학술논문
Collaborative Modeling to Compare Different Breast Cancer Screening Strategies: A Decision Analysis for the US Preventive Services Task Force.
Document Type
Academic Journal
Author
Trentham-Dietz A; Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison.; Chapman CH; Department of Radiation Oncology and Center for Innovations in Quality, Safety, and Effectiveness, Baylor College of Medicine, Houston, Texas.; Jayasekera J; 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.; Lowry KP; University of Washington School of Medicine, Seattle.; Heckman-Stoddard BM; Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.; Hampton JM; Department of Population Health Sciences and Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison.; Caswell-Jin JL; Department of Medicine, Stanford University School of Medicine, Stanford, California.; Gangnon RE; 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.; Lu Y; Stanford University, Stanford, California.; Huang H; Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.; Stein S; Harvard Pilgrim Health Care Institute, Boston, Massachusetts.; Sun L; Stanford University, Stanford, California.; Gil Quessep EJ; Erasmus MC-University Medical Center, Rotterdam, the Netherlands.; Yang Y; University of Texas MD Anderson Cancer Center, Houston.; Lu Y; Department of Industrial and Systems Engineering and Carbone Cancer Center, University of Wisconsin-Madison.; Song J; University of Texas MD Anderson Cancer Center, Houston.; Muñoz DF; Stanford University, Stanford, California.; Li Y; University of Texas MD Anderson Cancer Center, Houston.; Kurian AW; Departments of Medicine and Epidemiology and Population Health, Stanford University, Stanford, California.; Kerlikowske K; Departments of Medicine and Epidemiology and Biostatistics, University of California San Francisco.; O'Meara ES; Kaiser Permanente Washington Health Research Institute, Seattle, Washington.; Sprague BL; Department of Surgery, University of Vermont, Burlington.; Tosteson ANA; 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.; Feuer EJ; Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.; Berry D; University of Texas MD Anderson Cancer Center, Houston.; Plevritis SK; Departments of Biomedical Data Science and Radiology, Stanford University, Stanford, California.; Huang X; University of Texas MD Anderson Cancer Center, Houston.; de Koning HJ; Erasmus MC-University Medical Center, Rotterdam, the Netherlands.; van Ravesteyn NT; Erasmus MC-University Medical Center, Rotterdam, the Netherlands.; Lee SJ; Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts.; Alagoz O; Department of Industrial and Systems Engineering and Carbone Cancer Center, University of Wisconsin-Madison.; Schechter CB; Albert Einstein College of Medicine, Bronx, New York.; Stout NK; Harvard Pilgrim Health Care Institute, Boston, Massachusetts.; Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.; Miglioretti DL; Kaiser Permanente Washington Health Research Institute, Seattle, Washington.; Department of Public Health Sciences, University of California Davis.; Mandelblatt JS; 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.
Source
Publisher: American Medical Association Country of Publication: United States NLM ID: 7501160 Publication Model: Print Cited Medium: Internet ISSN: 1538-3598 (Electronic) Linking ISSN: 00987484 NLM ISO Abbreviation: JAMA Subsets: MEDLINE
Subject
Language
English
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.
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.