Diego Munoz1, Aimee M Near1, Nicolien T van Ravesteyn1, Sandra J Lee1, Clyde B Schechter1, Oguzhan Alagoz1, Donald A Berry1, Elizabeth S Burnside1, Yaojen Chang1, Gary Chisholm1, Harry J de Koning1, Mehmet Ali Ergun1, Eveline A M Heijnsdijk1, Hui Huang1, Natasha K Stout1, Brian L Sprague1, Amy Trentham-Dietz1, Jeanne S Mandelblatt1, Sylvia K Plevritis2. 1. Division of Biomedical Informatics Research (DM) and Department of Radiology (DM, SKP), School of Medicine, Stanford University, Stanford, CA (DM); Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC (AMN, YC, JSM); Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands (NTvR, HJdK, EAMH); Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, MA (SJL, HH); Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York (CBS); Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI (OA, MAE); Carbone Cancer Center, University of Wisconsin, Madison, WI (ESB, ATD); University of Texas M.D. Anderson Cancer Center, Houston, TX (DAB, GC); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA (NKS); Department of Surgery, College of Medicine, University of Vermont, VT (BLS). 2. Division of Biomedical Informatics Research (DM) and Department of Radiology (DM, SKP), School of Medicine, Stanford University, Stanford, CA (DM); Department of Oncology, Georgetown University Medical Center and Cancer Prevention and Control Program, Georgetown-Lombardi Comprehensive Cancer Center, Washington, DC (AMN, YC, JSM); Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands (NTvR, HJdK, EAMH); Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, MA (SJL, HH); Departments of Family and Social Medicine and Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York (CBS); Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI (OA, MAE); Carbone Cancer Center, University of Wisconsin, Madison, WI (ESB, ATD); University of Texas M.D. Anderson Cancer Center, Houston, TX (DAB, GC); Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA (NKS); Department of Surgery, College of Medicine, University of Vermont, VT (BLS). sylvia.plevritis@stanford.edu.
Abstract
BACKGROUND: Molecular characterization of breast cancer allows subtype-directed interventions. Estrogen receptor (ER) is the longest-established molecular marker. METHODS: We used six established population models with ER-specific input parameters on age-specific incidence, disease natural history, mammography characteristics, and treatment effects to quantify the impact of screening and adjuvant therapy on age-adjusted US breast cancer mortality by ER status from 1975 to 2000. Outcomes included stage-shifts and absolute and relative reductions in mortality; sensitivity analyses evaluated the impact of varying screening frequency or accuracy. RESULTS: In the year 2000, actual screening and adjuvant treatment reduced breast cancer mortality by a median of 17 per 100000 women (model range = 13-21) and 5 per 100000 women (model range = 3-6) for ER-positive and ER-negative cases, respectively, relative to no screening and no adjuvant treatment. For ER-positive cases, adjuvant treatment made a higher relative contribution to breast cancer mortality reduction than screening, whereas for ER-negative cases the relative contributions were similar for screening and adjuvant treatment. ER-negative cases were less likely to be screen-detected than ER-positive cases (35.1% vs 51.2%), but when screen-detected yielded a greater survival gain (five-year breast cancer survival = 35.6% vs 30.7%). Screening biennially would have captured a lower proportion of mortality reduction than annual screening for ER-negative vs ER-positive cases (model range = 80.2%-87.8% vs 85.7%-96.5%). CONCLUSION: As advances in risk assessment facilitate identification of women with increased risk of ER-negative breast cancer, additional mortality reductions could be realized through more frequent targeted screening, provided these benefits are balanced against screening harms.
BACKGROUND: Molecular characterization of breast cancer allows subtype-directed interventions. Estrogen receptor (ER) is the longest-established molecular marker. METHODS: We used six established population models with ER-specific input parameters on age-specific incidence, disease natural history, mammography characteristics, and treatment effects to quantify the impact of screening and adjuvant therapy on age-adjusted US breast cancer mortality by ER status from 1975 to 2000. Outcomes included stage-shifts and absolute and relative reductions in mortality; sensitivity analyses evaluated the impact of varying screening frequency or accuracy. RESULTS: In the year 2000, actual screening and adjuvant treatment reduced breast cancer mortality by a median of 17 per 100000 women (model range = 13-21) and 5 per 100000 women (model range = 3-6) for ER-positive and ER-negative cases, respectively, relative to no screening and no adjuvant treatment. For ER-positive cases, adjuvant treatment made a higher relative contribution to breast cancer mortality reduction than screening, whereas for ER-negative cases the relative contributions were similar for screening and adjuvant treatment. ER-negative cases were less likely to be screen-detected than ER-positive cases (35.1% vs 51.2%), but when screen-detected yielded a greater survival gain (five-year breast cancer survival = 35.6% vs 30.7%). Screening biennially would have captured a lower proportion of mortality reduction than annual screening for ER-negative vs ER-positive cases (model range = 80.2%-87.8% vs 85.7%-96.5%). CONCLUSION: As advances in risk assessment facilitate identification of women with increased risk of ER-negative breast cancer, additional mortality reductions could be realized through more frequent targeted screening, provided these benefits are balanced against screening harms.
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