The DOI matches its Crossref record.
Breast Disease: Clinical Application of US Elastography for Diagnosis
Abstract
PURPOSE: To evaluate the diagnostic performance of real-time freehand elastography by using the extended combined autocorrelation method (CAM) to differentiate benign from malignant breast lesions, with pathologic diagnosis as the reference standard. MATERIALS AND METHODS: This study was approved by the University of Tsukuba Human Subjects Institutional Review Board; all patients gave informed consent. Conventional ultrasonography (US) and real-time US elastography with CAM were performed in 111 women (mean age, 49.4 years; age range, 27-91 years) who had breast lesions (59 benign, 52 malignant). Elasticity images were assigned an elasticity score according to the degree and distribution of strain induced by light compression. The area under the curve and cutoff point, both of which were obtained by using a receiver operating characteristic curve analysis, were used to assess diagnostic performance. Mean scores were examined by using a Student t test. Sensitivity, specificity, and accuracy were compared by using the standard proportion difference test or the Delta-equivalent test. RESULTS: For elasticity score, the mean +/- standard deviation was 4.2 +/- 0.9 for malignant lesions and 2.1 +/- 1.0 for benign lesions (P < .001). When a cutoff point of between 3 and 4 was used, elastography had 86.5% sensitivity, 89.8% specificity, and 88.3% accuracy. When a best cutoff point of between 4 and 5 was used, conventional US had 71.2% sensitivity, 96.6% specificity, and 84.7% accuracy. Elastography had higher sensitivity than conventional US (P < .05). By using equivalence bands for noninferiority or equivalence, it was shown that the specificity of elastography was not inferior to that of conventional US and that the accuracy of elastography was equivalent to that of conventional US. CONCLUSION: For assessing breast lesions, US elastography with the proposed imaging classification, which was simple compared with that of the Breast Imaging Recording and Data System classification, had almost the same diagnostic performance as conventional US.
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
Cited by
Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer
Zheng, X., Yao, Z., Huang, Y., Yu, Y., Wang, Y., Liu, Y., Mao, R., Li, F., Xiao, Y., Wang, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15027-z · Cited by 607
Elastography imaging: the 30 year perspective
Ormachea, J., & Parker, K. J. (2020) · Physics in Medicine & Biology · doi:10.1088/1361-6560/abca00 · Cited by 104
Five-dimensional ultrasound system for soft tissue visualization
Deshmukh, N. P., Caban, J. J., Taylor, R. H., Hager, G. D., & Boctor, E. M. (2015) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1277-z · Cited by 4
Metadata
- Authors
- Itoh, A., Ueno, E., Tohno, E., Kamma, H., Takahashi, H., Shiina, T., Yamakawa, M., & Matsumura, T.
- Venue
- Radiology
- Published
- 2006-05
- Volume
- 239
- Issue
- 2
- Pages
- 341-350
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2132290789
- Cited by
- 1,640
- References
- 0
- FWCI
- 35.26
- Type
- journal article
- Language
- en
- Open access
- closed
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 Disease: Clinical Application of US Elastography for Diagnosis. https://app.cochat.ai/papers/10.1148/radiol.2391041676
- The paper
- Itoh, A., Ueno, E., Tohno, E., Kamma, H., Takahashi, H., Shiina, T., Yamakawa, M., & Matsumura, T. (2006). Breast Disease: Clinical Application of US Elastography for Diagnosis. Radiology. https://doi.org/10.1148/radiol.2391041676
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.