Evidence map›Paper›PMID 27598536›Full record

ArticleRadiology2017

Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS.

Abra M Jeffers, Weiva Sieh, Jafi A Lipson, Joseph H Rothstein, Valerie McGuire, Alice S Whittemore, Daniel L Rubin

2 registry-linked trialsOpen access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 1 pooled it
6.9field-weighted citation impact, top 3% 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.

NCT05021055 unknown statusnot on this mapstarted 2021, after this paper: background citation

Multicentric Study for External Validation of a Deep Learning Model for Mammographic Breast Density Categorization

TypeobservationalSponsorHospital Italiano de Buenos AiresRan2021 to 2022Enrolled277ConditionsBreast Cancer
NCT07415499 recruitingnot on this mapstarted 2026, after this paper: background citation

Retrospective Study on Patients Undergoing Mammographic Screening to Evaluate the Impact of Breast Density on Breast Cancer Diagnosis

TypeobservationalSponsorAzienda Sanitaria Locale CN2 Alba-BraRan2026 to 2026Enrolled3,300ConditionsBreast Cancer Screening, Breast Cancer Screening and DiagnosisArmsNo intervention is administered.
3 · Its place in the literature

Who cites it

45 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Breast MRI to Screen Women With Extremely Dense Breasts.Journal of magnetic resonance imaging : JMRI · 2025
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
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  12. Breast Density Analysis Using Digital Breast Tomosynthesis.bioRxiv : the preprint server for biology · 2023
    Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Abra M JeffersFrom the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Weiva SiehFrom the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Jafi A LipsonFrom the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Joseph H RothsteinFrom the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Valerie McGuireFrom the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Alice S WhittemoreFrom the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Daniel L RubinFrom the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Stanford University · US

Funding

Genome-Wide Association Study of Mammographic DensityR01CA166827 · NCI · STANFORD UNIVERSITY · PI HABEL, LAUREL A, SIEH, WEIVA · 2012 to 2016
$2.3M
NCI NIH HHS R01 CA166827
6 · The paper itself

Abstract

Purpose To compare three metrics of breast density on full-field digital mammographic (FFDM) images as predictors of future breast cancer risk. Materials and Methods This institutional review board-approved study included 125 women with invasive breast cancer and 274 age- and race-matched control subjects who underwent screening FFDM during 2004-2013 and provided informed consent. The percentage of density and dense area were assessed semiautomatically with software (Cumulus 4.0; University of Toronto, Toronto, Canada), and volumetric percentage of density and dense volume were assessed automatically with software (Volpara; Volpara Solutions, Wellington, New Zealand). Clinical Breast Imaging Reporting and Data System (BI-RADS) classifications of breast density were extracted from mammography reports. Odds ratios and 95% confidence intervals (CIs) were estimated by using conditional logistic regression stratified according to age and race and adjusted for body mass index, parity, and menopausal status, and the area under the receiver operating characteristic curve (AUC) was computed. Results The adjusted odds ratios and 95% CIs for each standard deviation increment of the percentage of density, dense area, volumetric percentage of density, and dense volume were 1.61 (95% CI: 1.19, 2.19), 1.49 (95% CI: 1.15, 1.92), 1.54 (95% CI: 1.12, 2.10), and 1.41 (95% CI: 1.11, 1.80), respectively. Odds ratios for women with extremely dense breasts compared with those with scattered areas of fibroglandular density were 2.06 (95% CI: 0.85, 4.97) and 2.05 (95% CI: 0.90, 4.64) for BI-RADS and Volpara density classifications, respectively. Clinical BI-RADS was more accurate (AUC, 0.68; 95% CI: 0.63, 0.74) than Volpara (AUC, 0.64; 95% CI: 0.58, 0.70) and continuous measures of percentage of density (AUC, 0.66; 95% CI: 0.60, 0.72), dense area (AUC, 0.66; 95% CI: 0.60, 0.72), volumetric percentage of density (AUC, 0.64; 95% CI: 0.58, 0.70), and density volume (AUC, 0.65; 95% CI: 0.59, 0.71), although the AUC differences were not statistically significant. Conclusion Mammographic density on FFDM images was positively associated with breast cancer risk by using the computer assisted methods and BI-RADS. BI-RADS classification was as accurate as computer-assisted methods for discrimination of patients from control subjects.

Indexed as

Breast DensityMammographyAdultAgedBody Mass IndexBreast NeoplasmsCase-Control StudiesEarly Detection of CancerFemaleHumansMiddle AgedNeoplasm InvasivenessRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesRisk AssessmentSoftware

Identifiers

PMID27598536
PMCPMC5283867
OpenAlexW2516138792

What OpenQuestion holds

Textmetadata
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Registered trials

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.