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Cited by 225

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Noninvasive photoacoustic identification of sentinel lymph nodes containing methylene blue in vivo in a rat model

Open access
Song, Kwang Hyun, Stein, Erich W., Margenthaler, Julie A., Wang, Lihong V.·2008·Journal of Biomedical Optics

Abstract

Sentinel lymph node biopsy (SLNB) has become the standard method of axillary staging for patients with breast cancer and clinically negative axillae. Even though SLNB using both methylene blue and radioactive tracers has a high identification rate, it still relies on an invasive surgical procedure with associated morbidity. Axillary ultrasound has emerged as a diagnostic tool to evaluate the axilla, but it can only assess morphology and cannot specifically identify sentinel lymph nodes (SLNs). In this pilot study, we propose a noninvasive photoacoustic SLN identification system using methylene blue injection in a rat model. We successfully image a SLN with high optical contrast (146+/-41, standard deviation) and good ultrasonic resolution (approximately 500 microm) in vivo. We also show potential feasibility for clinical applications by imaging 20- and 31-mm-deep SLNs in 3-D and 2-D, respectively. Our results suggest that this technology would be a useful clinical tool, allowing clinicians to identify SLNs noninvasively in vivo.

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Metadata

Authors
Song, K. H., Stein, E. W., Margenthaler, J. A., & Wang, L. V.
Venue
Journal of Biomedical Optics
Published
2008
Volume
13
Issue
5
Pages
054033
ISSN
1083-3668 (ISSN-L)
OpenAlex
W2099046460
Cited by
225
References
0
FWCI
10.45
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Noninvasive photoacoustic identification of sentinel lymph nodes containing methylene blue in vivo in a rat model. https://app.cochat.ai/papers/10.1117/1.2976427
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Song, K. H., Stein, E. W., Margenthaler, J. A., & Wang, L. V. (2008). Noninvasive photoacoustic identification of sentinel lymph nodes containing methylene blue in vivo in a rat model. Journal of Biomedical Optics. https://doi.org/10.1117/1.2976427

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.