evidence sources and modeling estimates, limiting the USPSTF assessment. The most plausible estimates remain those based on observational studies that suggest overdiagnosis in breast cancer screening is 10% or less and can be attributed primarily to obligate overdiagnosis and nonprogressive ductal carcinoma in situ.
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Affiliation(s)
- R Edward Hendrick
- Department of Radiology, University of Colorado Anschutz School of Medicine, Aurora, CO, USA
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4
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Trentham-Dietz A, Chapman CH, Jayasekera J, Lowry KP, Heckman-Stoddard BM, Hampton JM, Caswell-Jin JL, Gangnon RE, Lu Y, Huang H, Stein S, Sun L, Gil Quessep EJ, Yang Y, Lu Y, Song J, Muñoz DF, Li Y, Kurian AW, Kerlikowske K, O'Meara ES, Sprague BL, Tosteson ANA, Feuer EJ, Berry D, Plevritis SK, Huang X, de Koning HJ, van Ravesteyn NT, Lee SJ, Alagoz O, Schechter CB, Stout NK, Miglioretti DL, Mandelblatt JS. Collaborative Modeling to Compare Different Breast Cancer Screening Strategies: A Decision Analysis for the US Preventive Services Task Force. JAMA 2024; 331:1947-1960. [PMID: 38687505 DOI: 10.1001/jama.2023.24766] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Importance The effects of breast cancer incidence changes and advances in screening and treatment on outcomes of different screening strategies are not well known. Objective To estimate outcomes of various mammography screening strategies. Design, Setting, and Population Comparison of outcomes using 6 Cancer Intervention and Surveillance Modeling Network (CISNET) models and national data on breast cancer incidence, mammography performance, treatment effects, and other-cause mortality in US women without previous cancer diagnoses. Exposures Thirty-six screening strategies with varying start ages (40, 45, 50 years) and stop ages (74, 79 years) with digital mammography or digital breast tomosynthesis (DBT) annually, biennially, or a combination of intervals. Strategies were evaluated for all women and for Black women, assuming 100% screening adherence and "real-world" treatment. Main Outcomes and Measures Estimated lifetime benefits (breast cancer deaths averted, percent reduction in breast cancer mortality, life-years gained), harms (false-positive recalls, benign biopsies, overdiagnosis), and number of mammograms per 1000 women. Results Biennial screening with DBT starting at age 40, 45, or 50 years until age 74 years averted a median of 8.2, 7.5, or 6.7 breast cancer deaths per 1000 women screened, respectively, vs no screening. Biennial DBT screening at age 40 to 74 years (vs no screening) was associated with a 30.0% breast cancer mortality reduction, 1376 false-positive recalls, and 14 overdiagnosed cases per 1000 women screened. Digital mammography screening benefits were similar to those for DBT but had more false-positive recalls. Annual screening increased benefits but resulted in more false-positive recalls and overdiagnosed cases. Benefit-to-harm ratios of continuing screening until age 79 years were similar or superior to stopping at age 74. In all strategies, women with higher-than-average breast cancer risk, higher breast density, and lower comorbidity level experienced greater screening benefits than other groups. Annual screening of Black women from age 40 to 49 years with biennial screening thereafter reduced breast cancer mortality disparities while maintaining similar benefit-to-harm trade-offs as for all women. Conclusions This modeling analysis suggests that biennial mammography screening starting at age 40 years reduces breast cancer mortality and increases life-years gained per mammogram. More intensive screening for women with greater risk of breast cancer diagnosis or death can maintain similar benefit-to-harm trade-offs and reduce mortality disparities.
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Affiliation(s)
- Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Christina Hunter Chapman
- Department of Radiation Oncology and Center for Innovations in Quality, Safety, and Effectiveness, Baylor College of Medicine, Houston, Texas
| | - Jinani Jayasekera
- Health Equity and Decision Sciences (HEADS) Research Laboratory, Division of Intramural Research at the National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | | | - Brandy M Heckman-Stoddard
- Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - John M Hampton
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison
| | | | - Ronald E Gangnon
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison
| | - Ying Lu
- Stanford University, Stanford, California
| | - Hui Huang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Sarah Stein
- Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Liyang Sun
- Stanford University, Stanford, California
| | | | | | - Yifan Lu
- Department of Industrial and Systems Engineering and Carbone Cancer Center, University of Wisconsin-Madison
| | - Juhee Song
- University of Texas MD Anderson Cancer Center, Houston
| | | | - Yisheng Li
- University of Texas MD Anderson Cancer Center, Houston
| | - Allison W Kurian
- Departments of Medicine and Epidemiology and Population Health, Stanford University, Stanford, California
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco
| | - Ellen S O'Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | | | - Anna N A Tosteson
- Dartmouth Institute for Health Policy and Clinical Practice and Departments of Medicine and Community and Family Medicine, Dartmouth Geisel School of Medicine, Hanover, New Hampshire
| | - Eric J Feuer
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Donald Berry
- University of Texas MD Anderson Cancer Center, Houston
| | - Sylvia K Plevritis
- Departments of Biomedical Data Science and Radiology, Stanford University, Stanford, California
| | - Xuelin Huang
- University of Texas MD Anderson Cancer Center, Houston
| | | | | | - Sandra J Lee
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering and Carbone Cancer Center, University of Wisconsin-Madison
| | | | - Natasha K Stout
- Harvard Pilgrim Health Care Institute, Boston, Massachusetts
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Diana L Miglioretti
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington
- Department of Public Health Sciences, University of California Davis
| | - Jeanne S Mandelblatt
- Departments of Oncology and Medicine, Georgetown University Medical Center, and Georgetown Lombardi Comprehensive Institute for Cancer and Aging Research at Georgetown University Lombardi Comprehensive Cancer Center, Washington, DC
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5
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Alagoz O, Zhang Y, Arroyo N, Fernandes-Taylor S, Yang DY, Krebsbach C, Venkatesh M, Hsiao V, Davies L, Francis DO. Modeling Thyroid Cancer Epidemiology in the United States Using Papillary Thyroid Carcinoma Microsimulation Model. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2024; 27:367-375. [PMID: 38141816 PMCID: PMC10922958 DOI: 10.1016/j.jval.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 12/05/2023] [Accepted: 12/06/2023] [Indexed: 12/25/2023]
Abstract
OBJECTIVES Thyroid cancer incidence increased over 200% from 1992 to 2018, whereas mortality rates had not increased proportionately. The increased incidence has been attributed primarily to the detection of subclinical disease, raising important questions related to thyroid cancer control. We developed the Papillary Thyroid Carcinoma Microsimulation model (PATCAM) to answer them, including the impact of overdiagnosis on thyroid cancer incidence. METHODS PATCAM simulates individuals from age 15 until death in birth cohorts starting from 1975 using 4 inter-related components, including natural history, detection, post-diagnosis, and other-cause mortality. PATCAM was built using high-quality data and calibrated against observed age-, sex-, and stage-specific incidence in the United States as reported by the Surveillance, Epidemiology, and End Results database. PATCAM was validated against US thyroid cancer mortality and 3 active surveillance studies, including the largest and longest running thyroid cancer active surveillance cohort in the world (from Japan) and 2 from the United States. RESULTS PATCAM successfully replicated age- and stage-specific papillary thyroid cancers (PTC) incidence and mean tumor size at diagnosis and PTC mortality in the United States between 1975 and 2015. PATCAM accurately predicted the proportion of tumors that grew more than 3 mm and 5 mm in 5 years and 10 years, aligning with the 95% confidence intervals of the reported rates from active surveillance studies in most cases. CONCLUSIONS PATCAM successfully reproduced observed US thyroid cancer incidence and mortality over time and was externally validated. PATCAM can be used to identify factors that influence the detection of subclinical PTCs.
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Affiliation(s)
- Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
| | - Yichi Zhang
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Natalia Arroyo
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Dou-Yan Yang
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | - Craig Krebsbach
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | - Manasa Venkatesh
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | - Vivian Hsiao
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
| | - Louise Davies
- Geisel School of Medicine at Dartmouth and The Dartmouth Institute for Health Policy & Clinical Practice, Hanover, NH, USA; Department of Veterans Affairs Medical Center, White River Junction, VT, USA
| | - David O Francis
- Department of Surgery, University of Wisconsin-Madison, Madison, WI, USA
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Jayasekera J, Stein S, Wilson OWA, Wojcik KM, Kamil D, Røssell EL, Abraham LA, O'Meara ES, Schoenborn NL, Schechter CB, Mandelblatt JS, Schonberg MA, Stout NK. Benefits and Harms of Mammography Screening in 75 + Women to Inform Shared Decision-making: a Simulation Modeling Study. J Gen Intern Med 2024; 39:428-439. [PMID: 38010458 PMCID: PMC10897118 DOI: 10.1007/s11606-023-08518-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 10/27/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Guidelines recommend shared decision-making (SDM) around mammography screening for women ≥ 75 years old. OBJECTIVE To use microsimulation modeling to estimate the lifetime benefits and harms of screening women aged 75, 80, and 85 years based on their individual risk factors (family history, breast density, prior biopsy) and comorbidity level to support SDM in clinical practice. DESIGN, SETTING, AND PARTICIPANTS We adapted two established Cancer Intervention and Surveillance Modeling Network (CISNET) models to evaluate the remaining lifetime benefits and harms of screening U.S. women born in 1940, at decision ages 75, 80, and 85 years considering their individual risk factors and comorbidity levels. Results were summarized for average- and higher-risk women (defined as having breast cancer family history, heterogeneously dense breasts, and no prior biopsy, 5% of the population). MAIN OUTCOMES AND MEASURES Remaining lifetime breast cancers detected, deaths (breast cancer/other causes), false positives, and overdiagnoses for average- and higher-risk women by age and comorbidity level for screening (one or five screens) vs. no screening per 1000 women. RESULTS Compared to stopping, one additional screen at 75 years old resulted in six and eight more breast cancers detected (10% overdiagnoses), one and two fewer breast cancer deaths, and 52 and 59 false positives per 1000 average- and higher-risk women without comorbidities, respectively. Five additional screens over 10 years led to 23 and 31 additional breast cancer cases (29-31% overdiagnoses), four and 15 breast cancer deaths avoided, and 238 and 268 false positives per 1000 average- and higher-risk screened women without comorbidities, respectively. Screening women at older ages (80 and 85 years old) and high comorbidity levels led to fewer breast cancer deaths and a higher percentage of overdiagnoses. CONCLUSIONS Simulation models show that continuing screening in women ≥ 75 years old results in fewer breast cancer deaths but more false positive tests and overdiagnoses. Together, clinicians and 75 + women may use model output to weigh the benefits and harms of continued screening.
