Evidence map›Paper›PMID 29185118›Full record

ArticleBreast cancer research and treatment2018

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

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 and 4 more

Open access · greenAbstract readComparative StudyEvaluation Study
In one paragraph

Article in Breast cancer research and treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.1field-weighted citation impact, top 46% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 5 institutions in 1 country.

Amy Trentham-DietzDepartment of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut St, WARF Room 307, Madison, WI, 53726, USA. trentham@wisc.edu.ORCID http://orcid.org/0000-0002-5971-4660
Mehmet Ali ErgunDepartment of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Oguzhan AlagozUniversity of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA.
Natasha K StoutDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.
Ronald E GangnonDepartment of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut St, WARF Room 307, Madison, WI, 53726, USA.
John M HamptonDepartment of Population Health Sciences, School of Medicine and Public Health, University of Wisconsin-Madison, 610 Walnut St, WARF Room 307, Madison, WI, 53726, USA.
Kim DittusDepartment of Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
Ted A JamesBreastCare Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Pamela M VacekUniversity of Vermont Cancer Center, Burlington, VT, USA.
Sally D HerschornUniversity of Vermont Cancer Center, Burlington, VT, USA.
Elizabeth S BurnsideUniversity of Wisconsin Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA.
Anna N A TostesonThe Dartmouth Institute for Health Policy and Clinical Practice and Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA.
Donald L WeaverUniversity of Vermont Cancer Center, Burlington, VT, USA.
Brian L SpragueUniversity of Vermont Cancer Center, Burlington, VT, USA. brian.sprague@uvm.edu.
University of Wisconsin–Madison · USUniversity of Vermont · USBeth Israel Deaconess Medical Center · USDartmouth Institute for Health Policy and Clinical Practice · USHarvard University · US

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI AMY TRENTHAM-DIETZ · 1985 to 2026
$142.6M
Translational Engineering in Cancer (TEC)P30CA023108 · NCI · DARTMOUTH COLLEGE · PI Steven D Leach · 1985 to 2026
$91.3M
Risk-based Imaging Strategies to Improve Breast Cancer Surveillance OutcomesP01CA154292 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Diana L Miglioretti · 2011 to 2026
$52.9M
Institutional Clinical and Translational Science AwardUL1TR000427 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI DREZNER, MARC KENNETH · 2012 to 2016
$32.2M
Comparative Modeling: Informing Breast Cancer Control Practice and PolicyU01CA199218 · NCI · GEORGETOWN UNIVERSITY · PI BERRY, DONALD A, DE KONING, HARRY J · 2015 to 2019
$9.0M
Vermont Screening Process Documentation UnitU54CA163303 · NCI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI SPRAGUE, BRIAN L · 2011 to 2017
$6.4M
Promoting personalized decisions for breast cancer screening for women AND theirU54CA163307 · NCI · DARTMOUTH COLLEGE · PI HAAS, JENNIFER S, ONEGA, TRACY · 2011 to 2017
$6.1M
Informatics Techniques to Optimize Mammography for Aging PopulationsR01CA165229 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI BURNSIDE, ELIZABETH S · 2012 to 2016
$1.7M
National Cancer Institute HSN261201100031CNational Cancer Institute P01 CA154292National Cancer Institute P30 CA014520National Cancer Institute U01 CA199218National Cancer Institute U54 CA163303National Cancer Institute U54CA163307NCATS NIH HHS UL1 TR000427NCI NIH HHS P01 CA154292NCI NIH HHS P30 CA014520NCI NIH HHS P30 CA023108NCI NIH HHS R01 CA165229NCI NIH HHS U01 CA199218NCI NIH HHS U54 CA163303NCI NIH HHS U54 CA163307
6 · The paper itself

Abstract

purposeDue 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.

methodsA 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.

resultsCompared 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.

conclusionA 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.

Indexed as

AdultAgedBiomarkers, TumorBreast NeoplasmsCarcinoma, Intraductal, NoninfiltratingCohort StudiesCost-Benefit AnalysisDisease ProgressionEarly Detection of CancerFemaleGenetic TestingHumansMammographyMiddle AgedModels, BiologicalNeoplasm StagingBiomarkers, TumorBreast cancerCostsDuctal carcinoma in situIndolent diseasePrognosisQuality-adjusted life years

Identifiers

PMID29185118
PMCPMC5963699
OpenAlexW2768730102

What OpenQuestion holds

Textmetadata
LicenceTDM
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.