ArticleAcademic radiology2013
Interreader scoring variability in an observer study using dual-modality imaging for breast cancer detection in women with dense breasts.
Article in Academic radiology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03047122 (Detection of Breast Lesions by Automatic Breast US), which is not on this map. Cited by 5 papers.
What it found
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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.
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.
Detection of Breast Lesions by Automatic Breast US (Comparison to Current Hand Held US and Pathological Findings When Exist).
Who cites it
5 citing papers in PubMed, 20 citations in OpenAlex.
- Repeat Breast Ultrasound Demonstrates Utility with Added Cancer Detection in Patients following Breast Imaging Second Opinion Recommendations.The breast journal · 2022Article
- Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection.EXCLI journal · 2017Review
- Impact of lesion segmentation metrics on computer-aided diagnosis/detection in breast computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2014Article
- Segmentation of breast masses on dedicated breast computed tomography and three-dimensional breast ultrasound images.Journal of medical imaging (Bellingham, Wash.) · 2014Article
- Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts.Medical physics · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
RATIONALE AND
objectivesTo evaluate variability in the clinical assessment of breast images, we evaluated scoring behavior of radiologists in a retrospective reader study combining x-ray mammography (XRM) and three-dimensional automated breast ultrasound (ABUS) for breast cancer detection in women with dense breasts.
methodsThe study involved 17 breast radiologists in a sequential study design with readers first interpreting XRM-alone followed by an interpretation of combined XRM + ABUS. Each interpretation included a forced Breast Imaging Reporting and Data System scale and a likelihood that the woman had breast cancer. The analysis included 164 asymptomatic patients, including 31 breast cancer patients, with dense breasts and a negative screening XRM. Of interest were interreader scoring variability for XRM-alone, XRM + ABUS, and the sequential effect. In addition, a simulated double reading by pairs of readers of XRM + ABUS was investigated. Performance analysis included receiver operating characteristic analysis, percentile analysis, and κ statistics. Bootstrapping was used to determine statistical significance.
resultsThe median change in area under the receiver operating characteristic curve after ABUS interpretation was 0.12 (range 0.04-0.19). Reader agreement was fair with the median interreader κ being 0.26 (0.05-0.48) for XRM-alone and 0.34 (0.11-0.55) for XRM + ABUS (95% confidence interval for the difference in κ, 0.06-0.11). Simulated double reading of XRM + ABUS demonstrated tradeoffs in sensitivity and specificity, but conservative simulated double reading resulted in a significant improvement in both sensitivity (16.7%) and specificity (7.6%) with respect to XRM-alone.
conclusionA modest, but statistically significant, increase in interreader agreement was observed after interpretation of ABUS.
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Registered trials
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.