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Affiliation(s)
- Jinani Jayasekera
- Health Equity and Decision Sciences Research Laboratory, National Institute on Minority Health and Health Disparities (NIMHD) Intramural Research Program (IRP), National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Sarah Stein
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Oliver W A Wilson
- Health Equity and Decision Sciences Research Laboratory, National Institute on Minority Health and Health Disparities (NIMHD) Intramural Research Program (IRP), National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kaitlyn M Wojcik
- Health Equity and Decision Sciences Research Laboratory, National Institute on Minority Health and Health Disparities (NIMHD) Intramural Research Program (IRP), National Institutes of Health, Bethesda, MD, 20892, USA
| | - Dalya Kamil
- Health Equity and Decision Sciences Research Laboratory, National Institute on Minority Health and Health Disparities (NIMHD) Intramural Research Program (IRP), National Institutes of Health, Bethesda, MD, 20892, USA
| | | | - Linn A Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Ellen S O'Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Nancy Li Schoenborn
- Division of Geriatric Medicine and Gerontology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Clyde B Schechter
- Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jeanne S Mandelblatt
- Georgetown Lombardi Institute for Cancer and Aging Research and the Cancer Prevention and Control Program at the Georgetown Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Washington, DC, USA
| | - Mara A Schonberg
- Division of General Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
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7
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Caswell-Jin JL, Sun LP, Munoz D, Lu Y, Li Y, Huang H, Hampton JM, Song J, Jayasekera J, Schechter C, Alagoz O, Stout NK, Trentham-Dietz A, Lee SJ, Huang X, Mandelblatt JS, Berry DA, Kurian AW, Plevritis SK. Analysis of Breast Cancer Mortality in the US-1975 to 2019. JAMA 2024; 331:233-241. [PMID: 38227031 PMCID: PMC10792466 DOI: 10.1001/jama.2023.25881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 11/27/2023] [Indexed: 01/17/2024]
Abstract
Importance Breast cancer mortality in the US declined between 1975 and 2019. The association of changes in metastatic breast cancer treatment with improved breast cancer mortality is unclear. Objective To simulate the relative associations of breast cancer screening, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer with improved breast cancer mortality. Design, Setting, and Participants Using aggregated observational and clinical trial data on the dissemination and effects of screening and treatment, 4 Cancer Intervention and Surveillance Modeling Network (CISNET) models simulated US breast cancer mortality rates. Death due to breast cancer, overall and by estrogen receptor and ERBB2 (formerly HER2) status, among women aged 30 to 79 years in the US from 1975 to 2019 was simulated. Exposures Screening mammography, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer. Main Outcomes and Measures Model-estimated age-adjusted breast cancer mortality rate associated with screening, stage I to III treatment, and metastatic treatment relative to the absence of these exposures was assessed, as was model-estimated median survival after breast cancer metastatic recurrence. Results The breast cancer mortality rate in the US (age adjusted) was 48/100 000 women in 1975 and 27/100 000 women in 2019. In 2019, the combination of screening, stage I to III treatment, and metastatic treatment was associated with a 58% reduction (model range, 55%-61%) in breast cancer mortality. Of this reduction, 29% (model range, 19%-33%) was associated with treatment of metastatic breast cancer, 47% (model range, 35%-60%) with treatment of stage I to III breast cancer, and 25% (model range, 21%-33%) with mammography screening. Based on simulations, the greatest change in survival after metastatic recurrence occurred between 2000 and 2019, from 1.9 years (model range, 1.0-2.7 years) to 3.2 years (model range, 2.0-4.9 years). Median survival for estrogen receptor (ER)-positive/ERBB2-positive breast cancer improved by 2.5 years (model range, 2.0-3.4 years), whereas median survival for ER-/ERBB2- breast cancer improved by 0.5 years (model range, 0.3-0.8 years). Conclusions and Relevance According to 4 simulation models, breast cancer screening and treatment in 2019 were associated with a 58% reduction in US breast cancer mortality compared with interventions in 1975. Simulations suggested that treatment for stage I to III breast cancer was associated with approximately 47% of the mortality reduction, whereas treatment for metastatic breast cancer was associated with 29% of the reduction and screening with 25% of the reduction.
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Affiliation(s)
| | - Liyang P. Sun
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Diego Munoz
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Ying Lu
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Yisheng Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston
| | | | - John M. Hampton
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison School of Medicine and Public Health, Madison
| | - Juhee Song
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston
| | - Jinani Jayasekera
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, Maryland
| | - Clyde Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School, Boston, Massachusetts
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison School of Medicine and Public Health, Madison
| | - Sandra J. Lee
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Data Sciences, Harvard Medical School, Boston, Massachusetts
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston
| | - Jeanne S. Mandelblatt
- Department of Oncology, Georgetown University Medical Center, Georgetown Lombardi Comprehensive Cancer Center, Washington, DC
- Georgetown-Lombardi Institute for Cancer and Aging, Washington, DC
| | - Donald A. Berry
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston
| | - Allison W. Kurian
- Department of Medicine, Stanford University School of Medicine, Stanford, California
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
| | - Sylvia K. Plevritis
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
- Department of Radiology, Stanford University School of Medicine, Stanford, California
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Mandelblatt JS, Schechter CB, Stout NK, Huang H, Stein S, Hunter Chapman C, Trentham-Dietz A, Jayasekera J, Gangnon RE, Hampton JM, Abraham L, O’Meara ES, Sheppard VB, Lee SJ. Population simulation modeling of disparities in US breast cancer mortality. J Natl Cancer Inst Monogr 2023; 2023:178-187. [PMID: 37947337 PMCID: PMC10637022 DOI: 10.1093/jncimonographs/lgad023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/13/2023] [Accepted: 07/31/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Populations of African American or Black women have persistently higher breast cancer mortality than the overall US population, despite having slightly lower age-adjusted incidence. METHODS Three Cancer Intervention and Surveillance Modeling Network simulation teams modeled cancer mortality disparities between Black female populations and the overall US population. Model inputs used racial group-specific data from clinical trials, national registries, nationally representative surveys, and observational studies. Analyses began with cancer mortality in the overall population and sequentially replaced parameters for Black populations to quantify the percentage of modeled breast cancer morality disparities attributable to differences in demographics, incidence, access to screening and treatment, and variation in tumor biology and response to therapy. RESULTS Results were similar across the 3 models. In 2019, racial differences in incidence and competing mortality accounted for a net ‒1% of mortality disparities, while tumor subtype and stage distributions accounted for a mean of 20% (range across models = 13%-24%), and screening accounted for a mean of 3% (range = 3%-4%) of the modeled mortality disparities. Treatment parameters accounted for the majority of modeled mortality disparities: mean = 17% (range = 16%-19%) for treatment initiation and mean = 61% (range = 57%-63%) for real-world effectiveness. CONCLUSION Our model results suggest that changes in policies that target improvements in treatment access could increase breast cancer equity. The findings also highlight that efforts must extend beyond policies targeting equity in treatment initiation to include high-quality treatment completion. This research will facilitate future modeling to test the effects of different specific policy changes on mortality disparities.
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Affiliation(s)
- Jeanne S Mandelblatt
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program at Georgetown Lombardi Comprehensive Cancer Center, Washington, DC, USA
| | - Clyde B Schechter
- Departments of Family and Social Medicine and of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Natasha K Stout
- Department of Population Sciences, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Hui Huang
- Department of Data Science, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Sarah Stein
- Department of Population Sciences, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Christina Hunter Chapman
- Department of Radiation Oncology, Section of Health Services Research, Baylor College of Medicine and Health Policy, Quality and Informatics Program at the Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Jinani Jayasekera
- Health Equity and Decision Sciences Research Lab, National Institute on Minority Health and Health Disparities, Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Ronald E Gangnon
- Departments of Population Health Sciences and of Biostatistics and Medical Informatics and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - John M Hampton
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Linn Abraham
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Ellen S O’Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Vanessa B Sheppard
- Department of Health Behavior and Policy and Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA
| | - Sandra J Lee
- Department of Data Science, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
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Poelhekken K, Lin Y, Greuter MJW, van der Vegt B, Dorrius M, de Bock GH. The natural history of ductal carcinoma in situ (DCIS) in simulation models: A systematic review. Breast 2023; 71:74-81. [PMID: 37541171 PMCID: PMC10412870 DOI: 10.1016/j.breast.2023.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 08/06/2023] Open
Abstract
OBJECTIVE Assumptions on the natural history of ductal carcinoma in situ (DCIS) are necessary to accurately model it and estimate overdiagnosis. To improve current estimates of overdiagnosis (0-91%), the purpose of this review was to identify and analyse assumptions made in modelling studies on the natural history of DCIS in women. METHODS A systematic review of English full-text articles using PubMed, Embase, and Web of Science was conducted up to February 6, 2023. Eligibility and all assessments were done independently by two reviewers. Risk of bias and quality assessments were performed. Discrepancies were resolved by consensus. Reader agreement was quantified with Cohen's kappa. Data extraction was performed with three forms on study characteristics, model assessment, and tumour progression. RESULTS Thirty models were distinguished. The most important assumptions regarding the natural history of DCIS were addition of non-progressive DCIS of 20-100%, classification of DCIS into three grades, where high grade DCIS had an increased chance of progression to invasive breast cancer (IBC), and regression possibilities of 1-4%, depending on age and grade. Other identified risk factors of progression of DCIS to IBC were younger age, birth cohort, larger tumour size, and individual risk. CONCLUSION To accurately model the natural history of DCIS, aspects to consider are DCIS grades, non-progressive DCIS (9-80%), regression from DCIS to no cancer (below 10%), and use of well-established risk factors for progression probabilities (age). Improved knowledge on key factors to consider when studying DCIS can improve estimates of overdiagnosis and optimization of screening.
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Affiliation(s)
- Keris Poelhekken
- University of Groningen, University Medical Center Groningen, Groningen, Department of Epidemiology, P.O. Box 30 001, FA40, 9700, RB, Groningen, the Netherlands; University of Groningen, University Medical Center Groningen, Groningen, Department of Radiology, PO Box 30.001, EB44, 9700, RB, Groningen, the Netherlands.
| | - Yixuan Lin
- University of Groningen, University Medical Center Groningen, Groningen, Department of Epidemiology, P.O. Box 30 001, FA40, 9700, RB, Groningen, the Netherlands
| | - Marcel J W Greuter
- University of Groningen, University Medical Center Groningen, Groningen, Department of Radiology, PO Box 30.001, EB44, 9700, RB, Groningen, the Netherlands
| | - Bert van der Vegt
- University of Groningen, University Medical Center Groningen, Groningen, Department of Pathology and Medical Biology, PO Box 30.001, 9700, RB, Groningen, the Netherlands
| | - Monique Dorrius
- University of Groningen, University Medical Center Groningen, Groningen, Department of Radiology, PO Box 30.001, EB44, 9700, RB, Groningen, the Netherlands
| | - Geertruida H de Bock
- University of Groningen, University Medical Center Groningen, Groningen, Department of Epidemiology, P.O. Box 30 001, FA40, 9700, RB, Groningen, the Netherlands
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Tunç S, Alagoz O, Burnside ES. A new perspective on breast cancer diagnostic guidelines to reduce overdiagnosis. PRODUCTION AND OPERATIONS MANAGEMENT 2022; 31:2361-2378. [PMID: 35915601 PMCID: PMC9313854 DOI: 10.1111/poms.13691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 01/19/2022] [Indexed: 06/15/2023]
Abstract
Overdiagnosis of breast cancer, defined as diagnosing a cancer that would otherwise not cause symptoms or death in a patient's lifetime, costs U.S. health care system over $1.2 billion annually. Overdiagnosis rates, estimated to be around 10%-40%, may be reduced if indolent breast findings can be identified and followed with noninvasive imaging rather than biopsy. However, there are no validated guidelines for radiologists to decide when to choose imaging options recognizing cancer grades and types. The aim of this study is to optimize breast cancer diagnostic decisions based on cancer types using a large-scale finite-horizon Markov decision process (MDP) model with 4.6 million states to help reduce overdiagnosis. We prove the optimality of a divide-and-search algorithm that relies on tight upper bounds on the optimal decision thresholds to find an exact optimal solution. We project the high-dimensional MDP onto two lower dimensional MDPs and obtain feasible upper bounds on the optimal decision thresholds. We use real data from two private mammography databases and demonstrate our model performance through a previously validated simulation model that has been used by the policy makers to set the national screening guidelines in the United States. We find that a decision-analytical framework optimizing diagnostic decisions while accounting for breast cancer types has a strong potential to improve the quality of life and alleviate the immense costs of overdiagnosis. Our model leads to a20 % reduction in overdiagnosis on the screening population, which translates into an annual savings of approximately $300 million for the U.S. health care system.
