Evidence map›Paper›PMID 34362092›Full record

ArticleJournal of clinical medicine2021

Quantitative Breast Density in Contrast-Enhanced Mammography.

Gisella Gennaro, Melissa L Hill, Elisabetta Bezzon, Francesca Caumo

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.6field-weighted citation impact, top 32% 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

9 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Contrast-enhanced mammography improves patient access to functional breast imaging.Journal of medical imaging and radiation oncology · 2025
    Review
  7. Contrast-enhanced mammography in high-dense breasts: a narrative review.Translational breast cancer research : a journal focusing on translational research in breast cancer · 2025
    Review
  8. Article
  9. 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

4 authors at 1 institution in 1 country.

Gisella GennaroBreast Radiology Department, Veneto Institute of Oncology (IRCCS), 35128 Padua, Italy.ORCID 0000-0003-2444-1778
Melissa L HillVolpara Health Technologies Ltd., Wellington 6011, New Zealand.ORCID 0000-0002-9925-7601
Elisabetta BezzonBreast Radiology Department, Veneto Institute of Oncology (IRCCS), 35128 Padua, Italy.
Francesca CaumoBreast Radiology Department, Veneto Institute of Oncology (IRCCS), 35128 Padua, Italy.
Istituto Oncologico Veneto · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Contrast-enhanced mammography (CEM) demonstrates a potential role in personalized screening models, in particular for women at increased risk and women with dense breasts. In this study, volumetric breast density (VBD) measured in CEM images was compared with VBD obtained from digital mammography (DM) or tomosynthesis (DBT) images. A total of 150 women who underwent CEM between March 2019 and December 2020, having at least a DM/DBT study performed before/after CEM, were included. Low-energy CEM (LE-CEM) and DM/DBT images were processed with automatic software to obtain the VBD. VBDs from the paired datasets were compared by Wilcoxon tests. A multivariate regression model was applied to analyze the relationship between VBD differences and multiple independent variables certainly or potentially affecting VBD. Median VBD was comparable for LE-CEM and DM/DBT (12.73% vs. 12.39%), not evidencing any statistically significant difference (

Indexed as

breast densitycontrast-enhanced mammographymammographytomosynthesis

Identifiers

PMID34362092
PMCPMC8348046
OpenAlexW3154380349

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.