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Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening

  • Epidemiology
  • Published:
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Abstract

Purpose

Due to limitations in the ability to identify non-progressive disease, ductal carcinoma in situ (DCIS) is usually managed similarly to localized invasive breast cancer. We used simulation modeling to evaluate the potential impact of a hypothetical test that identifies non-progressive DCIS.

Methods

A discrete-event model simulated a cohort of U.S. women undergoing digital screening mammography. All women diagnosed with DCIS underwent the hypothetical DCIS prognostic test. Women with test results indicating progressive DCIS received standard breast cancer treatment and a decrement to quality of life corresponding to the treatment. If the DCIS test indicated non-progressive DCIS, no treatment was received and women continued routine annual surveillance mammography. A range of test performance characteristics and prevalence of non-progressive disease were simulated. Analysis compared discounted quality-adjusted life years (QALYs) and costs for test scenarios to base-case scenarios without the test.

Results

Compared to the base case, a perfect prognostic test resulted in a 40% decrease in treatment costs, from $13,321 to $8005 USD per DCIS case. A perfect test produced 0.04 additional QALYs (16 days) for women diagnosed with DCIS, added to the base case of 5.88 QALYs per DCIS case. The results were sensitive to the performance characteristics of the prognostic test, the proportion of DCIS cases that were non-progressive in the model, and the frequency of mammography screening in the population.

Conclusion

A prognostic test that identifies non-progressive DCIS would substantially reduce treatment costs but result in only modest improvements in quality of life when averaged over all DCIS cases.

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Acknowledgements

We thank Berta Geller, Julie McGregor, Kathy Peck, Oyewale Shiyanbola, Dawn Pelkey, and Kathleen Howe for their advice, project management, and assistance with data for this project.

Funding

This work was supported by the National Cancer Institute at the National Institutes of Health for the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) Program (Grant No. U54 CA163303) and other projects (Grant Nos. U01 CA199218, P30 CA014520, and U54 CA163307). Data collection for model inputs from the Breast Cancer Surveillance Consortium (BCSC) was supported by the National Cancer Institute Grant P01 CA154292, contract HSN261201100031C, and Grant U54 CA163303. The collection of BCSC cancer and vital status data used in this study was supported in part by several state public health departments and cancer registries throughout the US. For a full description of these sources, please see: http://breastscreening.cancer.gov/work/acknowledgement.html.

Author information

Author notes

  1. Donald L. Weaver and Brian L. Sprague contributed equally as senior authors.

Authors and Affiliations

  1. Department of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut St, WARF Room 307, Madison, WI, 53726, USA

    Amy Trentham-Dietz, Ronald E. Gangnon & John M. Hampton

  2. University of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA

    Amy Trentham-Dietz, Oguzhan Alagoz, Ronald E. Gangnon, John M. Hampton & Elizabeth S. Burnside

  3. Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA

    Mehmet Ali Ergun & Oguzhan Alagoz

  4. Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA

    Natasha K. Stout

  5. Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA

    Ronald E. Gangnon

  6. Department of Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA

    Kim Dittus

  7. University of Vermont Cancer Center, Burlington, VT, USA

    Kim Dittus, Pamela M. Vacek, Sally D. Herschorn, Donald L. Weaver & Brian L. Sprague

  8. BreastCare Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

    Ted A. James

  9. Department of Medical Biostatistics, Larner College of Medicine, University of Vermont, Burlington, VT, USA

    Pamela M. Vacek

  10. Department of Radiology, Larner College of Medicine, University of Vermont, Burlington, VT, USA

    Sally D. Herschorn

  11. Department of Radiology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA

    Elizabeth S. Burnside

  12. The Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA

    Anna N. A. Tosteson

  13. Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA

    Donald L. Weaver

  14. Department of Surgery, Larner College of Medicine, University of Vermont, Burlington, VT, USA

    Brian L. Sprague

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  1. Amy Trentham-Dietz
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  2. Mehmet Ali Ergun
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Contributions

The authors are solely responsible for the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication.

Corresponding authors

Correspondence to Amy Trentham-Dietz or Brian L. Sprague.

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Conflicts of interest

Dr. Herschorn reports previously owning stock and currently serving as an unpaid advisor for Hologic. All other authors have no conflicts to disclose.

Ethical approval

The University of Wisconsin Health Sciences Human Subjects Committee determined that this study was exempt from review.

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Trentham-Dietz, A., Ergun, M.A., Alagoz, O. et al. Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening. Breast Cancer Res Treat 168, 229–239 (2018). https://doi.org/10.1007/s10549-017-4582-0

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