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Affiliation(s)
- Sait Tunç
- Grado Department of Industrial and Systems EngineeringVirginia TechBlacksburgVirginiaUSA
| | - Oguzhan Alagoz
- Department of Industrial and Systems EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsinUSA
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11
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Lowry KP, Geuzinge HA, Stout NK, Alagoz O, Hampton J, Kerlikowske K, de Koning HJ, Miglioretti DL, van Ravesteyn NT, Schechter C, Sprague BL, Tosteson ANA, Trentham-Dietz A, Weaver D, Yaffe MJ, Yeh JM, Couch FJ, Hu C, Kraft P, Polley EC, Mandelblatt JS, Kurian AW, Robson ME. Breast Cancer Screening Strategies for Women With ATM, CHEK2, and PALB2 Pathogenic Variants: A Comparative Modeling Analysis. JAMA Oncol 2022; 8:587-596. [PMID: 35175286 PMCID: PMC8855312 DOI: 10.1001/jamaoncol.2021.6204] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 08/25/2021] [Indexed: 12/14/2022]
Abstract
IMPORTANCE Screening mammography and magnetic resonance imaging (MRI) are recommended for women with ATM, CHEK2, and PALB2 pathogenic variants. However, there are few data to guide screening regimens for these women. OBJECTIVE To estimate the benefits and harms of breast cancer screening strategies using mammography and MRI at various start ages for women with ATM, CHEK2, and PALB2 pathogenic variants. DESIGN, SETTING, AND PARTICIPANTS This comparative modeling analysis used 2 established breast cancer microsimulation models from the Cancer Intervention and Surveillance Modeling Network (CISNET) to evaluate different screening strategies. Age-specific breast cancer risks were estimated using aggregated data from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium for 32 247 cases and 32 544 controls in 12 population-based studies. Data on screening performance for mammography and MRI were estimated from published literature. The models simulated US women with ATM, CHEK2, or PALB2 pathogenic variants born in 1985. INTERVENTIONS Screening strategies with combinations of annual mammography alone and with MRI starting at age 25, 30, 35, or 40 years until age 74 years. MAIN OUTCOMES AND MEASURES Estimated lifetime breast cancer mortality reduction, life-years gained, breast cancer deaths averted, total screening examinations, false-positive screenings, and benign biopsies per 1000 women screened. Results are reported as model mean values and ranges. RESULTS The mean model-estimated lifetime breast cancer risk was 20.9% (18.1%-23.7%) for women with ATM pathogenic variants, 27.6% (23.4%-31.7%) for women with CHEK2 pathogenic variants, and 39.5% (35.6%-43.3%) for women with PALB2 pathogenic variants. Across pathogenic variants, annual mammography alone from 40 to 74 years was estimated to reduce breast cancer mortality by 36.4% (34.6%-38.2%) to 38.5% (37.8%-39.2%) compared with no screening. Screening with annual MRI starting at 35 years followed by annual mammography and MRI at 40 years was estimated to reduce breast cancer mortality by 54.4% (54.2%-54.7%) to 57.6% (57.2%-58.0%), with 4661 (4635-4688) to 5001 (4979-5023) false-positive screenings and 1280 (1272-1287) to 1368 (1362-1374) benign biopsies per 1000 women. Annual MRI starting at 30 years followed by mammography and MRI at 40 years was estimated to reduce mortality by 55.4% (55.3%-55.4%) to 59.5% (58.5%-60.4%), with 5075 (5057-5093) to 5415 (5393-5437) false-positive screenings and 1439 (1429-1449) to 1528 (1517-1538) benign biopsies per 1000 women. When starting MRI at 30 years, initiating annual mammography starting at 30 vs 40 years did not meaningfully reduce mean mortality rates (0.1% [0.1%-0.2%] to 0.3% [0.2%-0.3%]) but was estimated to add 649 (602-695) to 650 (603-696) false-positive screenings and 58 (41-76) to 59 (41-76) benign biopsies per 1000 women. CONCLUSIONS AND RELEVANCE This analysis suggests that annual MRI screening starting at 30 to 35 years followed by annual MRI and mammography at 40 years may reduce breast cancer mortality by more than 50% for women with ATM, CHEK2, and PALB2 pathogenic variants. In the setting of MRI screening, mammography prior to 40 years may offer little additional benefit.
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Affiliation(s)
- Kathryn P. Lowry
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle
| | - H. Amarens Geuzinge
- Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Healthcare Institute, Boston, Massachusetts
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison
| | - John Hampton
- Carbone Cancer Center, Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin–Madison, Madison
| | - Karla Kerlikowske
- Department of Medicine, University of California, San Francisco
- Department of Epidemiology and Biostatistics, University of California, San Francisco
| | - Harry J. de Koning
- Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Diana L. Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis
| | | | - Clyde Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York
| | - Brian L. Sprague
- Department of Surgery, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington
- Department of Radiology, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington
| | - Anna N. A. Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Amy Trentham-Dietz
- Carbone Cancer Center, Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin–Madison, Madison
| | - Donald Weaver
- Department of Pathology, University of Vermont Larner College of Medicine, Burlington
| | - Martin J. Yaffe
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Jennifer M. Yeh
- Department of Pediatrics, Harvard Medical School, Boston Children’s Hospital, Boston, Massachusetts
| | - Fergus J. Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, New York
| | - Chunling Hu
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, New York
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Eric C. Polley
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota
| | - Jeanne S. Mandelblatt
- Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC
| | - Allison W. Kurian
- Department of Medicine, Stanford University, Stanford, California
- Department of Epidemiology and Population Health, Stanford University, Stanford, California
| | - Mark E. Robson
- Department of Breast Oncology, Memorial Sloan Kettering Cancer Center, New York, New York
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12
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Ryser MD, Lange J, Inoue LYT, O'Meara ES, Gard C, Miglioretti DL, Bulliard JL, Brouwer AF, Hwang ES, Etzioni RB. Estimation of Breast Cancer Overdiagnosis in a U.S. Breast Screening Cohort. Ann Intern Med 2022; 175:471-478. [PMID: 35226520 PMCID: PMC9359467 DOI: 10.7326/m21-3577] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Mammography screening can lead to overdiagnosis-that is, screen-detected breast cancer that would not have caused symptoms or signs in the remaining lifetime. There is no consensus about the frequency of breast cancer overdiagnosis. OBJECTIVE To estimate the rate of breast cancer overdiagnosis in contemporary mammography practice accounting for the detection of nonprogressive cancer. DESIGN Bayesian inference of the natural history of breast cancer using individual screening and diagnosis records, allowing for nonprogressive preclinical cancer. Combination of fitted natural history model with life-table data to predict the rate of overdiagnosis among screen-detected cancer under biennial screening. SETTING Breast Cancer Surveillance Consortium (BCSC) facilities. PARTICIPANTS Women aged 50 to 74 years at first mammography screen between 2000 and 2018. MEASUREMENTS Screening mammograms and screen-detected or interval breast cancer. RESULTS The cohort included 35 986 women, 82 677 mammograms, and 718 breast cancer diagnoses. Among all preclinical cancer cases, 4.5% (95% uncertainty interval [UI], 0.1% to 14.8%) were estimated to be nonprogressive. In a program of biennial screening from age 50 to 74 years, 15.4% (UI, 9.4% to 26.5%) of screen-detected cancer cases were estimated to be overdiagnosed, with 6.1% (UI, 0.2% to 20.1%) due to detecting indolent preclinical cancer and 9.3% (UI, 5.5% to 13.5%) due to detecting progressive preclinical cancer in women who would have died of an unrelated cause before clinical diagnosis. LIMITATIONS Exclusion of women with first mammography screen outside BCSC. CONCLUSION On the basis of an authoritative U.S. population data set, the analysis projected that among biennially screened women aged 50 to 74 years, about 1 in 7 cases of screen-detected cancer is overdiagnosed. This information clarifies the risk for breast cancer overdiagnosis in contemporary screening practice and should facilitate shared and informed decision making about mammography screening. PRIMARY FUNDING SOURCE National Cancer Institute.
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Affiliation(s)
- Marc D Ryser
- Department of Population Health Sciences, Duke University Medical Center, and Department of Mathematics, Duke University, Durham, North Carolina (M.D.R.)
| | - Jane Lange
- Center for Early Detection Advanced Research, Knight Cancer Institute, Oregon Health Sciences University, Portland, Oregon (J.L.)
| | - Lurdes Y T Inoue
- Department of Biostatistics, University of Washington, Seattle, Washington (L.Y.I.)
| | - Ellen S O'Meara
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington (E.S.O.)
| | - Charlotte Gard
- Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, New Mexico (C.G.)
| | - Diana L Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis, California, and Kaiser Permanente Washington Health Research Institute, Seattle, Washington (D.L.M.)
| | - Jean-Luc Bulliard
- Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland (J.B.)
| | - Andrew F Brouwer
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan (A.F.B.)
| | - E Shelley Hwang
- Department of Surgery, Duke University Medical Center, Durham, North Carolina (E.S.H.)
| | - Ruth B Etzioni
- Program in Biostatistics, Fred Hutchinson Cancer Research Center, Seattle, Washington (R.B.E.)
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13
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Benefits and harms of annual, biennial, or triennial breast cancer mammography screening for women at average risk of breast cancer: a systematic review for the European Commission Initiative on Breast Cancer (ECIBC). Br J Cancer 2022; 126:673-688. [PMID: 34837076 PMCID: PMC8854566 DOI: 10.1038/s41416-021-01521-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 06/20/2021] [Accepted: 07/30/2021] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Although mammography screening is recommended in most European countries, the balance between the benefits and harms of different screening intervals is still a matter of debate. This review informed the European Commission Initiative on Breast Cancer (BC) recommendations. METHODS We searched PubMed, EMBASE, and the Cochrane Library to identify RCTs, observational or modelling studies, comparing desirable (BC deaths averted, QALYs, BC stage, interval cancer) and undesirable (overdiagnosis, false positive related, radiation related) effects from annual, biennial, or triennial mammography screening in women of average risk for BC. We assessed the certainty of the evidence using the GRADE approach. RESULTS We included one RCT, 13 observational, and 11 modelling studies. In women 50-69, annual compared to biennial screening may have small additional benefits but an important increase in false positive results; triennial compared to biennial screening may have smaller benefits while avoiding some harms. In younger women (aged 45-49), annual compared to biennial screening had a smaller gain in benefits and larger harms, showing a less favourable balance in this age group than in women 50-69. In women 70-74, there were fewer additional harms and similar benefits with shorter screening intervals. The overall certainty of the evidence for each of these comparisons was very low. CONCLUSIONS In women of average BC risk, screening intervals have different trade-offs for each age group. The balance probably favours biennial screening in women 50-69. In younger women, annual screening may have a less favourable balance, while in women aged 70-74 years longer screening intervals may be more favourable.
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14
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Yong JHE, Nadeau C, Flanagan WM, Coldman AJ, Asakawa K, Garner R, Fitzgerald N, Yaffe MJ, Miller AB. The OncoSim-Breast Cancer Microsimulation Model. Curr Oncol 2022; 29:1619-1633. [PMID: 35323336 PMCID: PMC8947518 DOI: 10.3390/curroncol29030136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/22/2022] [Accepted: 02/28/2022] [Indexed: 01/02/2023] Open
Abstract
Background: OncoSim-Breast is a Canadian breast cancer simulation model to evaluate breast cancer interventions. This paper aims to describe the OncoSim-Breast model and how well it reproduces observed breast cancer trends. Methods: The OncoSim-Breast model simulates the onset, growth, and spread of invasive and ductal carcinoma in situ tumours. It combines Canadian cancer incidence, mortality, screening program, and cost data to project population-level outcomes. Users can change the model input to answer specific questions. Here, we compared its projections with observed data. First, we compared the model’s projected breast cancer trends with the observed data in the Canadian Cancer Registry and from Vital Statistics. Next, we replicated a screening trial to compare the model’s projections with the trial’s observed screening effects. Results: OncoSim-Breast’s projected incidence, mortality, and stage distribution of breast cancer were close to the observed data in the Canadian Cancer Registry and from Vital Statistics. OncoSim-Breast also reproduced the breast cancer screening effects observed in the UK Age trial. Conclusions: OncoSim-Breast’s ability to reproduce the observed population-level breast cancer trends and the screening effects in a randomized trial increases the confidence of using its results to inform policy decisions related to early detection of breast cancer.
