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Background: Measurement of thyroglobulin (Tg) protein in the washout of the needle used for fine needle aspiration biopsy cytology (FNAB-C) has been shown to increase the sensitivity of FNAB-C in identifying cervical lymph node (CLN) metastasis from well-differentiated thyroid cancer (TC). In this study, we evaluated whether routine measurement of Tg protein (FNAB-Tgp), Tg mRNA (FNAB-Tgm) and calcitonin (CT) mRNA (FNAB-CTm) in the FNAB washout of CLN increases the accuracy of FNAB-C in the diagnosis of suspicious metastatic CLN. Methods: In this prospective study 35 CLN from 28 patients were examined. Histology showed metastatic papillary TC (PTC) in 26 CLN, metastatic medullary TC (MTC) in 3 CLN, metastatic anaplastic TC (ATC) in 3 CLN and 3 metastatic CLN from extra-thyroidal cancers. Results: The overall accuracy of FNAB-C was 84.4%, reaching 95.7% when the analysis was restricted to PTC. Both FNAB-Tgp and FNAB-Tgm compared favorably with FNAB-C and shown diagnostic performances not statistically different from that of FNAB-C. However, FNAB-Tgp and FNAB-Tgm/FNAB-CTm were found useful in cases in which cytology results were inadequate or provided diagnosis inconsistent with patient's clinical parameters. Conclusions: We demonstrated that FNAB-C, Tg/CT mRNA and Tg protein determination in the fine-needle washout showed similar accuracy in the diagnosis of metastatic CLN from TC. The results of this study suggest that samples for Tg protein and Tg/CT mRNA measurements from CLN suspicious for metastatic TC should be collected, but their measurements should be restricted to cases in which FNAB-C provides uninformative or inconsistent diagnosis with respect to patient's clinical parameters. [ABSTRACT FROM AUTHOR]

Simple Summary: Papillary thyroid cancer (PTC) patients are staged according to the Tumor-Node-Metastasis staging system (TNM). This work was aimed at comparing the usefulness of the 8th edition of TNM (TNM-8), currently used, and that of the previous one (TNM-7) for predicting disease-free interval (DFI) in a cohort of 1148 patients. Moreover, clinicopathological and molecular factors were statistically evaluated in order to determine which of these was/were the best predictor(s) of DFI. Results obtained from the multivariate analysis indicated that advanced tumor stages were independent risk factors for a lower DFI regardless of TNM, but the statistical model created with the TNM-7 was most accurate. When stage-determining factors were included individually in the multivariate analysis, LN metastases, tall cell variant, and age emerged as independent risk factors for a shorter DFI, with lateral LN metastases being the most relevant. No molecular parameters could improve the prediction of DFI provided by LN metastases. The American Joint Committee on Cancer has revised the Tumor-Node-Metastasis (TNM) staging system for papillary thyroid cancer (PTC) patients. We examined the impact of this new classification (TNM-8) on patient stratification and estimated the prognostic value of clinicopathological features for the disease-free interval (DFI) in a cohort of 1148 PTC patients. Kaplan–Meier analyses showed that all clinicopathological parameters analyzed, except age and multifocality, were associated significantly with DFI. Cox regression identified tall cell PTC variant and stage as independent risk factors for DFI. When the stage was replaced with age, tumor size, and lymph node (LN) metastases in the set of covariates, the lateral LN metastases stood out as the strongest independent predictor of DFI, followed by tall cell variant and age. A noteworthy result emerging from these analyzes is that regression models had lower Akaike and Bayesian information criterions if variables were categorized based on the TNM-7. In addition, we examined data from a different PTC patient cohort, acquired from The Cancer Genome Atlas database, to verify whether the DFI prediction could be enhanced by further clinicopathological and molecular parameters. However, none of these was found to be a significant predictor of DFI in the Cox model. [ABSTRACT FROM AUTHOR]