Evidence map›Paper›PMID 25825980›Full record

ArticlePhysics in medicine and biology2015

Simulated lesion, human observer performance comparison between thin-section dedicated breast CT images versus computed thick-section simulated projection images of the breast.

L Chen, J M Boone, C K Abbey, J Hargreaves, C Bateni, K K Lindfors, K Yang, A Nosratieh, A Hernandez, P Gazi

Open access · greenAbstract readComparative Study
In one paragraph

Article in Physics in medicine and biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.5field-weighted citation impact, top 31% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. High-resolutionJournal of medical imaging (Bellingham, Wash.) · 2021
    Article
  5. Article
  6. Visual adaptation and the amplitude spectra of radiological images.Cognitive research: principles and implications · 2018
    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 at 2 institutions in 1 country.

L ChenDepartment of Radiology, University of California, Davis, CA, USA.
J M BooneDepartment of Radiology, University of California, Davis, CA, USA.
C K AbbeyDepartment of Psychology, University of California Santa Barbara, CA, USA.
J HargreavesDepartment of Radiology, University of California, Davis, CA, USA.
C BateniDepartment of Radiology, University of California, Davis, CA, USA.
K K LindforsDepartment of Radiology, University of California, Davis, CA, USA.
K YangDepartment of Radiology, University of California, Davis, CA, USA.
A NosratiehDepartment of Radiology, University of California, Davis, CA, USA.
A HernandezDepartment of Radiology, University of California, Davis, CA, USA.
P GaziDepartment of Radiology, University of California, Davis, CA, USA.
University of California, Davis · USUniversity of California, Santa Barbara · US

Funding

Breast CT scanner for earlier cancer detectionR01EB002138 · NIBIB · UNIVERSITY OF CALIFORNIA DAVIS · PI BOONE, JOHN M · 2003 to 2012
$9.0M
NIBIB NIH HHS NIH R01EB002138NIBIB NIH HHS R01 EB002138
6 · The paper itself

Abstract

The objective of this study was to compare the lesion detection performance of human observers between thin-section computed tomography images of the breast, with thick-section (>40 mm) simulated projection images of the breast. Three radiologists and six physicists each executed a two alterative force choice (2AFC) study involving simulated spherical lesions placed mathematically into breast images produced on a prototype dedicated breast CT scanner. The breast image data sets from 88 patients were used to create 352 pairs of image data. Spherical lesions with diameters of 1, 2, 3, 5, and 11 mm were simulated and adaptively positioned into 3D breast CT image data sets; the native thin section (0.33 mm) images were averaged to produce images with different slice thicknesses; average section thicknesses of 0.33, 0.71, 1.5 and 2.9 mm were representative of breast CT; the average 43 mm slice thickness served to simulate simulated projection images of the breast.The percent correct of the human observer's responses were evaluated in the 2AFC experiments. Radiologists lesion detection performance was significantly (p < 0.05) better in the case of thin-section images, compared to thick section images similar to mammography, for all but the 1 mm lesion diameter lesions. For example, the average of three radiologist's performance for 3 mm diameter lesions was 92% correct for thin section breast CT images while it was 67% for the simulated projection images. A gradual reduction in observer performance was observed as the section thickness increased beyond about 1 mm. While a performance difference based on breast density was seen in both breast CT and the projection image results, the average radiologist performance using breast CT images in dense breasts outperformed the performance using simulated projection images in fatty breasts for all lesion diameters except 11 mm. The average radiologist performance outperformed that of the average physicist observer, however trends in performance were similar. Human observers demonstrate significantly better mass-lesion detection performance on thin-section CT images of the breast, compared to thick-section simulated projection images of the breast.

Indexed as

Observer VariationAdultAgedBreastBreast NeoplasmsComputer SimulationFemaleHumansImage Processing, Computer-AssistedImaging, Three-DimensionalMammographyMiddle AgedPattern Recognition, VisualRadiographic Image Interpretation, Computer-AssistedTask Performance and AnalysisTomography, X-Ray Computed

Identifiers

PMID25825980
PMCPMC4414405
OpenAlexW2001697171

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.