Evidence map›Paper›PMID 26756606›Full record

ArticleAnnals of internal medicine2016

Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies.

Jeanne S Mandelblatt, Natasha K Stout, Clyde B Schechter, Jeroen J van den Broek, Diana L Miglioretti, Martin Krapcho, Amy Trentham-Dietz, Diego Munoz, Sandra J Lee, Donald A Berry and 18 more

Abstract read
In one paragraph

Article in Annals of internal medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 146 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
146citing papers in PubMed, 3 pooled it
–field-weighted citation impact
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

146 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Trial
  5. Clinical Decision Support with or without Shared Decision Making to Improve Preventive Cancer Care: A Cluster-Randomized Trial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022
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  16. A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025
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86 more citing papers are in PubMed but not listed here.

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

28 authors.

Jeanne S Mandelblatt
Natasha K Stout
Clyde B Schechter
Jeroen J van den Broek
Diana L Miglioretti
Martin Krapcho
Amy Trentham-Dietz
Diego Munoz
Sandra J Lee
Donald A Berry
Nicolien T van Ravesteyn
Oguzhan Alagoz
Karla Kerlikowske
Anna N A Tosteson
Aimee M Near
Amanda Hoeffken
Yaojen Chang
Eveline A Heijnsdijk
Gary Chisholm
Xuelin Huang
Hui Huang
Mehmet Ali Ergun
Ronald Gangnon
Brian L Sprague
Sylvia Plevritis
Eric Feuer
Harry J de Koning
Kathleen A Cronin

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
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
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
Vermont Screening Process Documentation UnitU54CA163303 · NCI · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · PI SPRAGUE, BRIAN L · 2011 to 2017
$6.4M
Analytical Investigation of Breast Cancer Progression: DCIS, OverdiagnosisR01CA164301 · NCI · DANA-FARBER CANCER INST · PI LEE, SANDRA J · 2012 to 2016
$1.9M
NCI NIH HHS P01 CA154292NCI NIH HHS P30 CA016672NCI NIH HHS P30 CA023108NCI NIH HHS R01 CA164301NCI NIH HHS U01 CA152958NCI NIH HHS U01 CA199218NCI NIH HHS U54 CA163303
6 · The paper itself

Abstract

backgroundControversy persists about optimal mammography screening strategies.

objectiveTo evaluate screening outcomes, taking into account advances in mammography and treatment of breast cancer.

designCollaboration of 6 simulation models using national data on incidence, digital mammography performance, treatment effects, and other-cause mortality.

settingUnited States. PATIENTS: Average-risk U.S. female population and subgroups with varying risk, breast density, or comorbidity.

interventionEight strategies differing by age at which screening starts (40, 45, or 50 years) and screening interval (annual, biennial, and hybrid [annual for women in their 40s and biennial thereafter]). All strategies assumed 100% adherence and stopped at age 74 years. MEASUREMENTS: Benefits (breast cancer-specific mortality reduction, breast cancer deaths averted, life-years, and quality-adjusted life-years); number of mammograms used; harms (false-positive results, benign biopsies, and overdiagnosis); and ratios of harms (or use) and benefits (efficiency) per 1000 screens.

resultsBiennial strategies were consistently the most efficient for average-risk women. Biennial screening from age 50 to 74 years avoided a median of 7 breast cancer deaths versus no screening; annual screening from age 40 to 74 years avoided an additional 3 deaths, but yielded 1988 more false-positive results and 11 more overdiagnoses per 1000 women screened. Annual screening from age 50 to 74 years was inefficient (similar benefits, but more harms than other strategies). For groups with a 2- to 4-fold increased risk, annual screening from age 40 years had similar harms and benefits as screening average-risk women biennially from 50 to 74 years. For groups with moderate or severe comorbidity, screening could stop at age 66 to 68 years. LIMITATION: Other imaging technologies, polygenic risk, and nonadherence were not considered.

conclusionBiennial screening for breast cancer is efficient for average-risk populations. Decisions about starting ages and intervals will depend on population characteristics and the decision makers' weight given to the harms and benefits of screening. PRIMARY FUNDING SOURCE: National Institutes of Health.

Indexed as

AdultAgedAge FactorsBreastBreast NeoplasmsComorbidityComputer SimulationEarly Detection of CancerFalse Positive ReactionsFemaleHumansIncidenceMammographyMass ScreeningMiddle AgedRisk Assessment

Identifiers

PMID26756606
PMCPMC5079106

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Registered trials

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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.