Evidence map›Paper›PMID 29554468›Full record

ArticleMedical decision making : an international journal of the Society for Medical Decision Making2018

Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49.

Jeroen J van den Broek, Nicolien T van Ravesteyn, Jeanne S Mandelblatt, Hui Huang, Mehmet Ali Ergun, Elizabeth S Burnside, Cong Xu, Yisheng Li, Oguzhan Alagoz, Sandra J Lee and 6 more

Abstract readComparative StudyValidation Study
In one paragraph

Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 20% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

  1. Pooled it
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  5. Review
  6. Article
  7. The OncoSim-Breast Cancer Microsimulation Model.Current oncology (Toronto, Ont.) · 2022
    Article
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  11. Review
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  14. Article
  15. A Bayesian Simulation Model for Breast Cancer Screening, Incidence, Treatment, and Mortality.Medical decision making : an international journal of the Society for Medical Decision Making · 2018
    Article
  16. Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2018
    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

16 authors at 9 institutions in 3 countries.

Jeroen J van den BroekDepartment of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands.
Nicolien T van RavesteynDepartment of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands.
Jeanne S MandelblattDepartment of Oncology, Georgetown-Lombardi Comprehensive Cancer Center, Georgetown University School of Medicine, Washington DC, USA.
Hui HuangDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA.
Mehmet Ali ErgunDepartment of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Elizabeth S BurnsideDepartment of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Cong XuDepartment of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
Yisheng LiDepartment of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
Oguzhan AlagozDepartment of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Sandra J LeeDepartment of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Medical School Boston, Boston, MA, USA.
Natasha K StoutDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.
Juhee SongDepartment of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
Amy Trentham-DietzDepartment of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Sylvia K PlevritisDepartment of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
Sue M MossDepartment of cancer prevention, Wolfson Institute, Queen Mary University of London, London, UK.
Harry J de KoningDepartment of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands.
University of Wisconsin–Madison · USErasmus MC · NLStanford University · USThe University of Texas MD Anderson Cancer Center · USDana-Farber Cancer Institute · USGeorgetown University · USHarvard Pilgrim Health Care · USHarvard University · USQueen Mary University of London · GB

Funding

UW COMPREHENSIVE CANCER CENTER SUPPORTP30CA014520 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI AMY TRENTHAM-DIETZ · 1985 to 2026
$142.6M
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
Comparative Modeling: Informing Breast Cancer Control Practice & PolicyU01CA152958 · NCI · GEORGETOWN UNIVERSITY · PI BERRY, DONALD A, DE KONING, HARRY J · 2010 to 2015
$7.6M
Informatics Techniques to Optimize Mammography for Aging PopulationsR01CA165229 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI BURNSIDE, ELIZABETH S · 2012 to 2016
$1.7M
Mentoring And Research In Patient-Oriented Breast Cancer DiagnosisK24CA194251 · NCI · UNIVERSITY OF WISCONSIN-MADISON · PI BURNSIDE, ELIZABETH S · 2015 to 2019
$963k
Department of Health 10/57/55NCI NIH HHS K24 CA194251NCI NIH HHS P30 CA014520NCI NIH HHS R01 CA165229NCI NIH HHS U01 CA152958NCI NIH HHS U01 CA199218
6 · The paper itself

Abstract

backgroundThe UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results.

methodsFive established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial.

resultsThe models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial.

conclusionsThe models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.

Indexed as

AdultAntineoplastic AgentsBreast NeoplasmsCarcinoma, Intraductal, NoninfiltratingComputer SimulationFemaleHumansIncidenceMammographyMiddle AgedModels, StatisticalMortalityNeoplasm InvasivenessRandomized Controlled Trials as TopicRisk AssessmentUnited KingdomAntineoplastic Agentsbreast cancer modelsCISNETexternal validationmammography trial simulation

Identifiers

PMID29554468
PMCPMC5862071
OpenAlexW2792336645

What OpenQuestion holds

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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.