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Affiliation(s)
- Jean H. E. Yong
- Canadian Partnership Against Cancer, Toronto, ON M5H 1J8, Canada;
- Correspondence:
| | - Claude Nadeau
- Statistics Canada, Ottawa, ON K1A 0T6, Canada; (C.N.); (W.M.F.); (K.A.); (R.G.)
| | - William M. Flanagan
- Statistics Canada, Ottawa, ON K1A 0T6, Canada; (C.N.); (W.M.F.); (K.A.); (R.G.)
| | - Andrew J. Coldman
- British Columbia Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada;
| | - Keiko Asakawa
- Statistics Canada, Ottawa, ON K1A 0T6, Canada; (C.N.); (W.M.F.); (K.A.); (R.G.)
| | - Rochelle Garner
- Statistics Canada, Ottawa, ON K1A 0T6, Canada; (C.N.); (W.M.F.); (K.A.); (R.G.)
| | | | | | - Anthony B. Miller
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada;
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15
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Hadid M, Elomri A, El Mekkawy T, Kerbache L, El Omri A, El Omri H, Taha RY, Hamad AA, Al Thani MHJ. Bibliometric analysis of cancer care operations management: current status, developments, and future directions. Health Care Manag Sci 2022; 25:166-185. [PMID: 34981268 DOI: 10.1007/s10729-021-09585-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Accepted: 10/05/2021] [Indexed: 01/31/2023]
Abstract
Around the world, cancer care services are facing many operational challenges. Operations management research can provide important solutions to these challenges, from screening and diagnosis to treatment. In recent years, the growth in the number of papers published on cancer care operations management (CCOM) indicates that development has been fast. Within this context, the objective of this research was to understand the evolution of CCOM through a comprehensive study and an up-to-date bibliometric analysis of the literature. To achieve this aim, the Web of Science Core Collection database was used as the source of bibliographic records. The data-mining and quantitative tools in the software Biblioshiny were used to analyze CCOM articles published from 2010 to 2021. First, a historical analysis described CCOM research, the sources, and the subfields. Second, an analysis of keywords highlighted the significant developments in this field. Third, an analysis of research themes identified three main directions for future research in CCOM, which has 11 evolutionary paths. Finally, this paper discussed the gaps in CCOM research and the areas that require further investigation and development.
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Affiliation(s)
- Majed Hadid
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Adel Elomri
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
| | | | - Laoucine Kerbache
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | | | - Halima El Omri
- National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Ruba Y Taha
- National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
| | - Anas Ahmad Hamad
- National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar
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16
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Alagoz O, Lowry KP, Kurian AW, Mandelblatt JS, Ergun MA, Huang H, Lee SJ, Schechter CB, Tosteson ANA, Miglioretti DL, Trentham-Dietz A, Nyante SJ, Kerlikowske K, Sprague BL, Stout NK. Impact of the COVID-19 Pandemic on Breast Cancer Mortality in the US: Estimates From Collaborative Simulation Modeling. J Natl Cancer Inst 2021; 113:1484-1494. [PMID: 34258611 PMCID: PMC8344930 DOI: 10.1093/jnci/djab097] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/21/2021] [Accepted: 04/26/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has disrupted breast cancer control through short-term declines in screening and delays in diagnosis and treatments. We projected the impact of COVID-19 on future breast cancer mortality between 2020 and 2030. METHODS Three established Cancer Intervention and Surveillance Modeling Network breast cancer models modeled reductions in mammography screening use, delays in symptomatic cancer diagnosis, and reduced use of chemotherapy for women with early-stage disease for the first 6 months of the pandemic with return to prepandemic patterns after that time. Sensitivity analyses were performed to determine the effect of key model parameters, including the duration of the pandemic impact. RESULTS By 2030, the models project 950 (model range = 860-1297) cumulative excess breast cancer deaths related to reduced screening, 1314 (model range = 266-1325) associated with delayed diagnosis of symptomatic cases, and 151 (model range = 146-207) associated with reduced chemotherapy use in women with hormone positive, early-stage cancer. Jointly, 2487 (model range = 1713-2575) excess breast cancer deaths were estimated, representing a 0.52% (model range = 0.36%-0.56%) cumulative increase over breast cancer deaths expected by 2030 in the absence of the pandemic's disruptions. Sensitivity analyses indicated that the breast cancer mortality impact would be approximately double if the modeled pandemic effects on screening, symptomatic diagnosis, and chemotherapy extended for 12 months. CONCLUSIONS Initial pandemic-related disruptions in breast cancer care will have a small long-term cumulative impact on breast cancer mortality. Continued efforts to ensure prompt return to screening and minimize delays in evaluation of symptomatic women can largely mitigate the effects of the initial pandemic-associated disruptions.
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Affiliation(s)
- Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Kathryn P Lowry
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, WA, USA
| | - Allison W Kurian
- Departments of Medicine and of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Jeanne S Mandelblatt
- Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC, USA
| | - Mehmet A Ergun
- Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey
| | - Hui Huang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sandra J Lee
- Department of Data Science, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Clyde B Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | - Diana L Miglioretti
- Department of Public Health Sciences, University of California at Davis, Davis, CA, USA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and the Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Sarah J Nyante
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology/Biostatistics, University of California at San Francisco, San Francisco, CA, USA
| | - Brian L Sprague
- Department of Surgery and the University of Vermont Cancer Center, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
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17
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Zwick ED, Pepperell CS, Alagoz O. Representing Tuberculosis Transmission with Complex Contagion: An Agent-Based Simulation Modeling Approach. Med Decis Making 2021; 41:641-652. [PMID: 33904344 PMCID: PMC8295181 DOI: 10.1177/0272989x211007842] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
OBJECTIVE A recent study reported a tuberculosis (TB) outbreak in which, among newly infected individuals, exposure to additional active infections was associated with a higher probability of developing active disease. Referred to as complex contagion, multiple reexposures to TB within a short period after initial infection is hypothesized to confer a greater likelihood of developing active infection in 1 y. The purpose of this article is to develop and validate an agent-based simulation model (ABM) to study the effect of complex contagion on population-level TB transmission dynamics. METHODS We built an ABM of a TB epidemic using data from a series of outbreaks recorded in the 20th century in Saskatchewan, Canada. We fit 3 dynamical schemes: base, with no complex contagion; additive, in which each reexposure confers an independent risk of activated infection; and threshold, in which a small number of reexposures confers a low risk and a high number of reexposures confers a high risk of activation. RESULTS We find that the base model fits the mortality and incidence output targets best, followed by the threshold and then the additive models. The threshold model fits the incidence better than the base model does but overestimates mortality. All 3 models produce qualitatively realistic epidemic curves. CONCLUSION We find that complex contagion qualitatively changes the trajectory of a TB epidemic, although data from a high-incidence setting are reproduced better with the base model. Results from this model demonstrate the feasibility of using ABM to capture nuances in TB transmission.
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Affiliation(s)
- Erin D Zwick
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Caitlin S Pepperell
- Department of Medicine and Department of Medical Microbiology and Immunology, University of Wisconsin-Madison, Madison, WI, USA
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA, PhD
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18
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Yeh JM, Lowry KP, Schechter CB, Diller LR, O'Brien G, Alagoz O, Armstrong GT, Hampton JM, Hudson MM, Leisenring W, Liu Q, Mandelblatt JS, Miglioretti DL, Moskowitz CS, Nathan PC, Neglia JP, Oeffinger KC, Trentham-Dietz A, Stout NK. Breast Cancer Screening Among Childhood Cancer Survivors Treated Without Chest Radiation: Clinical Benefits and Cost-Effectiveness. J Natl Cancer Inst 2021; 114:235-244. [PMID: 34324686 DOI: 10.1093/jnci/djab149] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/22/2021] [Accepted: 07/22/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Early initiation of breast cancer screening is recommended for high-risk women, including survivors of childhood cancer treated with chest radiation. Recent studies suggest that female survivors of childhood leukemia or sarcoma treated without chest radiation are also at elevated early onset breast cancer risk. However, the potential clinical benefits and cost-effectiveness of early breast cancer screening among these women are uncertain. METHODS Using data from the Childhood Cancer Survivor Study, we adapted two Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer simulation models to reflect the elevated risks of breast cancer and competing mortality among leukemia and sarcoma survivors. Costs and utility weights were based on published studies and databases. Outcomes included breast cancer deaths averted, false-positive-screening results, benign biopsies, and incremental cost-effectiveness ratios (ICERs). RESULTS In the absence of screening, the lifetime risk of dying from breast cancer among survivors was 6.8% to 7.0% across models. Early initiation of annual mammography with MRI screening between ages 25 and 40 would avert 52.6% to 64.3% of breast cancer deaths. When costs and quality of life impacts were considered, screening starting at age 40 was the only strategy with an ICER below the $100,000 per quality-adjusted life-year (QALY) gained cost-effectiveness threshold ($27,680 to $44,380 per QALY gained across models). CONCLUSIONS Among survivors of childhood leukemia or sarcoma, early initiation of breast cancer screening at age 40 may reduce breast cancer deaths by half and is cost-effective. These findings could help inform screening guidelines for survivors treated without chest radiation.
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Affiliation(s)
- Jennifer M Yeh
- Division of General Pediatrics, Boston Children's Hospital, Boston, MA.,Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Kathryn P Lowry
- University of Washington, Seattle Cancer Care Alliance, Seattle, WA
| | - Clyde B Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY
| | - Lisa R Diller
- Department of Pediatrics, Harvard Medical School, Boston, MA.,Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA
| | - Grace O'Brien
- Division of General Pediatrics, Boston Children's Hospital, Boston, MA
| | | | - Gregory T Armstrong
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN
| | - John M Hampton
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI
| | - Melissa M Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN.,Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN
| | | | - Qi Liu
- University of Alberta, Edmonton, Alberta, Canada
| | | | - Diana L Miglioretti
- Department of Public Health Sciences, University of California Davis School of Medicine, Davis, CA
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, NY, NY
| | | | - Joseph P Neglia
- Department of Pediatrics, University of Minnesota Medical School
| | | | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
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19
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Trentham-Dietz A, Alagoz O, Chapman C, Huang X, Jayasekera J, van Ravesteyn NT, Lee SJ, Schechter CB, Yeh JM, Plevritis SK, Mandelblatt JS. Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts. PLoS Comput Biol 2021; 17:e1009020. [PMID: 34138842 PMCID: PMC8211268 DOI: 10.1371/journal.pcbi.1009020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Since 2000, the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts. Since 2000, our research teams have used computer models of breast cancer to address important clinical and policy-relevant questions as part of the National Cancer Institute’s Cancer Intervention and Surveillance Modeling Network (CISNET). Our 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to represent the burden of breast cancer. We have used our models to investigate questions related to breast cancer biology, compare strategies to improve the balance of benefits and harms of screening mammography, and support insights into the delivery of care by modeling outcomes following clinical decisions about breast cancer treatment. Moving forward, our research will continue to use systems modeling to address issues related to reducing the burden of breast cancer including modeling structural inequities affecting racial disparities. Our future work will also leverage lessons from engaging multidisciplinary scientific teams, expand efforts to share modeling resources with other researchers, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.
