Application of a carbon nanoparticle suspension for sentinel lymph node mapping in patients with early breast cancer: a retrospective cohort study

Liulu Zhang(Guangdong Academy of Medical Sciences), Yi‐Jie Huang(Guangdong Academy of Medical Sciences), Ciqiu Yang(Guangdong General Hospital), Teng Zhu(Guangdong General Hospital), Yufeng Lin(Guangdong Academy of Medical Sciences), Hongfei Gao(Guangdong Academy of Medical Sciences), Mei Yang(Guangdong Academy of Medical Sciences), Minyi Cheng(Guangdong General Hospital), Kun Wang(Guangdong General Hospital)
World Journal of Surgical Oncology
June 18, 2018
Cited by 51Open Access
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Abstract

BACKGROUND: To stage axillary lymph nodes in women with early-stage breast cancer, sentinel lymph node biopsy (SLNB), rather than axillary lymph node dissection (ALND), has been employed. Moreover, different tracer methods have various advantages and disadvantages. In recent years, carbon nanoparticle suspensions (CNSs) have been used as lymph node tracers during surgeries for thyroid cancer, gastric cancer, and colorectal cancer. The study retrospectively analyzed the feasibility and accuracy of CNS for sentinel lymph node (SLN) mapping in patients with early breast cancer. METHODS: This single-center, retrospective study included breast cancer patients who underwent SLNB from January 1, 2016, to December 31, 2017, in the Department of Breast Cancer, Guangdong General Hospital. All patients received standard SLNB surgery using a CNS tracer. RESULTS: A total of 332 cases were included in this study. The SLN identification rate was 99.1% (329/332), and the mean number of SLNs was 2.6 (range, 1-6). SLN metastasis was found in 62 (18.8%) cases, of which 90.3% were found to be macrometastases. The sensitivity of SLNB was 95.9% (47/49), with a specificity of 100% (42/42), a positive predictive value of 100% (47/47), a negative predictive value of 95.5% (42/44), and a false-negative rate of 4.1% (2/49). CONCLUSION: The identification and predictive values of a CNS tracer for SLNB were satisfactory.


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