Overdiagnosis from screening can result from the detection of indolent preclinical cancer or progressive preclinical cancer where the person would have died of an unrelated cause before clinical diagnosis. This article uses statistical modeling to account for both types of overdiagnosis in estimating the rate of screen-detected breast cancer that is overdiagnosed.
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.
References
- 1.
Nelson HD ,Pappas M ,Cantor A ,et al . Harms of breast cancer screening: systematic review to update the 2009 U.S. Preventive Services Task Force recommendation. Ann Intern Med. 2016;164:256-67. [PMID: 26756737] doi:10.7326/M15-0970 LinkGoogle Scholar - 2.
Siu AL; U.S. Preventive Services Task Force . Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2016;164:279-96. [PMID: 26757170] doi:10.7326/M15-2886 LinkGoogle Scholar - 3.
Oeffinger KC ,Fontham ET ,Etzioni R ,et al ;American Cancer Society . Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA. 2015;314:1599-614. [PMID: 26501536] doi:10.1001/jama.2015.12783 CrossrefMedlineGoogle Scholar - 4.
Biesheuvel C ,Barratt A ,Howard K ,et al . Effects of study methods and biases on estimates of invasive breast cancer overdetection with mammography screening: a systematic review. Lancet Oncol. 2007;8:1129-1138. [PMID: 18054882] doi:10.1016/S1470-2045(07)70380-7 CrossrefMedlineGoogle Scholar - 5.
Etzioni R ,Gulati R ,Mallinger L ,et al . Influence of study features and methods on overdiagnosis estimates in breast and prostate cancer screening. Ann Intern Med. 2013;158:831-8. [PMID: 23732716] doi:10.7326/0003-4819-158-11-201306040-00008 LinkGoogle Scholar - 6.
Gulati R ,Feuer EJ ,Etzioni R . Conditions for valid empirical estimates of cancer overdiagnosis in randomized trials and population studies. Am J Epidemiol. 2016;184:140-7. [PMID: 27358266] doi:10.1093/aje/kwv342 CrossrefMedlineGoogle Scholar - 7.
Duffy SW ,Parmar D . Overdiagnosis in breast cancer screening: the importance of length of observation period and lead time. Breast Cancer Res. 2013;15:R41. [PMID: 23680223] doi:10.1186/bcr3427 CrossrefMedlineGoogle Scholar - 8.
Shen Y ,Zelen M . Screening sensitivity and sojourn time from breast cancer early detection clinical trials: mammograms and physical examinations. J Clin Oncol. 2001;19:3490-9. [PMID: 11481355] CrossrefMedlineGoogle Scholar - 9.
Ryser MD ,Gulati R ,Eisenberg MC ,et al . Identification of the fraction of indolent tumors and associated overdiagnosis in breast cancer screening trials. Am J Epidemiol. 2019;188:197-205. [PMID: 30325415] doi:10.1093/aje/kwy214 CrossrefMedlineGoogle Scholar - 10. Shen Y, Zelen M. Parametric estimation procedures for screening programmes: stable and nonstable disease models for multimodality case finding. Biometrika. 1999;86:503-515. doi:10.1093/biomet/86.3.503 CrossrefGoogle Scholar
- 11.
Day NE ,Walter SD . Simplified models of screening for chronic disease: estimation procedures from mass screening programmes. Biometrics. 1984;40:1-14. [PMID: 6733223] CrossrefMedlineGoogle Scholar - 12.
Baker SG ,Prorok PC ,Kramer BS . Lead time and overdiagnosis [Editorial]. J Natl Cancer Inst. 2014;106. [PMID: 25362702] doi:10.1093/jnci/dju346 CrossrefMedlineGoogle Scholar - 13.
Zahl PH ,Jørgensen KJ ,Gøtzsche PC . Lead-time models should not be used to estimate overdiagnosis in cancer screening. J Gen Intern Med. 2014;29:1283-6. [PMID: 24590736] doi:10.1007/s11606-014-2812-2 CrossrefMedlineGoogle Scholar - 14.
Ryser MD ,Weaver DL ,Zhao F ,et al . Cancer outcomes in DCIS patients without locoregional treatment. J Natl Cancer Inst. 2019;111:952-960. [PMID: 30759222] doi:10.1093/jnci/djy220 CrossrefMedlineGoogle Scholar - 15.
van Seijen M, Lips EH, Thompson AM, et al; PRECISION team. Ductal carcinoma in situ: to treat or not to treat, that is the question. Br J Cancer. 2019;121:285-292. [PMID: 31285590] doi:10.1038/s41416-019-0478-6 CrossrefMedlineGoogle Scholar - 16.
Raue A ,Kreutz C ,Maiwald T ,et al . Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics. 2009;25:1923-9. [PMID: 19505944] doi:10.1093/bioinformatics/btp358 CrossrefMedlineGoogle Scholar - 17.
Brouwer AF ,Meza R ,Eisenberg MC . Parameter estimation for multistage clonal expansion models from cancer incidence data: a practical identifiability analysis. PLoS Comput Biol. 2017;13:e1005431. [PMID: 28288156] doi:10.1371/journal.pcbi.1005431 CrossrefMedlineGoogle Scholar - 18.
