The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.
Breast MRI: State of the Art
Open accessAbstract
MRI of the breast has the highest sensitivity for breast cancer detection among current clinical imaging modalities and is indispensable for breast imaging practice. While the basis of breast MRI consists of T1-weighted contrast-enhanced imaging, T2-weighted, ultrafast, and diffusion-weighted imaging may be used to improve lesion characterization. Such multiparametric assessment of breast lesions allows for excellent discrimination between benign and malignant breast lesions. Indications for breast MRI are expanding. In preoperative staging, multiple studies confirm the superiority of MRI to other imaging modalities for tumor size estimation and detection of additional tumor foci in the ipsilateral and contralateral breast. Ongoing studies show that in experienced hands this can be used to improve breast cancer surgery, although there is no evidence of improved long-term outcomes. Screening indications are likewise growing as evidence is accumulating that OncologicRI depicts cancers at an earlier stage than mammography in all women. To manage the associated costs for screening, the use of abbreviated protocols may be beneficial. In patients treated with neoadjuvant chemotherapy, MRI is used to document response. It is essential to realize that oncologic and surgical response are different, and evaluation should be adapted to the underlying question.
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Cited by
Deep Learning for Automated Triaging of 4581 Breast MRI Examinations from the DENSE Trial
Verburg, E., van Gils, C. H., van der Velden, B. H. M., Bakker, M. F., Pijnappel, R. M., Veldhuis, W. B., & Gilhuijs, K. G. A. (2022) · Radiology · doi:10.1148/radiol.2021203960 · Cited by 73
Metadata
- Authors
- Mann, R. M., Cho, N., & Moy, L.
- Venue
- Radiology
- Published
- 2019-09
- Volume
- 292
- Issue
- 3
- Pages
- 520-536
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W2964656561
- Cited by
- 917
- References
- 90
- FWCI
- 42.60
- Type
- journal article
- Language
- en
- Open access
- green
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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.
