The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts
Open accessAbstract
PURPOSE: Mammography, the standard method of breast cancer screening, misses many cancers, especially in dense-breasted women. We compared the performance and diagnostic yield of mammography alone versus an automated whole breast ultrasound (AWBU) plus mammography in women with dense breasts and/or at elevated risk of breast cancer. METHODS: AWBU screening was tested in 4,419 women having routine mammography ( TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00649337). Cancers occurring during the study and subsequent 1-year follow-up were evaluated. Sensitivity, specificity and positive predictive value (PPV) of biopsy recommendation for mammography alone, AWBU and mammography with AWBU were calculated. RESULTS: Breast cancer detection doubled from 23 to 46 in 6,425 studies using AWBU with mammography, resulting in an increase in diagnostic yield from 3.6 per 1,000 with mammography alone to 7.2 per 1,000 by adding AWBU. PPV for biopsy based on mammography findings was 39.0% and for AWBU 38.4%. The number of detected invasive cancers 10 mm or less in size tripled from 7 to 21 when AWBU findings were added to mammography. CONCLUSION: AWBU resulted in significant cancer detection improvement compared with mammography alone. Additional detection and the smaller size of invasive cancers may justify this technology's expense for women with dense breasts and/or at high risk for breast cancer.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Gail, M. H., Brinton, L. A., Byar, D. P., Corle, D. K., Green, S. B., Schairer, C., & Mulvihill, J. J. (1989) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/81.24.1879 · Cited by 3,334
Cited by
Breast cancer detection using deep learning: Datasets, methods, and challenges ahead
din, N. M. U., Dar, R. A., Rasool, M., & Assad, A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106073 · Cited by 363
Saba, T. (2020) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2020.06.033 · Cited by 284
Ciritsis, A., Rossi, C., Eberhard, M., Marcon, M., Becker, A. S., & Boss, A. (2019) · European Radiology · doi:10.1007/s00330-019-06118-7 · Cited by 117
Choi, J. S., Han, B.-K., Ko, E. S., Bae, J. M., Ko, E. Y., Song, S. H., Kwon, M.-R., Shin, J. H., & Hahn, S. Y. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2018.0530 · Cited by 104
RETRACTED ARTICLE: Deep learning approaches to detect breast cancer: a comprehensive review
Sharafaddini, A. M., Esfahani, K. K., & Mansouri, N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20011-6 · Cited by 104
Retracted
Metadata
- Authors
- Kelly, K. M., Dean, J., Comulada, W. S., & Lee, S.-J.
- Venue
- European Radiology
- Published
- 2009-09-02
- Volume
- 20
- Issue
- 3
- Pages
- 734-742
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- OpenAlex
- W2157687887
- Cited by
- 497
- References
- 1
- FWCI
- 16.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. https://app.cochat.ai/papers/10.1007/s00330-009-1588-y
- The paper
- Kelly, K. M., Dean, J., Comulada, W. S., & Lee, S.-J. (2010). Breast cancer detection using automated whole breast ultrasound and mammography in radiographically dense breasts. European Radiology. https://doi.org/10.1007/s00330-009-1588-y
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.