Literature DB >> 22547470

Tipping the balance of benefits and harms to favor screening mammography starting at age 40 years: a comparative modeling study of risk.

Nicolien T van Ravesteyn1, Diana L Miglioretti, Natasha K Stout, Sandra J Lee, Clyde B Schechter, Diana S M Buist, Hui Huang, Eveline A M Heijnsdijk, Amy Trentham-Dietz, Oguzhan Alagoz, Aimee M Near, Karla Kerlikowske, Heidi D Nelson, Jeanne S Mandelblatt, Harry J de Koning.   

Abstract

BACKGROUND: Timing of initiation of screening for breast cancer is controversial in the United States.
OBJECTIVE: To determine the threshold relative risk (RR) at which the harm-benefit ratio of screening women aged 40 to 49 years equals that of biennial screening for women aged 50 to 74 years.
DESIGN: Comparative modeling study. DATA SOURCES: Surveillance, Epidemiology, and End Results program, Breast Cancer Surveillance Consortium, and medical literature. TARGET POPULATION: A contemporary cohort of women eligible for routine screening. TIME HORIZON: Lifetime. PERSPECTIVE: Societal. INTERVENTION: Mammography screening starting at age 40 versus 50 years with different screening methods (film, digital) and screening intervals (annual, biennial). BENEFITS: life-years gained, breast cancer deaths averted; harms: false-positive mammography findings; harm-benefit ratios: false-positive findings/life-years gained, false-positive findings/deaths averted. RESULTS OF BASE-CASE ANALYSIS: Screening average-risk women aged 50 to 74 years biennially yields the same false-positive findings/life-years gained as biennial screening with digital mammography starting at age 40 years for women with a 2-fold increased risk above average (median threshold RR, 1.9 [range across models, 1.5 to 4.4]). The threshold RRs are higher for annual screening with digital mammography (median, 4.3 [range, 3.3 to 10]) and when false-positive findings/deaths averted is used as an outcome measure instead of false-positive findings/life-years gained. The harm-benefit ratio for film mammography is more favorable than for digital mammography because film has a lower false-positive rate. RESULTS OF SENSITIVITY ANALYSIS: The threshold RRs changed slightly when a more comprehensive measure of harm was used and were relatively insensitive to lower adherence assumptions. LIMITATION: Risk was assumed to influence onset of disease without influencing screening performance.
CONCLUSION: Women aged 40 to 49 years with a 2-fold increased risk have similar harm-benefit ratios for biennial screening mammography as average-risk women aged 50 to 74 years. Threshold RRs required for favorable harm-benefit ratios vary by screening method, interval, and outcome measure. PRIMARY FUNDING SOURCE: National Cancer Institute.

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Year:  2012        PMID: 22547470      PMCID: PMC3520058          DOI: 10.7326/0003-4819-156-9-201205010-00002

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  45 in total

Review 1.  Screening mammography in women 40 to 49 years of age: a systematic review for the American College of Physicians.

Authors:  Katrina Armstrong; Elizabeth Moye; Sankey Williams; Jesse A Berlin; Eileen E Reynolds
Journal:  Ann Intern Med       Date:  2007-04-03       Impact factor: 25.391

2.  Screening mammography for women 40 to 49 years of age: a clinical practice guideline from the American College of Physicians.

Authors:  Amir Qaseem; Vincenza Snow; Katherine Sherif; Mark Aronson; Kevin B Weiss; Douglas K Owens
Journal:  Ann Intern Med       Date:  2007-04-03       Impact factor: 25.391

3.  The MISCAN-Fadia continuous tumor growth model for breast cancer.

Authors:  Sita Y G L Tan; Gerrit J van Oortmarssen; Harry J de Koning; Rob Boer; J Dik F Habbema
Journal:  J Natl Cancer Inst Monogr       Date:  2006

4.  A stochastic model for predicting the mortality of breast cancer.

Authors:  Sandra Lee; Marvin Zelen
Journal:  J Natl Cancer Inst Monogr       Date:  2006

5.  The Wisconsin Breast Cancer Epidemiology Simulation Model.

Authors:  Dennis G Fryback; Natasha K Stout; Marjorie A Rosenberg; Amy Trentham-Dietz; Vipat Kuruchittham; Patrick L Remington
Journal:  J Natl Cancer Inst Monogr       Date:  2006

6.  The SPECTRUM population model of the impact of screening and treatment on U.S. breast cancer trends from 1975 to 2000: principles and practice of the model methods.

Authors:  Jeanne Mandelblatt; Clyde B Schechter; William Lawrence; Bin Yi; Jennifer Cullen
Journal:  J Natl Cancer Inst Monogr       Date:  2006

7.  Breast cancer screening: evidence for false reassurance?

Authors:  Rianne de Gelder; Elisabeth van As; Madeleine M A Tilanus-Linthorst; Carina C M Bartels; Rob Boer; Gerrit Draisma; Harry J de Koning
Journal:  Int J Cancer       Date:  2008-08-01       Impact factor: 7.396

8.  Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model.