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Affiliation(s)
- Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- * E-mail:
| | - Oguzhan Alagoz
- Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Christina Chapman
- Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, Michigan, United States of America
| | - Xuelin Huang
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Jinani Jayasekera
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown Lombardi Comprehensive Cancer Center, Washington, DC, United States of America
| | | | - Sandra J. Lee
- Department of Data Science, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Clyde B. Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Jennifer M. Yeh
- Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Sylvia K. Plevritis
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, United States of America
| | - Jeanne S. Mandelblatt
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown Lombardi Comprehensive Cancer Center, Washington, DC, United States of America
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20
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Yong JHE, Mainprize JG, Yaffe MJ, Ruan Y, Poirier AE, Coldman A, Nadeau C, Iragorri N, Hilsden RJ, Brenner DR. The impact of episodic screening interruption: COVID-19 and population-based cancer screening in Canada. J Med Screen 2021; 28:100-107. [PMID: 33241760 PMCID: PMC7691762 DOI: 10.1177/0969141320974711] [Citation(s) in RCA: 116] [Impact Index Per Article: 38.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Revised: 10/23/2020] [Accepted: 10/27/2020] [Indexed: 12/05/2022]
Abstract
BACKGROUND Population-based cancer screening can reduce cancer burden but was interrupted temporarily due to the COVID-19 pandemic. We estimated the long-term clinical impact of breast and colorectal cancer screening interruptions in Canada using a validated mathematical model. METHODS We used the OncoSim breast and colorectal cancers microsimulation models to explore scenarios of primary screening stops for 3, 6, and 12 months followed by 6-24-month transition periods of reduced screening volumes. For breast cancer, we estimated changes in cancer incidence over time, additional advanced-stage cases diagnosed, and excess cancer deaths in 2020-2029. For colorectal cancer, we estimated changes in cancer incidence over time, undiagnosed advanced adenomas and colorectal cancers in 2020, and lifetime excess cancer incidence and deaths. RESULTS Our simulations projected a surge of cancer cases when screening resumes. For breast cancer screening, a three-month interruption could increase cases diagnosed at advanced stages (310 more) and cancer deaths (110 more) in 2020-2029. A six-month interruption could lead to 670 extra advanced cancers and 250 additional cancer deaths. For colorectal cancers, a six-month suspension of primary screening could increase cancer incidence by 2200 cases with 960 more cancer deaths over the lifetime. Longer interruptions, and reduced volumes when screening resumes, would further increase excess cancer deaths. CONCLUSIONS Interruptions in cancer screening will lead to additional cancer deaths, additional advanced cancers diagnosed, and a surge in demand for downstream resources when screening resumes. An effective strategy is needed to minimize potential harm to people who missed their screening.
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Affiliation(s)
| | | | - Martin J Yaffe
- Sunnybrook Research Institute, Toronto, Canada
- Departments of Medical Biophysics and Medical Imaging, University of Toronto, Toronto, Canada
| | - Yibing Ruan
- Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, Canada
| | - Abbey E Poirier
- Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, Canada
| | | | | | | | - Robert J Hilsden
- Forzani & MacPhail Colon Cancer Screening Centre, Alberta Health Services, Calgary, Canada
- Departments of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Darren R Brenner
- Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, Canada
- Departments of Oncology and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Canada
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21
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Seigneurin A, Exbrayat C, Molinié F, Croisier L, Poncet F, Berquet K, Delafosse P, Colonna M. Association of Mammography Screening With a Reduction in Breast Cancer Mortality: A Modeling Study Using Population-Based Data From 2 French Departments. Am J Epidemiol 2021; 190:827-835. [PMID: 33043362 DOI: 10.1093/aje/kwaa218] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 10/01/2020] [Accepted: 10/07/2020] [Indexed: 12/24/2022] Open
Abstract
Meta-analyses of randomized controlled trials that started from 1963 to 1991 reported a decrease of breast cancer mortality, associated with mammography screening. However, the effectiveness of population-based screening programs conducted currently might have changed due to the higher effectiveness of treatments for late-stage cancers and the better diagnostic performance of mammography. The main objective of this study was to predict the reduction of breast cancer mortality associated with mammography screening in the current French setting. We compared breast cancer mortality in 2 simulated cohorts of women, which differed from each other solely in a 70% biennial participation in screening from 50 to 74 years old. The microsimulation model used for predictions was calibrated with incidence rates of breast cancer according to stage that were observed in Isère and Loire-Atlantique departments, France, in 2007-2013. The model predicted a decrease of breast cancer mortality associated with mammography screening of 18% (95% CI: 5, 31) and 17% (95% CI: 3, 29) for models calibrated with data from Isère and Loire-Atlantique departments, respectively. Our results highlight the interest in biennial mammography screening from ages 50 to 74 years old to decrease breast cancer mortality in the current setting, despite improvements in treatment effectiveness.
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22
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Mindfulness-based interventions for breast cancer patients in China across outcome domains: a systematic review and meta-analysis of the Chinese literature. Support Care Cancer 2021; 29:5611-5621. [PMID: 33770259 DOI: 10.1007/s00520-021-06166-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 03/18/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVE This study aims to evaluate the treatment effect of a mindfulness-based intervention for Chinese breast cancer patients across outcome domains, including symptom-related, psychosocial, and quality of life outcomes. METHODS Following the Cochrane Systematic Review guideline, we searched across five electronic databases, reference lists of eligible studies, professional websites, and major academic journals in Chinese. Publication bias was assessed using funnel plot and Vevea and Woods sensitivity analysis, and risk of bias was evaluated using the revised Cochrane risk of bias tool for randomized trials and risk of bias in non-randomized studies of interventions. A meta-analysis of Hedges' g was conducted using meta-regression with robust variance estimation. RESULTS Final analysis included a total of 45 controlled trials containing 286 effect size estimates. Across outcome domains, studies reported an overall large and statistically significant treatment effect, d = 0.921, 95% CI (0.805, 1.040), p < 0.001. Subgroup analyses of specific domains of outcome reported overall significant treatment effects for (1) symptom-related outcomes, d = 0.885, 95% CI (0.657, 1.110), p < 0.001; (2) psychosocial wellness outcomes, d = 0.984, 95% CI (0.879, 1.090), p < 0.001; and (3) quality of life, d = 0.990, 95% CI (0.776, 1.200), p < 0.001. Moderator analysis did not identify any significant moderator. CONCLUSION Chinese literature reported an overall statistically significant and large treatment effect of a mindfulness-based intervention for breast cancer patients in China. Except for physical symptom outcomes, e.g., nausea/vomiting and pain, a mindfulness-based intervention was effective across outcome domains among Chinese breast cancer patients.
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23
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van Ravesteyn NT, Schechter CB, Hampton JM, Alagoz O, van den Broek JJ, Kerlikowske K, Mandelblatt JS, Miglioretti DL, Sprague BL, Stout NK, de Koning HJ, Trentham-Dietz A, Tosteson ANA. Trade-Offs Between Harms and Benefits of Different Breast Cancer Screening Intervals Among Low-Risk Women. J Natl Cancer Inst 2021; 113:1017-1026. [PMID: 33515225 PMCID: PMC8502479 DOI: 10.1093/jnci/djaa218] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 10/30/2020] [Accepted: 12/21/2020] [Indexed: 01/25/2023] Open
Abstract
Background A paucity of research addresses breast cancer screening strategies for women at lower-than-average breast cancer risk. The aim of this study was to examine screening harms and benefits among women aged 50-74 years at lower-than-average breast cancer risk by breast density. Methods Three well-established, validated Cancer Intervention and Surveillance Network models were used to estimate the lifetime benefits and harms of different screening scenarios, varying by screening interval (biennial, triennial). Breast cancer deaths averted, life-years and quality-adjusted life-years gained, false-positives, benign biopsies, and overdiagnosis were assessed by relative risk (RR) level (0.6, 0.7, 0.85, 1 [average risk]) and breast density category, for US women born in 1970. Results Screening benefits decreased proportionally with decreasing risk and with lower breast density. False-positives, unnecessary biopsies, and the percentage overdiagnosis also varied substantially by breast density category; false-positives and unnecessary biopsies were highest in the heterogeneously dense category. For women with fatty or scattered fibroglandular breast density and a relative risk of no more than 0.85, the additional deaths averted and life-years gained were small with biennial vs triennial screening. For these groups, undergoing 4 additional screens (screening biennially [13 screens] vs triennially [9 screens]) averted no more than 1 additional breast cancer death and gained no more than 16 life-years and no more than 10 quality-adjusted life-years per 1000 women but resulted in up to 232 more false-positives per 1000 women. Conclusion Triennial screening from age 50 to 74 years may be a reasonable screening strategy for women with lower-than-average breast cancer risk and fatty or scattered fibroglandular breast density.
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Affiliation(s)
| | - Clyde B Schechter
- Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - John M Hampton
- Carbone Cancer Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Oguzhan Alagoz
- Carbone Cancer Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.,Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Jeroen J van den Broek
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Karla Kerlikowske
- Department of Medicine and Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Jeanne S Mandelblatt
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, WA, USA
| | - Diana L Miglioretti
- Department of Public Health Sciences, UC Davis School of Medicine, Davis, CA, USA.,Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Brian L Sprague
- Department of Surgery and University of Vermont Cancer Center, College of Medicine, University of Vermont, Burlington, VT, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Harry J de Koning
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Amy Trentham-Dietz
- Carbone Cancer Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.,Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Anna N A Tosteson
- Norris Cotton Cancer Center and the Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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24
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Yaffe MJ, Mainprize JG. The Value of All-Cause Mortality as a Metric for Assessing Breast Cancer Screening. J Natl Cancer Inst 2020; 112:989-993. [PMID: 32058543 PMCID: PMC7566389 DOI: 10.1093/jnci/djaa025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 01/09/2020] [Accepted: 02/05/2020] [Indexed: 12/29/2022] Open
Abstract
Although screening mammography has been demonstrated to contribute to reducing mortality due to breast cancer, some have suggested that reduced all-cause mortality should constitute the burden of proof for effectiveness. Using a microsimulation model of the development, detection, and treatment of breast cancer, it is straightforward to demonstrate that this is an unrealistic expectation for trials of practical size and period of observation, even where the reduction of breast cancer mortality is substantial. Estimates of all-cause mortality will depend not only on the efficacy of the screening intervention but also on the alignment between the age distribution of the effect of screening on reduction of deaths and that of the other major causes of death. The size of a randomized trial required to demonstrate a reduction in all-cause mortality will, therefore, depend on the length and timing of the observation period and will typically be at least 10 times larger than the size of a trial powered to test for a reduction in deaths due to breast cancer. For breast cancer, which represents a small fraction of overall deaths, all-cause mortality is neither a practical nor informative metric for assessing the effectiveness of screening.
