The MSRSGC is a valuable tool that can help to standardize reporting and stratify cases preoperatively. Having a better understanding of the indeterminate diagnoses will help further refine risk classification criteria. Cancer Cytopathol 2018. © 2018 American Cancer Society.

MicroRNA (miRNA) microarray analysis has consistently found altered expression of miRNAs in thyroid tumors, suggesting their roles in thyroid carcinogenesis. To explore whether this differential expression can be used as a diagnostic tool in surgical pathology and fine-needle aspirate (FNA) specimens, the expression of selected miRNA was evaluated by quantitative RT-PCR, using total RNA from 84 formalin-fixed paraffin-embedded tissues and 40 ex vivo aspirate specimens. miRNA from all paraffin-embedded tissues and all but one FNA sample were found to be analyzable, with paraffin sections yielding better miRNA quality. Preliminary analysis of 6 miRNAs in 10 papillary thyroid carcinoma and 10 follicular adenoma identified significant overexpression of miR-146b, -221, and -222 in papillary thyroid carcinoma (Po0.02), but not miR-146a, -155, or -187 (P40.08). The expression of these first three miRNAs was examined in a series of 5 normal thyroid, 11 hyperplastic nodules, 24 follicular adenoma, 27 classical papillary thyroid carcinoma, 5 follicular variant papillary thyroid carcinoma, 2 follicular carcinoma, and 10 encapsulated follicular lesions with partial nuclear features of papillary carcinoma. Results showed miR-146b to be most consistently overexpressed in both classical papillary carcinoma and follicular variants, whereas all other groups showed lower expression at a similar level (Po0.001 for pair-wise comparisons between papillary carcinoma and all other groups). Follicular lesions with partial features of papillary carcinoma all showed low miR-146b levels similar to other non-papillary carcinoma groups, suggesting that they are biologically distinctive from papillary carcinoma. miR-221 and miR-222 also showed higher expression in papillary carcinoma, but with substantial overlaps with the other groups. When applied to 40 FNA samples of various lesions, only miR-146b and miR-222 persisted as distinguishing markers for papillary carcinoma. We concluded that miRNAs, particularly miR-146b, might potentially be adjunct markers for diagnosing papillary thyroid carcinoma in both FNA and surgical pathology specimens.

Purpose: Indeterminate thyroid lesions on fine needle aspiration (FNA) harbor malignancy in about 25% of cases. Hemi-or total thyroidectomy has, therefore, been routinely advocated for definitive diagnosis. In this study, we analyzed miRNA expression in indeterminate FNA samples and determined its prognostic effects on final pathologic diagnosis.Experimental Design: A predictive model was derived using 29 ex vivo indeterminate thyroid lesions on FNA to differentiate malignant from benign tumors at a tertiary referral center and validated on an independent set of 72 prospectively collected in vivo FNA samples. Expression levels of miR-222, miR-328, miR-197, miR-21, miR-181a, and miR-146b were determined using reverse transcriptase PCR. A statistical model was developed using the support vector machine (SVM) approach.Results: A SVM model with four miRNAs (miR-222, miR-328, miR-197, and miR-21) was initially estimated to have 86% predictive accuracy using cross-validation. When applied to the 72 independent in vivo validation samples, performance was actually better than predicted with a sensitivity of 100% and specificity of 86%, for a predictive accuracy of 90% in differentiating malignant from benign indeterminate lesions. When Hurthle cell lesions were excluded, overall accuracy improved to 97% with 100% sensitivity and 95% specificity.Conclusions: This study shows that that the expression of miR-222, miR-328, miR-197, and miR-21 combined in a predictive model is accurate at differentiating malignant from benign indeterminate thyroid lesions on FNA. These findings suggest that FNA miRNA analysis could be a useful adjunct in the management algorithm of patients with thyroid nodules.