Evidence map›Paper›PMID 35230159›Full record

ArticleThe British journal of radiology2022

Comparison of breast density assessments between synthesized C-View™ & intelligent 2D™ mammography.

Sadia Khanani, Lekui Xiao, Matthew R Jensen, Amy L Conners, Robert T Fazzio, Carrie B Hruska, Stacey Winham, Fang Fang Wu, Christopher G Scott, Celine M Vachon

Abstract read
In one paragraph

Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

10 authors.

Sadia KhananiDepartment of Radiology, Rochester, MN.
Lekui XiaoDepartment of Radiology, Rochester, MN.
Matthew R JensenDepartment of Radiology, Rochester, MN.
Amy L ConnersDepartment of Radiology, Rochester, MN.
Robert T FazzioDepartment of Radiology, Rochester, MN.
Carrie B HruskaDepartment of Radiology, Rochester, MN.
Stacey WinhamDepartment of Quantitative Health Sciences, Rochester, MN.
Fang Fang WuDepartment of Quantitative Health Sciences, Rochester, MN.
Christopher G ScottDepartment of Quantitative Health Sciences, Rochester, MN.
Celine M VachonDepartment of Quantitative Health Sciences, Rochester, MN.

Funding

Paul Calabresi Program in Clinical/Translational Research at Mayo ClinicK12CA090628 · NCI · MAYO CLINIC ROCHESTER · PI WEROHA, SARAVUT · 2001 to 2025
$19.7M
NCI NIH HHS K12 CA090628
6 · The paper itself

Abstract

objectiveTo compare breast density assessments between C-View

methodsIn this retrospective study, we identified a subset of females between March 2017 and December 2019 who underwent screening digital breast tomosynthesis (DBT) with C-View followed by DBT with Intelligent 2D. Clinical Breast Imaging Reporting and Database System breast density was obtained along with volumetric breast density measures (including density grade, breast volume, percentage volumetric density, dense volume) using Volpara

results67 patients (avg age 62.7; range 40-84) were included with an average of 13.3 months between the two exams. No difference was found in Breast Imaging Reporting and Database System density between the SM reconstructions (

conclusionWe found no significant differences in clinical and automated breast density assessments between these two versions of SM. ADVANCES IN KNOWLEDGE: Lack of differences in density estimates between the two SM reconstructions is important as density assignment impacts risk stratification and adjunct screening recommendations.

Indexed as

Breast DensityBreast NeoplasmsBreastChildChild, PreschoolEarly Detection of CancerFemaleHumansMammographyRetrospective Studies

Identifiers

PMID35230159
PMCPMC10996406

What OpenQuestion holds

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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.