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Affiliation(s)
- Martin J Yaffe
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - James G Mainprize
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Canada
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25
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Yeh JM, Lowry KP, Schechter CB, Diller LR, Alagoz O, Armstrong GT, Hampton JM, Leisenring W, Liu Q, Mandelblatt JS, Miglioretti DL, Moskowitz CS, Oeffinger KC, Trentham-Dietz A, Stout NK. Clinical Benefits, Harms, and Cost-Effectiveness of Breast Cancer Screening for Survivors of Childhood Cancer Treated With Chest Radiation : A Comparative Modeling Study. Ann Intern Med 2020; 173:331-341. [PMID: 32628531 PMCID: PMC7510774 DOI: 10.7326/m19-3481] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Surveillance with annual mammography and breast magnetic resonance imaging (MRI) is recommended for female survivors of childhood cancer treated with chest radiation, yet benefits, harms, and costs are uncertain. OBJECTIVE To compare the benefits, harms, and cost-effectiveness of breast cancer screening strategies in childhood cancer survivors. DESIGN Collaborative simulation modeling using 2 Cancer Intervention and Surveillance Modeling Network breast cancer models. DATA SOURCES Childhood Cancer Survivor Study and published data. TARGET POPULATION Women aged 20 years with a history of chest radiotherapy. TIME HORIZON Lifetime. PERSPECTIVE Payer. INTERVENTION Annual MRI with or without mammography, starting at age 25, 30, or 35 years. OUTCOME MEASURES Breast cancer deaths averted, false-positive screening results, benign biopsy results, and incremental cost-effectiveness ratios (ICERs). RESULTS OF BASE-CASE ANALYSIS Lifetime breast cancer mortality risk without screening was 10% to 11% across models. Compared with no screening, starting at age 25 years, annual mammography with MRI averted the most deaths (56% to 71%) and annual MRI (without mammography) averted 56% to 62%. Both strategies had the most screening tests, false-positive screening results, and benign biopsy results. For an ICER threshold of less than $100 000 per quality-adjusted life-year gained, screening beginning at age 30 years was preferred. RESULTS OF SENSITIVITY ANALYSIS Assuming lower screening performance, the benefit of adding mammography to MRI increased in both models, although the conclusions about preferred starting age remained unchanged. LIMITATION Elevated breast cancer risk was based on survivors diagnosed with childhood cancer between 1970 and 1986. CONCLUSION Early initiation (at ages 25 to 30 years) of annual breast cancer screening with MRI, with or without mammography, might reduce breast cancer mortality by half or more in survivors of childhood cancer. PRIMARY FUNDING SOURCE American Cancer Society and National Institutes of Health.
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Affiliation(s)
- Jennifer M. Yeh
- Department of Pediatrics, Harvard Medical School and Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA 02115
| | - Kathryn P. Lowry
- University of Washington, Seattle Cancer Care Alliance, 825 Eastlake Ave. E., Seattle, WA 98109
| | - Clyde B. Schechter
- Department of Family and Social Medicine, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Block Building 406, Bronx, NY 10461
| | - Lisa R. Diller
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, 450 Brookline Avenue, Boston, MA 02115
| | - Oguzhan Alagoz
- University of Wisconsin–Madison, 1513 University Avenue, Madison, WI 53706
| | - Gregory T. Armstrong
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, 262 Danny Thomas Pl, Memphis, TN 38105
| | - John M. Hampton
- University of Wisconsin Carbone Cancer Center, 610 Walnut Street, WARF Room 307, Madison, WI 53726
| | - Wendy Leisenring
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Seattle, WA, 98109
| | - Qi Liu
- University of Alberta, 11405 87th Avenue, Edmonton, Alberta, Canada T6G 1C9
| | - Jeanne S. Mandelblatt
- Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street Northwest, Suite 4100, Washington, DC 20007
| | - Diana L. Miglioretti
- Department of Public Health Sciences, University of California Davis School of Medicine, One Shields Avenue, Med-Sci 1C, Room 145, Davis, CA 95616
| | - Chaya S. Moskowitz
- Memorial Sloan Kettering Cancer Center, 485 Lexington Ave, 2nd floor, NY, NY 10017
| | | | - Amy Trentham-Dietz
- University of Wisconsin Carbone Cancer Center, 610 Walnut Street, WARF Room 307, Madison, WI 53726
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Landmark Center, 401 Park Drive, Suite 401, Boston, MA 02215
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26
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Lowry KP, Trentham-Dietz A, Schechter CB, Alagoz O, Barlow WE, Burnside ES, Conant EF, Hampton JM, Huang H, Kerlikowske K, Lee SJ, Miglioretti DL, Sprague BL, Tosteson ANA, Yaffe MJ, Stout NK. Long-Term Outcomes and Cost-Effectiveness of Breast Cancer Screening With Digital Breast Tomosynthesis in the United States. J Natl Cancer Inst 2020; 112:582-589. [PMID: 31503283 PMCID: PMC7301096 DOI: 10.1093/jnci/djz184] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 08/01/2019] [Accepted: 09/05/2019] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Digital breast tomosynthesis (DBT) is increasingly being used for routine breast cancer screening. We projected the long-term impact and cost-effectiveness of DBT compared to conventional digital mammography (DM) for breast cancer screening in the United States. METHODS Three Cancer Intervention and Surveillance Modeling Network breast cancer models simulated US women ages 40 years and older undergoing breast cancer screening with either DBT or DM starting in 2011 and continuing for the lifetime of the cohort. Screening performance estimates were based on observational data; in an alternative scenario, we assumed 4% higher sensitivity for DBT. Analyses used federal payer perspective; costs and utilities were discounted at 3% annually. Outcomes included breast cancer deaths, quality-adjusted life-years (QALYs), false-positive examinations, costs, and incremental cost-effectiveness ratios (ICERs). RESULTS Compared to DM, DBT screening resulted in a slight reduction in breast cancer deaths (range across models 0-0.21 per 1000 women), small increase in QALYs (1.97-3.27 per 1000 women), and a 24-28% reduction in false-positive exams (237-268 per 1000 women) relative to DM. ICERs ranged from $195 026 to $270 135 per QALY for DBT relative to DM. When assuming 4% higher DBT sensitivity, ICERs decreased to $130 533-$156 624 per QALY. ICERs were sensitive to DBT costs, decreasing to $78 731 to $168 883 and $52 918 to $118 048 when the additional cost of DBT was reduced to $36 and $26 (from baseline of $56), respectively. CONCLUSION DBT reduces false-positive exams while achieving similar or slightly improved health benefits. At current reimbursement rates, the additional costs of DBT screening are likely high relative to the benefits gained; however, DBT could be cost-effective at lower screening costs.
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Affiliation(s)
- Kathryn P Lowry
- Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, WA
| | | | - Clyde B Schechter
- University of Wisconsin-Madison, Madison, WI; Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, NY
| | - Oguzhan Alagoz
- Carbone Cancer Center and Department of Population Health Sciences
- School of Medicine and Public Health, and Department of Industrial and Systems Engineering
| | - William E Barlow
- Cancer Research and Biostatistics, University of Washington, Seattle, WA
| | | | - Emily F Conant
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - John M Hampton
- Carbone Cancer Center and Department of Population Health Sciences
| | - Hui Huang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA
| | - Karla Kerlikowske
- Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA
| | - Sandra J Lee
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA
| | - Diana L Miglioretti
- Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA
| | - Brian L Sprague
- Departments of Surgery and Radiology, University of Vermont Cancer Center, University of Vermont Larner College of Medicine, Burlington, VT
| | - Anna N A Tosteson
- The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH
| | - Martin J Yaffe
- Departments of Medical Biophysics and Medical Imaging, University of Toronto, Toronto, Canada
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA
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Benefits and Harms of Mammography Screening for Women With Down Syndrome: a Collaborative Modeling Study. J Gen Intern Med 2019; 34:2374-2381. [PMID: 31385214 PMCID: PMC6848489 DOI: 10.1007/s11606-019-05182-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 03/20/2019] [Accepted: 06/07/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND Women with Down syndrome have a lower breast cancer risk and significantly lower life expectancies than women without Down syndrome. Therefore, it is not clear whether mammography screening strategies used for women without Down syndrome would benefit women with Down syndrome in the same way. OBJECTIVE To determine the benefits and harms of various mammography screening strategies for women with Down syndrome using collaborative simulation modeling. DESIGN Two established Cancer Intervention and Surveillance Modeling Network (CISNET) simulation models estimated the benefits and harms of various screening strategies for women with Down syndrome over a lifetime horizon. PARTICIPANTS We modeled a hypothetical cohort of US women with Down syndrome who were born in 1970. INTERVENTIONS Annual, biennial, triennial, and one-time digital mammography screenings during the ages 40-74. MAIN MEASURES The models estimated numbers of mammograms, false-positives, benign biopsies, breast cancer deaths prevented, and life-years gained per 1000 screened women when compared with no screening. KEY RESULTS In average-risk women 50-74, biennial screening incurred 122 mammograms, 10 false-positive mammograms, and 1.4 benign biopsies per one life-year gained compared with no screening. In women with Down syndrome, the same screening strategy incurred 2752 mammograms, 242 false-positive mammograms, and 34 benign biopsies per one life-year gained compared with no screening. The harm/benefit ratio varied for other screening strategies, and was most favorable for one-time screening at age 50, which incurred 1629 mammograms, 144 false-positive mammograms, and 20 benign biopsies per one life-year gained compared with no screening. CONCLUSIONS The harm/benefit ratios for various mammography screening strategies in women with Down syndrome are not as favorable as those for average-risk women. The benefit of screening mammography for women with Down syndrome is less pronounced due to lower breast cancer risk and shorter life expectancy.
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Yaffe MJ, Jong RA, Pritchard KI. Breast Cancer Screening: Beyond Mortality. JOURNAL OF BREAST IMAGING 2019; 1:161-165. [PMID: 38424760 DOI: 10.1093/jbi/wbz038] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Indexed: 03/02/2024]
Abstract
Traditionally, the effectiveness of breast cancer screening has been measured in terms of reducing the number of deaths attributable to breast cancer. Other metrics such as the number of life-years or quality-adjusted life-years gained through screening may be more relevant and certainly may better reflect the important burden of the disease on younger women, their families, and society. The effects of earlier detection of breast cancer in reducing morbidities associated with treatment have often also been neglected. In addition, the harms and limitations associated with cancer screening have been poorly quantified and are seldom put into perspective vis-à-vis the benefits. Here, these alternative measures will be discussed and quantified.
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Affiliation(s)
- Martin J Yaffe
- Sunnybrook Health Sciences Centre and The University of Toronto, Departments of Medical Biophysics, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre and The University of Toronto, Medical Imaging, Toronto, ON, Canada
| | - Roberta A Jong
- Sunnybrook Health Sciences Centre and The University of Toronto, Medical Imaging, Toronto, ON, Canada
| | - Kathleen I Pritchard
- Sunnybrook Health Sciences Centre and The University of Toronto, Medical Oncology, Toronto, ON, Canada
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Yaffe MJ, Mittmann N, Alagoz O, Trentham-Dietz A, Tosteson AN, Stout NK. The effect of mammography screening regimen on incidence-based breast cancer mortality. J Med Screen 2018; 25:197-204. [PMID: 30049249 DOI: 10.1177/0969141318780152] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVES Incidence-based mortality quantifies the distribution of cancer deaths and life-years lost, according to age at detection. We investigated the temporal distribution of the disease burden, and the effect of starting and stopping ages and interval between screening mammography examinations, on incidence-based mortality. METHODS Incidence-based mortality was estimated using an established breast cancer simulation model, adapted and validated to simulate breast cancer incidence, screening performance, and delivery of therapies in Canada. Ten strategies were examined, with varying starting age (40 or 50), stopping age (69 or 74), and interval (1, 2, 3 years), and "No Screening." Life-years lost were computed as the difference between model predicted time of breast cancer death and that estimated from life tables. RESULTS Without screening, 70% of the burden in terms of breast cancer deaths extends between ages 45 and 75. The mean of the distribution of ages of detection of breast cancers that will be fatal in an unscreened population is 61.8 years, while the mean age of detection weighted by the number of life-years lost is 55, a downward shift of 6.8 years. Similarly, the mean age of detection for the distribution of life-years gained through screening is lower than that for breast cancer deaths averted. CONCLUSION Incidence-based mortality predictions from modeling elucidate the age dependence of the breast cancer burden and can provide guidance for optimizing the timing of screening regimens to achieve maximal impact. Of the regimens studied, the greatest lifesaving effect was achieved with annual screening beginning at age 40.