Alagoz O ,Ergun MA ,Cevik M ,et al . The University of Wisconsin Breast Cancer Epidemiology Simulation Model: an update. Med Decis Making. 2018;38:99S-111S. [PMID: 29554470] doi:10.1177/0272989X17711927 CrossrefMedlineGoogle Scholar - 19.
Wu WY ,Törnberg S ,Elfström KM ,et al . Overdiagnosis in the population-based organized breast cancer screening program estimated by a non-homogeneous multi-state model: a cohort study using individual data with long-term follow-up. Breast Cancer Res. 2018;20:153. [PMID: 30558679] doi:10.1186/s13058-018-1082-z CrossrefMedlineGoogle Scholar - 20.
Seigneurin A ,François O ,Labarère J ,et al . Overdiagnosis from non-progressive cancer detected by screening mammography: stochastic simulation study with calibration to population based registry data. BMJ. 2011;343:d7017. [PMID: 22113564] doi:10.1136/bmj.d7017 CrossrefMedlineGoogle Scholar - 21.
Ballard-Barbash R ,Taplin SH ,Yankaskas BC ,et al . Breast Cancer Surveillance Consortium: a national mammography screening and outcomes database. AJR Am J Roentgenol. 1997;169:1001-8. [PMID: 9308451] CrossrefMedlineGoogle Scholar - 22.
Lehman CD ,Arao RF ,Sprague BL ,et al . National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology. 2017;283:49-58. [PMID: 27918707] doi:10.1148/radiol.2016161174 CrossrefMedlineGoogle Scholar - 23. Breast Cancer Surveillance Consortium. Standard definitions. Accessed at www.bcsc-research.org/data/bcsc_standard_definitions on 23 December 2021. Google Scholar
- 24.
Sprague BL ,Miglioretti DL ,Lee CI ,et al . New mammography screening performance metrics based on the entire screening episode. Cancer. 2020;126:3289-3296. [PMID: 32374471] doi:10.1002/cncr.32939 CrossrefMedlineGoogle Scholar - 25.
Gangnon RE ,Stout NK ,Alagoz O ,et al . Contribution of breast cancer to overall mortality for US women. Med Decis Making. 2018;38:24S-31S. [PMID: 29554467] doi:10.1177/0272989X17717981 CrossrefMedlineGoogle Scholar - 26.
Baines CJ ,To T ,Miller AB . Revised estimates of overdiagnosis from the Canadian National Breast Screening Study. Prev Med. 2016;90:66-71. [PMID: 27374944] doi:10.1016/j.ypmed.2016.06.033 CrossrefMedlineGoogle Scholar - 27.
Bleyer A ,Welch HG . Effect of three decades of screening mammography on breast-cancer incidence. N Engl J Med. 2012;367:1998-2005. [PMID: 23171096] doi:10.1056/NEJMoa1206809 CrossrefMedlineGoogle Scholar - 28.
Etzioni R ,Xia J ,Hubbard R ,et al . A reality check for overdiagnosis estimates associated with breast cancer screening. J Natl Cancer Inst. 2014;106. [PMID: 25362701] doi:10.1093/jnci/dju315 CrossrefMedlineGoogle Scholar - 29.
de Gelder R, Fracheboud J, Heijnsdijk EA, et al. Digital mammography screening: weighing reduced mortality against increased overdiagnosis. Prev Med. 2011;53:134-40. [PMID: 21718717] doi:10.1016/j.ypmed.2011.06.009 CrossrefMedlineGoogle Scholar - 30.
Gunsoy NB ,Garcia-Closas M ,Moss SM . Estimating breast cancer mortality reduction and overdiagnosis due to screening for different strategies in the United Kingdom. Br J Cancer. 2014;110:2412-9. [PMID: 24762956] doi:10.1038/bjc.2014.206 CrossrefMedlineGoogle Scholar - 31.
Bulliard JL ,Beau AB ,Njor S ,et al . Breast cancer screening and overdiagnosis. Int J Cancer. 2021. [PMID: 33872390] doi:10.1002/ijc.33602 CrossrefMedlineGoogle Scholar - 32.
Fryback DG ,Stout NK ,Rosenberg MA ,et al . The Wisconsin Breast Cancer Epidemiology Simulation Model. J Natl Cancer Inst Monogr. 2006:37-47. [PMID: 17032893] CrossrefMedlineGoogle Scholar - 33.
Schechter CB ,Near AM ,Jayasekera J ,et al . Structure, function, and applications of the Georgetown–Einstein (GE) Breast Cancer Simulation Model. Med Decis Making. 2018;38:66S-77S. [PMID: 29554462] doi:10.1177/0272989X17698685 CrossrefMedlineGoogle Scholar - 34.
van den Broek JJ, van Ravesteyn NT, Heijnsdijk EA, et al. Simulating the impact of risk-based screening and treatment on breast cancer outcomes with MISCAN-Fadia. Med Decis Making. 2018;38:54S-65S. [PMID: 29554469] doi:10.1177/0272989X17711928 CrossrefMedlineGoogle Scholar - 35.
