Αρχειοθήκη ιστολογίου

Αλέξανδρος Γ. Σφακιανάκης
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5
Άγιος Νικόλαος Κρήτη 72100
2841026182
6032607174

Παρασκευή 6 Ιανουαρίου 2017

Lymph node characteristics for predicting locoregional recurrence of papillary thyroid cancer in adolescents and young adults

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Publication date: March 2017
Source:Oral Oncology, Volume 66
Author(s): Yungang Sun, Xiang Liu, Wei Ouyang, Huijuan Feng, Juqing Wu, Pan Chen, Jing Wang
PurposeTo determine lymph node (LN) characteristics predictive of locoregional recurrence (LRR) in adolescent and young adult (AYA) ages 15–39years presenting with papillary thyroid cancer (PTC) and cervical pathologically proven LN metastases (pN1).Methods and materialsRetrospective chart review was performed for all patients with PTC and nodal metastases who had undergone total thyroidectomy, LN dissection, and postoperative radioactive iodine therapy in a university hospital between 2006 and 2014. Clinical and histopathologic markers that were independently associated with tumor recurrence were evaluated.ResultsIn all, 329 consecutive AYA patients were included. At a median follow-up of 57months, twenty patients (6.08%) experienced LRR. No patients had distant metastases, and no patients died during follow-up. Based on Cox regression analysis, the presence of extranodal extension and more than six metastatic LNs at presentation were independent predictive factors for LRR. However, age, male sex, total number of LNs resected, pN1b, LN ratio, size of the largest metastatic LN, extranodal extension, tumor size, bilateral tumor, multifocality, vascular invasion, and Hashimoto thyroiditis were not correlated with an increased risk for LRR. A cutoff of more than six positive nodes optimally predicted future LRR with sensitivity and specificity values of 85.0% and 60.2%, respectively.ConclusionThe presence of extranodal extension and more than six metastatic LNs were independent predictors of LRR in AYA patients with pN1 disease. Evaluation of these prognostic factors appears to help identify patients who require close monitoring.



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