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Affiliation(s)
- Martin J Yaffe
- 1 Physical Sciences Program, Sunnybrook Research Institute, Toronto, Canada.,2 Departments of Medical Biophysics and Medical Imaging, University of Toronto, Toronto, Canada.,3 Ontario Institute for Cancer Research, Toronto, Canada
| | - Nicole Mittmann
- 4 Health Outcomes and PharmacoEconomic (HOPE) Research Centre, Sunnybrook Research Institute, Toronto, Canada.,5 Applied Research in Cancer Control, Department of Pharmacology, University of Toronto, Toronto, Canada.,7 Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, USA
| | - Oguzhan Alagoz
- 7 Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, USA.,8 Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, USA
| | - Amy Trentham-Dietz
- 7 Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, USA
| | - Anna Na Tosteson
- 9 The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, USA
| | - Natasha K Stout
- 10 Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, USA
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van Ravesteyn NT, van den Broek JJ, Li X, Weedon-Fekjær H, Schechter CB, Alagoz O, Huang X, Weaver DL, Burnside ES, Punglia RS, de Koning HJ, Lee SJ. Modeling Ductal Carcinoma In Situ (DCIS): An Overview of CISNET Model Approaches. Med Decis Making 2018; 38:126S-139S. [PMID: 29554463 PMCID: PMC5862063 DOI: 10.1177/0272989x17729358] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Ductal carcinoma in situ (DCIS) can be a precursor to invasive breast cancer. Since the advent of screening mammography in the 1980's, the incidence of DCIS has increased dramatically. The value of screen detection and treatment of DCIS, however, is a matter of controversy, as it is unclear the extent to which detection and treatment of DCIS prevents invasive disease and reduces breast cancer mortality. The aim of this paper is to provide an overview of existing Cancer Intervention and Surveillance Modelling Network (CISNET) modeling approaches for the natural history of DCIS, and to compare these to other modeling approaches reported in the literature. DESIGN Five of the 6 CISNET models currently include DCIS. Most models assume that some, but not all, lesions progress to invasive cancer. The natural history of DCIS cannot be directly observed and the CISNET models differ in their assumptions and in the data sources used to estimate the DCIS model parameters. RESULTS These model differences translate into variation in outcomes, such as the amount of overdiagnosis of DCIS, with estimates ranging from 34% to 72% for biennial screening from ages 50 to 74 y. The other models described in the literature also report a large range in outcomes, with progression rates varying from 20% to 91%. LIMITATIONS DCIS grade was not yet included in the CISNET models. CONCLUSION In the future, DCIS data by grade from active surveillance trials, the development of predictive markers of progression probability, and evidence from other screening modalities, such as tomosynthesis, may be used to inform and improve the models' representation of DCIS, and might lead to convergence of the model estimates. Until then, the CISNET model results consistently show a considerable amount of overdiagnosis of DCIS, supporting the safety and value of observational trials for low-risk DCIS.
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Affiliation(s)
| | - Jeroen J van den Broek
- Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Xiaoxue Li
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Harald Weedon-Fekjær
- Center for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Clyde B Schechter
- Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Donald L Weaver
- Department of Pathology and Laboratory Medicine, University of Vermont, Burlington, VT, USA
| | - Elizabeth S Burnside
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - Rinaa S Punglia
- Department of Radiation Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
| | - Harry J de Koning
- Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Sandra J Lee
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA
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Alagoz O, Berry DA, de Koning HJ, Feuer EJ, Lee SJ, Plevritis SK, Schechter CB, Stout NK, Trentham-Dietz A, Mandelblatt JS. Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models. Med Decis Making 2018; 38:3S-8S. [PMID: 29554472 PMCID: PMC5862043 DOI: 10.1177/0272989x17737507] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group is a consortium of National Cancer Institute-sponsored investigators who use statistical and simulation modeling to evaluate the impact of cancer control interventions on long-term population-level breast cancer outcomes such as incidence and mortality and to determine the impact of different breast cancer control strategies. The CISNET breast cancer models have been continuously funded since 2000. The models have gone through several updates since their inception to reflect advances in the understanding of the molecular basis of breast cancer, changes in the prevalence of common risk factors, and improvements in therapy and early detection technology. This article provides an overview and history of the CISNET breast cancer models, provides an overview of the major changes in the model inputs over time, and presents examples for how CISNET breast cancer models have been used for policy evaluation.
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Affiliation(s)
- Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Donald A Berry
- Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Harry J de Koning
- Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Eric J Feuer
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA
| | - Sandra J Lee
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Sylvia K Plevritis
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Clyde B Schechter
- Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Jeanne S Mandelblatt
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA
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van den Broek JJ, van Ravesteyn NT, Mandelblatt JS, Huang H, Ergun MA, Burnside ES, Xu C, Li Y, Alagoz O, Lee SJ, Stout NK, Song J, Trentham-Dietz A, Plevritis SK, Moss SM, de Koning HJ. Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49. Med Decis Making 2018; 38:140S-150S. [PMID: 29554468 PMCID: PMC5862071 DOI: 10.1177/0272989x17718168] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results. METHODS Five established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial. RESULTS The models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial. CONCLUSIONS The models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.
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Affiliation(s)
| | | | - Jeanne S Mandelblatt
- Department of Oncology, Georgetown-Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington DC, USA
| | - Hui Huang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA
| | - Mehmet Ali Ergun
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Elizabeth S Burnside
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Cong Xu
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Yisheng Li
- Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Sandra J Lee
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA
| | - Natasha K Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Juhee Song
- Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Amy Trentham-Dietz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Sylvia K Plevritis
- Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Sue M Moss
- Department of cancer prevention, Wolfson Institute, Queen Mary University of London, London, UK
| | - Harry J de Koning
- Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands
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Mandelblatt JS, Near AM, Miglioretti DL, Munoz D, Sprague BL, Trentham-Dietz A, Gangnon R, Kurian AW, Weedon-Fekjaer H, Cronin KA, Plevritis SK. Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling. Med Decis Making 2018; 38:9S-23S. [PMID: 29554466 PMCID: PMC5862072 DOI: 10.1177/0272989x17700624] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Since their inception in 2000, the Cancer Intervention and Surveillance Network (CISNET) breast cancer models have collaborated to use a nationally representative core of common input parameters to represent key components of breast cancer control in each model. Employment of common inputs permits greater ability to compare model output than when each model begins with different input parameters. The use of common inputs also enhances inferences about the results, and provides a range of reasonable results based on variations in model structure, assumptions, and methods of use of the input values. The common input data are updated for each analysis to ensure that they reflect the most current practice and knowledge about breast cancer. The common core of parameters includes population rates of births and deaths; age- and cohort-specific temporal rates of breast cancer incidence in the absence of screening and treatment; effects of risk factors on incidence trends; dissemination of plain film and digital mammography; screening test performance characteristics; stage or size distribution of screen-, interval-, and clinically- detected tumors by age; the joint distribution of ER/HER2 by age and stage; survival in the absence of screening and treatment by stage and molecular subtype; age-, stage-, and molecular subtype-specific therapy; dissemination and effectiveness of therapies over time; and competing non-breast cancer mortality. METHOD AND RESULTS In this paper, we summarize the methods and results for the common input values presently used in the CISNET breast cancer models, note assumptions made because of unobservable phenomena and/or unavailable data, and highlight plans for the development of future parameters. CONCLUSION These data are intended to enhance the transparency of the breast CISNET models.
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Affiliation(s)
- Jeanne S Mandelblatt
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA
| | - Aimee M Near
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC, USA
| | - Diana L Miglioretti
- Department of Public Health Sciences, UC Davis School of Medicine, Davis, California, USA and Group Health Research Institute, Seattle, WA, USA and Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
| | - Diego Munoz
- Departments of Biomedical Informatics and Radiology, School of Medicine, Stanford University, Stanford, California, USA
| | - Brian L Sprague
- Department of Surgery, College of Medicine, University of Vermont, Burlington, Vermont, USA
| | - Amy Trentham-Dietz
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ronald Gangnon
- Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Biostatistics and Medical Informatics and Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Allison W Kurian
- Departments of Medicine and Health Research & Policy, School of Medicine, Stanford University, Stanford, California, USA
| | - Harald Weedon-Fekjaer
- Oslo Center for Biostatistics and Epidemiology [OCBE], Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Kathleen A Cronin
- Surveillance Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Sylvia K Plevritis
- Department of Radiology, School of Medicine, Stanford University, Stanford, California, USA
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Huang X, Li Y, Song J, Berry DA. A Bayesian Simulation Model for Breast Cancer Screening, Incidence, Treatment, and Mortality. Med Decis Making 2018; 38:78S-88S. [PMID: 28627297 PMCID: PMC5711634 DOI: 10.1177/0272989x17714473] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The important but complicated research questions regarding the optimization of mammography screening for the detection of breast cancer are unable to be answered through any single trial or a simple meta-analysis of related trials. The Cancer Intervention and Surveillance Network (CISNET) breast groups provide answers using complex statistical models to simulate population dynamics. Among them, the MD Anderson Cancer Center (Model M) takes a unique approach by not making any assumptions on the natural history of breast cancer, such as the distribution of the indolent time before detection, but simulating only the observable part of a woman's disease and life. METHODS The simulations start with 4 million women in the age distribution found in the year 1975, and follow them over several years. Input parameters are used to describe their breast cancer incidence rates, treatment efficacy, and survival. With these parameters, each woman's history of breast cancer diagnosis, treatment, and survival are generated and recorded each year. Research questions can then be answered by comparing the outcomes of interest, such as mortality rates, quality-adjusted life years, number of false positives, differences between hypothetical scenarios, such as different combinations of screening and treatment strategies. We use our model to estimate the relative contributions of screening and treatments on the mortality reduction in the United States, for both overall and different molecular (ER, HER2) subtypes of breast cancer. RESULTS We estimate and compare the benefits (life-years gained) and harm (false-positives, over-diagnoses) of mammography screening strategies with different frequencies (annual, biennial, triennial, mixed) and different starting (40 and 50 years) and end ages (70 and 80 years). CONCLUSIONS We will extend our model in future studies to account for local, regional, and distant disease recurrences.
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Affiliation(s)
- Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yisheng Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Juhee Song
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Donald A Berry
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Mittmann N, Stout NK, Tosteson ANA, Trentham-Dietz A, Alagoz O, Yaffe MJ. Cost-effectiveness of mammography from a publicly funded health care system perspective. CMAJ Open 2018; 6:E77-E86. [PMID: 29440151 PMCID: PMC5878949 DOI: 10.9778/cmajo.20170106] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND The implementation of population-wide breast cancer screening programs has important budget implications. We evaluated the cost-effectiveness of various breast cancer screening scenarios in Canada from a publicly funded health care system perspective using an established breast cancer simulation model. METHODS Breast cancer incidence, outcomes and total health care system costs (screening, investigation, diagnosis and treatment) for the Canadian health care environment were modelled. The model predicted costs (in 2012 dollars), life-years gained and quality-adjusted life-years (QALYs) gained for 11 active screening scenarios that varied by age range for starting and stopping screening (40-74 yr) and frequency of screening (annual, biennial or triennial) relative to no screening. All outcomes were discounted. Marginal and incremental cost-effectiveness analyses were conducted. One-way sensitivity analyses of key parameters assessed robustness. RESULTS The lifetime overall costs (undiscounted) to the health care system for annual screening per 1000 women ranged from $7.4 million (for women aged 50-69 yr) to $10.7 million (40-74 yr). For biennial and triennial screening per 1000 women (aged 50-74 yr), costs were less, at about $6.1 million and $5.3 million, respectively. The incremental cost-utility ratio varied from $36 981/QALY for triennial screening in women aged 50-69 versus no screening to $38 142/QALY for biennial screening in those aged 50-69 and $83 845/QALY for annual screening in those aged 40-74. INTERPRETATION Our economic analysis showed that both benefits of mortality reduction and costs rose together linearly with the number of lifetime screens per women. The decision on how to screen is related mainly to willingness to pay and additional considerations such as the number of women recalled after a positive screening result.