Lee SJ ,Li X ,Huang H ,et al . The Dana-Farber CISNET model for breast cancer screening strategies: an update. Med Decis Making. 2018;38:44S-53S. [PMID: 29554465] doi:10.1177/0272989X17741634 CrossrefMedlineGoogle Scholar - 36.
Erbas B ,Provenzano E ,Armes J ,et al . The natural history of ductal carcinoma in situ of the breast: a review. Breast Cancer Res Treat. 2006;97:135-44. [PMID: 16319971] CrossrefMedlineGoogle Scholar - 37.
Spiegelhalter DJ ,Riesch H . Don't know, can't know: embracing deeper uncertainties when analysing risks. Philos Trans A Math Phys Eng Sci. 2011;369:4730-50. [PMID: 22042895] doi:10.1098/rsta.2011.0163 CrossrefMedlineGoogle Scholar - 38.
Weedon-Fekjaer H ,Vatten LJ ,Aalen OO ,et al . Estimating mean sojourn time and screening test sensitivity in breast cancer mammography screening: new results. J Med Screen. 2005;12:172-8. [PMID: 16417693] CrossrefMedlineGoogle Scholar - 39.
Huang S ,Houssami N ,Brennan M ,et al . The impact of mandatory mammographic breast density notification on supplemental screening practice in the United States: a systematic review. Breast Cancer Res Treat. 2021;187:11-30. [PMID: 33774734] doi:10.1007/s10549-021-06203-w CrossrefMedlineGoogle Scholar - 40. National Cancer Institute. SEER*Explorer. Accessed at https://seer.cancer.gov/explorer/ on 23 December 2021. Google Scholar
Author, Article, and Disclosure Information
Marc D. Ryser,
Department of Population Health Sciences, Duke University Medical Center, and Department of Mathematics, Duke University, Durham, North Carolina (M.D.R.)
Center for Early Detection Advanced Research, Knight Cancer Institute, Oregon Health Sciences University, Portland, Oregon (J.L.)
Department of Biostatistics, University of Washington, Seattle, Washington (L.Y.I.)
Kaiser Permanente Washington Health Research Institute, Seattle, Washington (E.S.O.)
Department of Economics, Applied Statistics, and International Business, New Mexico State University, Las Cruces, New Mexico (C.G.)
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.)
Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland (J.B.)
Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan (A.F.B.)
Department of Surgery, Duke University Medical Center, Durham, North Carolina (E.S.H.)
Program in Biostatistics, Fred Hutchinson Cancer Research Center, Seattle, Washington (R.B.E.).
Disclaimer: The statements in this report are solely those of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute and its Board of Governors or Methodology Committee, National Cancer Institute, or National Institutes of Health.
Acknowledgment: The authors thank the participating women, mammography facilities, and radiologists for the data they have provided.
Financial Support: By grants R00CA207872, R01CA242735, and R01CA192492 from the National Institutes of Health. Data collection was additionally supported by the BCSC (www.bcsc-research.org/) with funding from the National Cancer Institute (grants P01CA154292 and U54CA163303) and the Patient-Centered Outcomes Research Institute (grant PCS-1504-30370). Cancer and vital status data collection were supported in part by several U.S. state public health departments and cancer registries (www.bcsc-research.org/about/work-acknowledgement).
Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M21-3577.
Reproducible Research Statement: Study protocol and data set: Not available. Statistical code: Available on GitHub.com/mdryser/BCSC_Overdiagnosis.
Corresponding Author: Marc D. Ryser, PhD, Duke University, 215 Morris Street, Durham, NC 27701; e-mail, marc.
Author Contributions: Conception and design: R.B. Etzioni, J. Lange, M.D. Ryser.
Analysis and interpretation of the data: J. Bulliard, R.B. Etzioni, J. Lange, D.L. Miglioretti, M.D. Ryser.
Drafting of the article: R.B. Etzioni, E.S. Hwang, M.D. Ryser.
Critical revision of the article for important intellectual content: A.F. Brouwer, J. Bulliard, R.B. Etzioni, E.S. Hwang, J. Lange, D.L. Miglioretti, M.D. Ryser.
Final approval of the article: A.F. Brouwer, J. Bulliard, R.B. Etzioni, C. Gard, E.S. Hwang, L. Inoue, J. Lange, D.L. Miglioretti, E.S. O’Meara, M.D. Ryser.
Statistical expertise: R.B. Etzioni, L. Inoue, J. Lange, D.L. Miglioretti, M.D. Ryser.
Obtaining of funding: R.B. Etzioni, D.L. Miglioretti, M.D. Ryser.
Administrative, technical, or logistic support: E.S. Hwang.
Collection and assembly of data: R.B. Etzioni, C. Gard, J. Lange, D.L. Miglioretti, E.S. O’Meara, M.D. Ryser.
This article was published at Annals.org on 1 March 2022.
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