Evidence map›Paper›PMID 30660473›Full record

ArticleAcademic radiology2019

Initial Clinical Experience with Stationary Digital Breast Tomosynthesis.

Yueh Z Lee, Connor Puett, Christina R Inscoe, Beilin Jia, Connie Kim, Ruth Walsh, Sora Yoon, Suk Jung Kim, Cherie M Kuzmiak, Donglin Zeng and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Academic radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Yueh Z LeeDepartment of Radiology, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514; Department of Biomedical Engineering, University of North Carolina at Chapel Hill, 152 MacNider Hall CB7575, Chapel Hill, NC 27599. Electronic address: leey@med.unc.edu.
Connor PuettDepartment of Biomedical Engineering, University of North Carolina at Chapel Hill, 152 MacNider Hall CB7575, Chapel Hill, NC 27599.
Christina R InscoeDepartment of Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Beilin JiaDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Connie KimDepartment of Radiology, Duke University, Durham, North Carolina.
Ruth WalshDepartment of Radiology, Duke University, Durham, North Carolina.
Sora YoonDepartment of Radiology, Duke University, Durham, North Carolina.
Suk Jung KimDepartment of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan,South Korea.
Cherie M KuzmiakDepartment of Radiology, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514.
Donglin ZengDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Jianping LuDepartment of Physics and Astronomy, University of North Carolina at Chapel Hill,Chapel Hill, North Carolina.
Otto ZhouDepartment of Physics and Astronomy, University of North Carolina at Chapel Hill,Chapel Hill, North Carolina.

Funding

Translational Nanosystems for Improved Lung Cancer Treatment with Small MoleculesU54CA151652 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, WEILI · 2010 to 2014
$12.1M
Next Generation Digital Breast Tomosynthesis ScannerR01CA134598 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ZHOU, OTTO Z · 2009 to 2012
$1.7M
NCI NIH HHS R01 CA134598NCI NIH HHS U54 CA151652
6 · The paper itself

Abstract

RATIONALE AND

objectivesA linear array of carbon nanotube-enabled x-ray sources allows for stationary digital breast tomosynthesis (sDBT), during which projection views are collected without the need to move the x-ray tube. This work presents our initial clinical experience with a first-generation sDBT device. MATERIALS AND

methodsFollowing informed consent, women with a "suspicious abnormality" (Breast Imaging Reporting and Data System 4), discovered by digital mammography and awaiting biopsy, were also imaged by the first generation sDBT. Four radiologists participated in this paired-image study, completing questionnaires while interpreting the mammograms and sDBT image stacks. Areas under the receiver operating characteristic curve were used to measure reader performance (likelihood of correctly identifying malignancy based on pathology as ground truth), while a multivariate analysis assessed preference, as readers compared one modality to the next when interpreting diagnostically important image features.

resultsFindings from 43 women were available for analysis, in whom 12 cases of malignancy were identified by pathology. The mean areas under the receiver operating characteristic curve was significantly higher (p < 0.05) for sDBT than mammography for all breast density categories and breast thicknesses. Additionally, readers preferred sDBT over mammography when evaluating mass margins and shape, architectural distortion, and asymmetry, but preferred mammography when characterizing microcalcifications.

conclusionReaders preferred sDBT over mammography when interpreting soft-tissue breast features and were diagnostically more accurate using images generated by sDBT in a Breast Imaging Reporting and Data System 4 population. However, the findings also demonstrated the need to improve microcalcification conspicuity, which is guiding both technological and image-processing design changes in future sDBT devices.

Indexed as

BreastMammographyAdultBreast NeoplasmsFemaleHumansImage Processing, Computer-AssistedMiddle AgedMultimodal ImagingNanotubes, CarbonRadiographic Image EnhancementNanotubes, Carbon3D mammographyBreast cancer imagingDigital mammographyStationary digital breast tomosynthesis

Identifiers

PMID30660473
PMCPMC6635094

What OpenQuestion holds

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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.