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ONCOLOGY

Comparative study of patients with and without sentinel lymph node biopsy (SLNB) in oral and oropharyngeal cancer: is SLNB an accurate and useful procedure?

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Pages 199-204 | Received 04 Feb 2008, Published online: 08 Jul 2009
 

Abstract

Conclusion. Sentinel lymph node biopsy (SLNB) is a technique that facilitates the study of metastasis in oral and oropharyngeal cancer and reduces morbidity in patients affected by this pathology. Objectives. Oral and oropharyngeal cancers are common and are frequently associated with spread to cervical lymph nodes. We attempted to confirm the usefulness of SLNB in patients suffering from squamous cell carcinoma of the oral cavity as a prognostic technique for lymph node prediction of micrometastatic invasion. Using surgical parameters, we compared its relevance against a retrospective group without SLNB. Patients and methods. We studied a prospective group of 22 patients, 21 men and 1 woman, aged 40–83 years (mean=57.77, SD=13) in which SLNB was performed and a retrospective group of 22 patients, 19 men and 3 women, aged 34–76 years (mean=52.68, SD=11) in which SLNB was not performed. Both groups presented oral cavity tumors at clinical stages T1–T3 and radiological stage N0. SLNB consisted of preoperative lymphoscintigraphy (LG), intraoperative detection of the sentinel lymph node (SLN), and a histopathological examination following the recommendations of the Association of Directors of Anatomic and Surgical Pathology (ADASP) and the Canniesburn protocol. Results. We observed SLNs in 21 of 22 patients, with a total of 32 SLNs detected at cervical level II (65.63%), I (21.87%), and III (12.5%). SLNB displayed a sensitivity of 73% (confidence interval (CI)=0.51–0.99) and 100% specificity (CI=1). Negative and positive predictive values were 80% and 100%, respectively. SLNB significantly reduced the incidence of radical neck dissection (p=0.003), the need for recovery/resuscitation beds after surgery (p=0.002), surgical complications (p=0.034), and length of hospital stay (p=0.01).

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