Authors:  Jeffrey A Tice; Steven R Cummings; Rebecca Smith-Bindman; Laura Ichikawa; William E Barlow; Karla Kerlikowske
Journal:  Ann Intern Med       Date:  2008-03-04       Impact factor: 25.391

Review 9.  Effects of study methods and biases on estimates of invasive breast cancer overdetection with mammography screening: a systematic review.

Authors:  Corné Biesheuvel; Alexandra Barratt; Kirsten Howard; Nehmat Houssami; Les Irwig
Journal:  Lancet Oncol       Date:  2007-12       Impact factor: 41.316

10.  Effect of mammographic screening from age 40 years on breast cancer mortality at 10 years' follow-up: a randomised controlled trial.

Authors:  Sue M Moss; Howard Cuckle; Andy Evans; Louise Johns; Michael Waller; Lynda Bobrow
Journal:  Lancet       Date:  2006-12-09       Impact factor: 79.321

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  46 in total

1.  Targeted screening of individuals at high risk for pancreatic cancer: results of a simulation model.

Authors:  Pari V Pandharipande; Curtis Heberle; Emily C Dowling; Chung Yin Kong; Angela Tramontano; Katherine E Perzan; William Brugge; Chin Hur
Journal:  Radiology       Date:  2014-11-12       Impact factor: 11.105

2.  Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies.

Authors:  Jeanne S Mandelblatt; Natasha K Stout; Clyde B Schechter; Jeroen J van den Broek; Diana L Miglioretti; Martin Krapcho; Amy Trentham-Dietz; Diego Munoz; Sandra J Lee; Donald A Berry; Nicolien T van Ravesteyn; Oguzhan Alagoz; Karla Kerlikowske; Anna N A Tosteson; Aimee M Near; Amanda Hoeffken; Yaojen Chang; Eveline A Heijnsdijk; Gary Chisholm; Xuelin Huang; Hui Huang; Mehmet Ali Ergun; Ronald Gangnon; Brian L Sprague; Sylvia Plevritis; Eric Feuer; Harry J de Koning; Kathleen A Cronin
Journal:  Ann Intern Med       Date:  2016-01-12       Impact factor: 25.391

3.  Aggregate cost of mammography screening in the United States: comparison of current practice and advocated guidelines.

Authors:  Cristina O'Donoghue; Martin Eklund; Elissa M Ozanne; Laura J Esserman
Journal:  Ann Intern Med       Date:  2014-02-04       Impact factor: 25.391

4.  Prediction of near-term breast cancer risk based on bilateral mammographic feature asymmetry.

Authors:  Maxine Tan; Bin Zheng; Pandiyarajan Ramalingam; David Gur
Journal:  Acad Radiol       Date:  2013-12       Impact factor: 3.173

5.  Breast Cancer Screening in Primary Care: A Call for Development and Validation of Patient-Oriented Shared Decision-Making Tools.

Authors:  Sarina Schrager; Elizabeth Burnside
Journal:  J Womens Health (Larchmt)       Date:  2018-05-14       Impact factor: 2.681

6.  Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49.

Authors:  Jeroen J van den Broek; Nicolien T van Ravesteyn; Jeanne S Mandelblatt; Hui Huang; Mehmet Ali Ergun; Elizabeth S Burnside; Cong Xu; Yisheng Li; Oguzhan Alagoz; Sandra J Lee; Natasha K Stout; Juhee Song; Amy Trentham-Dietz; Sylvia K Plevritis; Sue M Moss; Harry J de Koning
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

7.  Structure, Function, and Applications of the Georgetown-Einstein (GE) Breast Cancer Simulation Model.

Authors:  Clyde B Schechter; Aimee M Near; Jinani Jayasekera; Young Chandler; Jeanne S Mandelblatt
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

8.  The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update.

Authors:  Sandra J Lee; Xiaoxue Li; Hui Huang; Marvin Zelen
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

9.  Benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts.

Authors:  Brian L Sprague; Natasha K Stout; Clyde Schechter; Nicolien T van Ravesteyn; Mucahit Cevik; Oguzhan Alagoz; Christoph I Lee; Jeroen J van den Broek; Diana L Miglioretti; Jeanne S Mandelblatt; Harry J de Koning; Karla Kerlikowske; Constance D Lehman; Anna N A Tosteson
Journal:  Ann Intern Med       Date:  2015-02-03       Impact factor: 25.391

10.  Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia.

Authors:  Jeroen J van den Broek; Nicolien T van Ravesteyn; Eveline A Heijnsdijk; Harry J de Koning
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

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