Literature DB >> 25486550

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

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.   

Abstract

BACKGROUND: Many states have laws requiring mammography facilities to tell women with dense breasts and negative results on screening mammography to discuss supplemental screening tests with their providers. The most readily available supplemental screening method is ultrasonography, but little is known about its effectiveness.
OBJECTIVE: To evaluate the benefits, harms, and cost-effectiveness of supplemental ultrasonography screening for women with dense breasts.
DESIGN: Comparative modeling with 3 validated simulation models. DATA SOURCES: Surveillance, Epidemiology, and End Results Program; Breast Cancer Surveillance Consortium; and medical literature. TARGET POPULATION: Contemporary cohort of women eligible for routine screening. TIME HORIZON: Lifetime. PERSPECTIVE: Payer. INTERVENTION: Supplemental ultrasonography screening for women with dense breasts after a negative screening mammography result. OUTCOME MEASURES: Breast cancer deaths averted, quality-adjusted life-years (QALYs) gained, biopsies recommended after a false-positive ultrasonography result, and costs. RESULTS OF BASE-CASE ANALYSIS: Supplemental ultrasonography screening after a negative mammography result for women aged 50 to 74 years with heterogeneously or extremely dense breasts averted 0.36 additional breast cancer deaths (range across models, 0.14 to 0.75), gained 1.7 QALYs (range, 0.9 to 4.7), and resulted in 354 biopsy recommendations after a false-positive ultrasonography result (range, 345 to 421) per 1000 women with dense breasts compared with biennial screening by mammography alone. The cost-effectiveness ratio was $325,000 per QALY gained (range, $112,000 to $766,000). Supplemental ultrasonography screening for only women with extremely dense breasts cost $246,000 per QALY gained (range, $74,000 to $535,000). RESULTS OF SENSITIVITY ANALYSIS: The conclusions were not sensitive to ultrasonography performance characteristics, screening frequency, or starting age. LIMITATION: Provider costs for coordinating supplemental ultrasonography were not considered.
CONCLUSION: Supplemental ultrasonography screening for women with dense breasts would substantially increase costs while producing relatively small benefits. PRIMARY FUNDING SOURCE: National Cancer Institute.

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Year:  2015        PMID: 25486550      PMCID: PMC4314343          DOI: 10.7326/M14-0692

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


  38 in total

1.  Mammographic density and the risk and detection of breast cancer.

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Review 2.  A comparative review of CISNET breast models used to analyze U.S. breast cancer incidence and mortality trends.

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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
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4.  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

5.  Effect of age, breast density, and family history on the sensitivity of first screening mammography.

Authors:  K Kerlikowske; D Grady; J Barclay; E A Sickles; V Ernster
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6.  Effects of mammography screening under different screening schedules: model estimates of potential benefits and harms.

Authors:  Jeanne S Mandelblatt; Kathleen A Cronin; Stephanie Bailey; Donald A Berry; Harry J de Koning; Gerrit Draisma; Hui Huang; Sandra J Lee; Mark Munsell; Sylvia K Plevritis; Peter Ravdin; Clyde B Schechter; Bronislava Sigal; Michael A Stoto; Natasha K Stout; Nicolien T van Ravesteyn; John Venier; Marvin Zelen; Eric J Feuer
Journal:  Ann Intern Med       Date:  2009-11-17       Impact factor: 25.391

7.  Combined screening with ultrasound and mammography vs mammography alone in women at elevated risk of breast cancer.

Authors:  Wendie A Berg; Jeffrey D Blume; Jean B Cormack; Ellen B Mendelson; Daniel Lehrer; Marcela Böhm-Vélez; Etta D Pisano; Roberta A Jong; W Phil Evans; Marilyn J Morton; Mary C Mahoney; Linda Hovanessian Larsen; Richard G Barr; Dione M Farria; Helga S Marques; Karan Boparai
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Authors:  K Robin Yabroff; Elizabeth B Lamont; Angela Mariotto; Joan L Warren; Marie Topor; Angela Meekins; Martin L Brown
Journal:  J Natl Cancer Inst       Date:  2008-04-29       Impact factor: 13.506

9.  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 10.  Early detection of breast cancer: benefits and risks of supplemental breast ultrasound in asymptomatic women with mammographically dense breast tissue. A systematic review.

Authors:  Monika Nothacker; Volker Duda; Markus Hahn; Mathias Warm; Friedrich Degenhardt; Helmut Madjar; Susanne Weinbrenner; Ute-Susann Albert
Journal:  BMC Cancer       Date:  2009-09-20       Impact factor: 4.430

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  61 in total

1.  Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging.

Authors:  Said Pertuz; Elizabeth S McDonald; Susan P Weinstein; Emily F Conant; Despina Kontos
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2.  Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening.

Authors:  Amy Trentham-Dietz; Mehmet Ali Ergun; Oguzhan Alagoz; Natasha K Stout; Ronald E Gangnon; John M Hampton; Kim Dittus; Ted A James; Pamela M Vacek; Sally D Herschorn; Elizabeth S Burnside; Anna N A Tosteson; Donald L Weaver; Brian L Sprague
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3.  What Are the Public Health Effects of Dense Breast Notification Laws?

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4.  Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models.

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

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6.  Contribution of Breast Cancer to Overall Mortality for US Women.

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7.  A Qualitative Study of Spanish-Speakers' Experience with Dense Breast Notifications in a Massachusetts Safety-Net Hospital.

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

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9.  Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology.

Authors:  Jeroen J van den Broek; Nicolien T van Ravesteyn; Jeanne S Mandelblatt; Mucahit Cevik; Clyde B Schechter; Sandra J Lee; Hui Huang; Yisheng Li; Diego F Munoz; Sylvia K Plevritis; Harry J de Koning; Natasha K Stout; Marjolein van Ballegooijen
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

10.  The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update.

Authors:  Oguzhan Alagoz; Mehmet Ali Ergun; Mucahit Cevik; Brian L Sprague; Dennis G Fryback; Ronald E Gangnon; John M Hampton; Natasha K Stout; Amy Trentham-Dietz
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

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