Evidence map›Paper›PMID 27660807›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2016

Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study.

Nooshin Kiarashi, Loren W Nolte, Joseph Y Lo, W Paul Segars, Sujata V Ghate, Justin B Solomon, Ehsan Samei

Abstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-weighted citation impact, top 20% 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

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Virtual clinical trials in medical imaging: a review.Journal of medical imaging (Bellingham, Wash.) · 2020
    Review
  2. Article
  3. 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

7 authors at 2 institutions in 1 country.

Nooshin KiarashiDuke University Medical Center, Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina 27708, United States; Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina 27708, United States.
Loren W NolteDuke University, Department of Electrical and Computer Engineering, Durham, North Carolina 27708, United States; Duke University, Department of Biomedical Engineering, Durham, North Carolina 27708, United States.
Joseph Y LoDuke University Medical Center, Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina 27708, United States; Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina 27708, United States; Duke University, Department of Biomedical Engineering, Durham, North Carolina 27708, United States; Duke University, Medical Physics Graduate Program, Durham, North Carolina 27708, United States.
W Paul SegarsDuke University Medical Center, Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina 27708, United States; Duke University, Medical Physics Graduate Program, Durham, North Carolina 27708, United States.
Sujata V GhateDuke University Medical Center , Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina 27708, United States.
Justin B SolomonDuke University Medical Center, Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina 27708, United States; Duke University, Medical Physics Graduate Program, Durham, North Carolina 27708, United States.
Ehsan SameiDuke University Medical Center, Department of Radiology, Carl E. Ravin Advanced Imaging Laboratories, Durham, North Carolina 27708, United States; Duke University, Department of Electrical and Computer Engineering, Durham, North Carolina 27708, United States; Duke University, Department of Biomedical Engineering, Durham, North Carolina 27708, United States; Duke University, Medical Physics Graduate Program, Durham, North Carolina 27708, United States; Duke University, Department of Physics, Durham, North Carolina 27708, United States.
Duke University · USAdvanced Imaging Research (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to characterize the effect of background tissue density and heterogeneity on the detection of irregular masses in breast tomosynthesis, while demonstrating the capability of the sophisticated tools that can be used in the design, implementation, and performance analysis of virtual clinical trials (VCTs). Twenty breast phantoms from the extended cardiac-torso (XCAT) family, generated based on dedicated breast computed tomography of human subjects, were used to extract a total of 2173 volumes of interest (VOIs) from simulated tomosynthesis images. Five different lesions, modeled after human subject tomosynthesis images, were embedded in the breasts and combined with the lesion absent condition yielded a total of [Formula: see text] VOIs. Effects of background tissue density and heterogeneity on the detection of the lesions were studied by implementing a composite hypothesis signal detection paradigm with location known exactly, lesion known exactly or statistically, and background known statistically. Using the area under the receiver operating characteristic curve, detection performance deteriorated as density was increased, yielding findings consistent with clinical studies. A human observer study was performed on a subset of the simulated tomosynthesis images, confirming the detection performance trends with respect to density and serving as a validation of the implemented detector. Performance of the implemented detector varied substantially across the 20 breasts. Furthermore, background tissue density and heterogeneity affected the log-likelihood ratio test statistic differently under lesion absent and lesion present conditions. Therefore, considering background tissue variability in tissue models can change the outcomes of a VCT and is hence of crucial importance. The XCAT breast phantoms have the potential to address this concern by offering realistic modeling of background tissue variability based on a wide range of human subjects, comprising various breast shapes, sizes, and densities.

Indexed as

anthropomorphic lesion modelsbreast densitybreast imagingdetectiondigital breast tomosynthesisdoubly composite hypothesis testingextended cardiac-torso breast phantomsMonte Carlo integrationperformance evaluationreceiver operating characteristic curve analysistissue heterogeneityvirtual clinical trials

Identifiers

PMID27660807
PMCPMC5020162
OpenAlexW2540269388

What OpenQuestion holds

Textmetadata
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.