Evidence map›Paper›PMID 30816931›Full record

ArticleJAMA oncology2019

Association of Digital Breast Tomosynthesis vs Digital Mammography With Cancer Detection and Recall Rates by Age and Breast Density.

Emily F Conant, William E Barlow, Sally D Herschorn, Donald L Weaver, Elisabeth F Beaber, Anna N A Tosteson, Jennifer S Haas, Kathryn P Lowry, Natasha K Stout, Amy Trentham-Dietz and 6 more

Open access · bronzeAbstract readComparative Study
In one paragraph

Article in JAMA oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed, 4 pooled it
23.7field-weighted citation impact, top 1% 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

75 citing papers in PubMed, 4 syntheses or guidelines pooled it, 204 citations in OpenAlex.

  1. Guideline
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Digital Breast Tomosynthesis Use, Facility Mammography Volume, and Timely Follow-Up After Abnormal Screening Mammograms in Appalachia, United States.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2026
    Article
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  9. Breast Imaging Reporting and Data System v2025: Key Updates in Mammography.Journal of the Korean Society of Radiology · 2026
    Review
  10. Review
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  13. Patient, facility, and community determinants of timely follow up after abnormal screening mammograms in Appalachia: A multilevel analysis.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2026
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15 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 10 institutions in 1 country.

Emily F ConantDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
William E BarlowCancer Research and Biostatistics, Seattle, Washington.
Sally D HerschornDepartment of Radiology, University of Vermont, Burlington.
Donald L WeaverUniversity of Vermont Cancer Center, University of Vermont, Burlington.
Elisabeth F BeaberDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Anna N A TostesonDepartment of Community & Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Jennifer S HaasDivision of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts.
Kathryn P LowryDepartment of Radiology, University of Washington, Seattle.
Natasha K StoutDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.
Amy Trentham-DietzDepartment of Population Health Sciences and Carbone Cancer Center, University of Wisconsin, Madison.
Roberta M diFlorio-AlexanderDepartment of Radiology, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire.
Christopher I LiTranslational Research Program, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Mitchell D SchnallDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
Tracy OnegaNorris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire.
Brian L SpragueDepartment of Radiology, University of Vermont, Burlington.
Population-based Research Optimizing Screening Through Personalized Regimen (PROSPR) Consortium
University of Vermont · USDartmouth College · USFred Hutch Cancer Center · USUniversity of Pennsylvania · USBrigham and Women's Hospital · USCancer Research And Biostatistics · USDartmouth–Hitchcock Medical Center · USHarvard University · USUniversity of Washington · USUniversity of Wisconsin Carbone Cancer Center · US

Funding

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
PROSPR Statistical Coordinating Center (PSCC)U01CA163304 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BARLOW, WILLIAM E, THORNQUIST, MARK · 2011 to 2017
$7.5M
Penn Center for Innovation in Personalized Breast ScreeningU54CA163313 · NCI · UNIVERSITY OF PENNSYLVANIA · PI ARMSTRONG, KATRINA, SCHNALL, MITCHELL D. · 2011 to 2017
$7.4M
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
NCI NIH HHS P01 CA154292NCI NIH HHS P30 CA023108NCI NIH HHS U01 CA163304NCI NIH HHS U54 CA163303NCI NIH HHS U54 CA163307NCI NIH HHS U54 CA163313
6 · The paper itself

Abstract

importanceBreast cancer screening examinations using digital breast tomosynthesis (DBT) has been shown to be associated with decreased false-positive test results and increased breast cancer detection compared with digital mammography (DM). Little is known regarding the size and stage of breast cancer types detected and their association with age and breast density.

objectiveTo determine whether screening examinations using DBT detect breast cancers that are associated with an improved prognosis and to compare the detection rates by patient age and breast density. DESIGN, SETTING, AND

participantsThis retrospective analysis of prospective cohort data from 3 research centers in the Population-based Research Optimizing Screening Through Personalized Regimens (PROSPR) consortium included data of women aged 40 to 74 years who underwent screening examinations using DM and DBT from January 1, 2011, through September 30, 2014. Statistical analysis was performed from November 8, 2017, to August 14, 2018. EXPOSURES: Use of DBT as a supplement to DM at breast cancer screening examination. MAIN OUTCOMES AND MEASURES: Recall rate, cancer detection rate, positive predictive value, biopsy rate, and distribution of invasive cancer subtypes.

resultsAmong 96 269 women (mean [SD] patient age for all examinations, 55.9 [9.0] years), patient age was 56.4 (9.0) years for DM and 54.6 (8.9) years for DBT. Of 180 340 breast cancer screening examinations, 129 369 examinations (71.7%) used DM and 50 971 examinations (28.3%) used DBT. Screening examination with DBT (73 of 99 women [73.7%]) was associated with the detection of smaller, more often node-negative, HER2-negative, invasive cancers compared with DM (276 of 422 women [65.4%]). Screening examination with DBT was also associated with lower recall (odds ratio, 0.64; 95% CI, 0.57-0.72; P < .001) and higher cancer detection (odds ratio, 1.41; 95% CI, 1.05-1.89; P = .02) compared with DM for all age groups even when stratified by breast density. The largest increase in cancer detection rate and the greatest shift toward smaller, node-negative invasive cancers detected with DBT was for women aged 40 to 49 years. For women aged 40 to 49 years with nondense breasts, the cancer detection rate for examinations using DBT was 1.70 per 1000 women higher compared with the rate using DM; for women with dense breasts, the cancer detection rate was 2.27 per 1000 women higher for DBT. For these younger women, screening with DBT was associated with only 7 of 28 breast cancers (25.0%) categorized as poor prognosis compared with 19 of 47 breast cancers (40.4%) when screening with DM. CONCLUSIONS AND RELEVANCE: The findings suggest that screening with DBT is associated with increased specificity and an increased proportion of breast cancers detected with better prognosis compared with DM. In the subgroup of women aged 40 to 49 years, routine DBT screening may have a favorable risk-benefit ratio.

Indexed as

Breast DensityAdultAgedBreastBreast NeoplasmsEarly Detection of CancerFemaleHumansMammographyMass ScreeningMiddle AgedSensitivity and Specificity

Identifiers

PMID30816931
PMCPMC6512257
OpenAlexW2918601053

What OpenQuestion holds

Textmetadata
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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.