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Impact of immunohistochemistry in sentinel lymph node biopsy in head and neck cancer
Authors:Carlos Takahiro Chone  Marcello B Aniteli  Rodrigo S Magalhães  Leandro L Freitas  Albina Altemani  Celso D Ramos  Elba Etchebehere  Agricio N Crespo
Institution:1. Department of Otolaryngology, Head and Neck, University of Campinas, Rua Tessália Vieira de Camargo, Cidade Universitária, Post Box 6111, Campinas, SP, 13083-970, Brazil
2. Department of Surgical Pathology, University of Campinas, Campinas, Brazil
3. Department of Nuclear Medicine, University of Campinas, Campinas, Brazil
Abstract:To determine the sensitivity, specificity, negative predictive value (NPV) and accuracy of hematoxylin–eosin (HE) staining compared to immunohistochemistry (IHC) in sentinel lymph node (SLN) histological analyses of head and neck squamous cell carcinoma. The Clinical prospective study was carried out at Tertiary referral university hospital. Patients with oral, lip and oropharyngeal squamous cell carcinoma undergoing elective neck dissection with clinically and radiologically negative necks were included. All patients were submitted to computer tomography scan for the evaluation of lymphatic metastases. The surgical procedure consisted of tumor resection, SLN sampling and elective neck dissection. Negative SLNs via HE were then submitted for IHC analysis of cytokeratin AE1/AE3 and step serial section (SSS). The mait outcome measures were the negative predictive value of conventional HE staining techniques in the diagnosis of lymphatic metastases with the SSS/IHC analysis. Of 46 patients undergoing 63 neck procedures, 53 were SLN negative and 10 were positive on HE analysis. Using SSS/IHC analysis of these 53 negative SLNs on HE, two (3.8 %) were found to be positive. For HE, the sensitivity, specificity, NPV and accuracy were 77, 100, 94, and 95 %, respectively. With subsequent analysis with SSS/IHC, these values increased to 92, 100, 98 and 98 %, respectively. SSS/IHC is important in SLN analysis as the false negative rate decreased significantly while increasing the inherent sensitivity of the analyses.
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