ArticleAJR. American journal of roentgenology2012
Training the ACRIN 6666 Investigators and effects of feedback on breast ultrasound interpretive performance and agreement in BI-RADS ultrasound feature analysis.
Article in AJR. American journal of roentgenology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00072501 (Screening Breast Ultrasound in High-Risk Women), which is not on this map. Cited by 29 papers.
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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.
Screening Breast Ultrasound in High-Risk Women
Who cites it
29 citing papers in PubMed.
- Ultrasound as the Primary Screening Test for Breast Cancer: Analysis From ACRIN 6666.Journal of the National Cancer Institute · 2016Trial
- Generalized contrast-to-noise ratio applied to short-lag spatial coherence ultrasound differentiates breast cysts from solid masses.Radiology advances · 2025Article
- Improving breast ultrasonography education: the impact of AI-based decision support on the performance of non-specialist medical professionals.Ultrasonography (Seoul, Korea) · 2025Article
- Updates in Breast Cancer Screening and Diagnosis.Current treatment options in oncology · 2024Review
- Training on contrast-enhanced ultrasound LI-RADS classification for resident radiologists: a retrospective comparison of performance after training.Insights into imaging · 2024Article
- Article
- Artificial intelligence in breast ultrasound: application in clinical practice.Ultrasonography (Seoul, Korea) · 2024Article
- Validating racial and ethnic non-bias of artificial intelligence decision support for diagnostic breast ultrasound evaluation.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Development and validation of a transformer-based CAD model for improving the consistency of BI-RADS category 3-5 nodule classification among radiologists: a multiple center study.Quantitative imaging in medicine and surgery · 2023Article
- Texture quantified from ultrasound Nakagami parametric images is diagnostically relevant for breast tumor characterization.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Breast Tumor Classification Using Intratumoral Quantitative Ultrasound Descriptors.Computational and mathematical methods in medicine · 2022Article
- Clinical use and adjustment of ultrasound elastography for breast lesions followed WFUMB guidelines and recommendations in the real world.Frontiers in oncology · 2022Article
- Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study.Frontiers in oncology · 2022Article
- Agreement in breast lesion assessment and final BI-RADS classification between radial and meander-like breast ultrasound.BMC medical imaging · 2021Article
- Interobserver and intermethod variability in data interpretation of breast strain elastography in suspicious breast lesionsTurkish journal of medical sciences · 2021Article
- False-negative results on computer-aided detection software in preoperative automated breast ultrasonography of breast cancer patients.Ultrasonography (Seoul, Korea) · 2021Article
- Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment.AJR. American journal of roentgenology · 2020Article
- Effect of training on ultrasonography (US) BI-RADS features for radiology residents: a multicenter study comparing performances after training.European radiology · 2019Article
- Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue.Scientific reports · 2019Article
- Effect of a Deep Learning Framework-Based Computer-Aided Diagnosis System on the Diagnostic Performance of Radiologists in Differentiating between Malignant and Benign Masses on Breast Ultrasonography.Korean journal of radiology · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveQualification tasks in mammography and breast ultrasound were developed for the American College of Radiology Imaging Network (ACRIN) 6666 Investigators. We sought to assess the effects of feedback on breast ultrasound interpretive performance and agreement in BI-RADS feature analysis among a subset of these experienced observers. MATERIALS AND
methodsAfter a 1-hour didactic session on BI-RADS: Ultrasound, an interpretive skills quiz set of 70 orthogonal sets of breast ultrasound images including 25 (36%) malignancies was presented to 100 experienced breast imaging observers. Thirty-five observers reviewed the quiz set twice: first without and then with immediate feedback of consensus feature analysis, management recommendations, and pathologic truth. Observer performance (sensitivity, specificity, area under the curve [AUC]) was calculated without feedback and with feedback. Kappas were determined for agreement on feature analysis and assessments.
resultsFor 35 observers without feedback, the mean sensitivity was 89% (range, 68-100%); specificity, 62% (range, 42-82%); and AUC, 82% (range, 73-89%). With feedback, the mean sensitivity was 93% (range, 80-100%; mean increase, 4%; range of increase, 0-12%; p < 0.0001), the mean specificity was 61% (range, 45-73%; mean decrease, 1%; range of change, -18% to 11%; p = 0.19), and the mean AUC was 84% (range, 78-90%; mean increase, 2%; range of change, -3% to 9%; p < 0.0001). Three breast imagers in the lowest quartile of initial performance showed the greatest improvement in sensitivity with no change or improvement in AUC. The kappa values for feature analysis did not change, but there was improved agreement about final assessments, with the kappa value increasing from 0.53 (SE, 0.02) without feedback to 0.59 (SE, 0.02) with feedback (p < 0.0001).
conclusionMost experienced breast imagers showed excellent breast ultrasound interpretive skills. Immediate feedback of consensus BI-RADS: Ultrasound features and histopathologic results improved performance in ultrasound interpretation across all experience variables.
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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.