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Affiliation(s)
- Nicole Mittmann
- Affiliations: Sunnybrook Research Institute (Mittmann), Sunnybrook Health Sciences Centre; Department of Pharmacology and Toxicology (Mittmann), University of Toronto, Toronto, Ont.; Department of Population Medicine (Stout), Harvard Medical School and Harvard Pilgrim Health Care, Boston, Mass.; Dartmouth Institute for Health Policy and Clinical Practice (Tosteson), Geisel School of Medicine, Dartmouth College, Hanover, NH; Department of Population Health Sciences and Carbone Cancer Center (Trentham-Dietz, Alagoz); Department of Industrial and Systems Engineering (Alagoz), University of Wisconsin-Madison, Madison, Wisc.; Physical Sciences Program (Yaffe), Sunnybrook Research Institute, Sunnybrook Health Sciences Centre; Departments of Medical Biophysics and Medical Imaging (Yaffe), University of Toronto, Toronto, Ont
| | - Natasha K Stout
- Affiliations: Sunnybrook Research Institute (Mittmann), Sunnybrook Health Sciences Centre; Department of Pharmacology and Toxicology (Mittmann), University of Toronto, Toronto, Ont.; Department of Population Medicine (Stout), Harvard Medical School and Harvard Pilgrim Health Care, Boston, Mass.; Dartmouth Institute for Health Policy and Clinical Practice (Tosteson), Geisel School of Medicine, Dartmouth College, Hanover, NH; Department of Population Health Sciences and Carbone Cancer Center (Trentham-Dietz, Alagoz); Department of Industrial and Systems Engineering (Alagoz), University of Wisconsin-Madison, Madison, Wisc.; Physical Sciences Program (Yaffe), Sunnybrook Research Institute, Sunnybrook Health Sciences Centre; Departments of Medical Biophysics and Medical Imaging (Yaffe), University of Toronto, Toronto, Ont
| | - Anna N A Tosteson
- Affiliations: Sunnybrook Research Institute (Mittmann), Sunnybrook Health Sciences Centre; Department of Pharmacology and Toxicology (Mittmann), University of Toronto, Toronto, Ont.; Department of Population Medicine (Stout), Harvard Medical School and Harvard Pilgrim Health Care, Boston, Mass.; Dartmouth Institute for Health Policy and Clinical Practice (Tosteson), Geisel School of Medicine, Dartmouth College, Hanover, NH; Department of Population Health Sciences and Carbone Cancer Center (Trentham-Dietz, Alagoz); Department of Industrial and Systems Engineering (Alagoz), University of Wisconsin-Madison, Madison, Wisc.; Physical Sciences Program (Yaffe), Sunnybrook Research Institute, Sunnybrook Health Sciences Centre; Departments of Medical Biophysics and Medical Imaging (Yaffe), University of Toronto, Toronto, Ont
| | - Amy Trentham-Dietz
- Affiliations: Sunnybrook Research Institute (Mittmann), Sunnybrook Health Sciences Centre; Department of Pharmacology and Toxicology (Mittmann), University of Toronto, Toronto, Ont.; Department of Population Medicine (Stout), Harvard Medical School and Harvard Pilgrim Health Care, Boston, Mass.; Dartmouth Institute for Health Policy and Clinical Practice (Tosteson), Geisel School of Medicine, Dartmouth College, Hanover, NH; Department of Population Health Sciences and Carbone Cancer Center (Trentham-Dietz, Alagoz); Department of Industrial and Systems Engineering (Alagoz), University of Wisconsin-Madison, Madison, Wisc.; Physical Sciences Program (Yaffe), Sunnybrook Research Institute, Sunnybrook Health Sciences Centre; Departments of Medical Biophysics and Medical Imaging (Yaffe), University of Toronto, Toronto, Ont
| | - Oguzhan Alagoz
- Affiliations: Sunnybrook Research Institute (Mittmann), Sunnybrook Health Sciences Centre; Department of Pharmacology and Toxicology (Mittmann), University of Toronto, Toronto, Ont.; Department of Population Medicine (Stout), Harvard Medical School and Harvard Pilgrim Health Care, Boston, Mass.; Dartmouth Institute for Health Policy and Clinical Practice (Tosteson), Geisel School of Medicine, Dartmouth College, Hanover, NH; Department of Population Health Sciences and Carbone Cancer Center (Trentham-Dietz, Alagoz); Department of Industrial and Systems Engineering (Alagoz), University of Wisconsin-Madison, Madison, Wisc.; Physical Sciences Program (Yaffe), Sunnybrook Research Institute, Sunnybrook Health Sciences Centre; Departments of Medical Biophysics and Medical Imaging (Yaffe), University of Toronto, Toronto, Ont
| | - Martin J Yaffe
- Affiliations: Sunnybrook Research Institute (Mittmann), Sunnybrook Health Sciences Centre; Department of Pharmacology and Toxicology (Mittmann), University of Toronto, Toronto, Ont.; Department of Population Medicine (Stout), Harvard Medical School and Harvard Pilgrim Health Care, Boston, Mass.; Dartmouth Institute for Health Policy and Clinical Practice (Tosteson), Geisel School of Medicine, Dartmouth College, Hanover, NH; Department of Population Health Sciences and Carbone Cancer Center (Trentham-Dietz, Alagoz); Department of Industrial and Systems Engineering (Alagoz), University of Wisconsin-Madison, Madison, Wisc.; Physical Sciences Program (Yaffe), Sunnybrook Research Institute, Sunnybrook Health Sciences Centre; Departments of Medical Biophysics and Medical Imaging (Yaffe), University of Toronto, Toronto, Ont
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Plevritis SK, Munoz D, Kurian AW, Stout NK, Alagoz O, Near AM, Lee SJ, van den Broek JJ, Huang X, Schechter CB, Sprague BL, Song J, de Koning HJ, Trentham-Dietz A, van Ravesteyn NT, Gangnon R, Chandler Y, Li Y, Xu C, Ergun MA, Huang H, Berry DA, Mandelblatt JS. Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012. JAMA 2018; 319:154-164. [PMID: 29318276 PMCID: PMC5833658 DOI: 10.1001/jama.2017.19130] [Citation(s) in RCA: 178] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
IMPORTANCE Given recent advances in screening mammography and adjuvant therapy (treatment), quantifying their separate and combined effects on US breast cancer mortality reductions by molecular subtype could guide future decisions to reduce disease burden. OBJECTIVE To evaluate the contributions associated with screening and treatment to breast cancer mortality reductions by molecular subtype based on estrogen-receptor (ER) and human epidermal growth factor receptor 2 (ERBB2, formerly HER2 or HER2/neu). DESIGN, SETTING, AND PARTICIPANTS Six Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2012 using national data on plain-film and digital mammography patterns and performance, dissemination and efficacy of ER/ERBB2-specific treatment, and competing mortality. Multiple US birth cohorts were simulated. EXPOSURES Screening mammography and treatment. MAIN OUTCOMES AND MEASURES The models compared age-adjusted, overall, and ER/ERBB2-specific breast cancer mortality rates from 2000 to 2012 for women aged 30 to 79 years relative to the estimated mortality rate in the absence of screening and treatment (baseline rate); mortality reductions were apportioned to screening and treatment. RESULTS In 2000, the estimated reduction in overall breast cancer mortality rate was 37% (model range, 27%-42%) relative to the estimated baseline rate in 2000 of 64 deaths (model range, 56-73) per 100 000 women: 44% (model range, 35%-60%) of this reduction was associated with screening and 56% (model range, 40%-65%) with treatment. In 2012, the estimated reduction in overall breast cancer mortality rate was 49% (model range, 39%-58%) relative to the estimated baseline rate in 2012 of 63 deaths (model range, 54-73) per 100 000 women: 37% (model range, 26%-51%) of this reduction was associated with screening and 63% (model range, 49%-74%) with treatment. Of the 63% associated with treatment, 31% (model range, 22%-37%) was associated with chemotherapy, 27% (model range, 18%-36%) with hormone therapy, and 4% (model range, 1%-6%) with trastuzumab. The estimated relative contributions associated with screening vs treatment varied by molecular subtype: for ER+/ERBB2-, 36% (model range, 24%-50%) vs 64% (model range, 50%-76%); for ER+/ERBB2+, 31% (model range, 23%-41%) vs 69% (model range, 59%-77%); for ER-/ERBB2+, 40% (model range, 34%-47%) vs 60% (model range, 53%-66%); and for ER-/ERBB2-, 48% (model range, 38%-57%) vs 52% (model range, 44%-62%). CONCLUSIONS AND RELEVANCE In this simulation modeling study that projected trends in breast cancer mortality rates among US women, decreases in overall breast cancer mortality from 2000 to 2012 were associated with advances in screening and in adjuvant therapy, although the associations varied by breast cancer molecular subtype.
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Affiliation(s)
- Sylvia K. Plevritis
- Departments of Radiology and Biomedical Data Science, School of Medicine, Stanford University, Stanford, California
| | - Diego Munoz
- Departments of Radiology and Biomedical Data Science, School of Medicine, Stanford University, Stanford, California
| | - Allison W. Kurian
- Departments of Medicine and Health Research and Policy, School of Medicine, Stanford University, Stanford, California
| | - Natasha K. Stout
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Oguzhan Alagoz
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison
- Carbone Cancer Center, University of Wisconsin-Madison
| | - Aimee M. Near
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC
| | - Sandra J. Lee
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts
| | - Jeroen J. van den Broek
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | - Xuelin Huang
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston
| | - Clyde B. Schechter
- Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York
| | - Brian L. Sprague
- Department of Surgery, College of Medicine, University of Vermont, Burlington
| | - Juhee Song
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston
| | - Harry J. de Koning
- Department of Public Health, Erasmus MC University Medical Center, Rotterdam, the Netherlands
| | | | | | - Ronald Gangnon
- Carbone Cancer Center, University of Wisconsin-Madison
- Department of Biostatistics and Medical Informatics and Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health
| | - Young Chandler
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC
| | - Yisheng Li
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston
| | - Cong Xu
- Departments of Radiology and Biomedical Data Science, School of Medicine, Stanford University, Stanford, California
| | - Mehmet Ali Ergun
- Department of Industrial and Systems Engineering, University of Wisconsin-Madison
| | - Hui Huang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts
| | - Donald A. Berry
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston
| | - Jeanne S. Mandelblatt
- Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC
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Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening. Breast Cancer Res Treat 2017; 168:229-239. [PMID: 29185118 DOI: 10.1007/s10549-017-4582-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2017] [Accepted: 11/15/2017] [Indexed: 12/18/2022]
Abstract
PURPOSE Due to limitations in the ability to identify non-progressive disease, ductal carcinoma in situ (DCIS) is usually managed similarly to localized invasive breast cancer. We used simulation modeling to evaluate the potential impact of a hypothetical test that identifies non-progressive DCIS. METHODS A discrete-event model simulated a cohort of U.S. women undergoing digital screening mammography. All women diagnosed with DCIS underwent the hypothetical DCIS prognostic test. Women with test results indicating progressive DCIS received standard breast cancer treatment and a decrement to quality of life corresponding to the treatment. If the DCIS test indicated non-progressive DCIS, no treatment was received and women continued routine annual surveillance mammography. A range of test performance characteristics and prevalence of non-progressive disease were simulated. Analysis compared discounted quality-adjusted life years (QALYs) and costs for test scenarios to base-case scenarios without the test. RESULTS Compared to the base case, a perfect prognostic test resulted in a 40% decrease in treatment costs, from $13,321 to $8005 USD per DCIS case. A perfect test produced 0.04 additional QALYs (16 days) for women diagnosed with DCIS, added to the base case of 5.88 QALYs per DCIS case. The results were sensitive to the performance characteristics of the prognostic test, the proportion of DCIS cases that were non-progressive in the model, and the frequency of mammography screening in the population. CONCLUSION A prognostic test that identifies non-progressive DCIS would substantially reduce treatment costs but result in only modest improvements in quality of life when averaged over all DCIS cases.
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