Indeterminate thyroid nodules ( TIR TIR 3A/ TIR TIR 3B) according to the new Italian reporting system for thyroid cytology: A cytomorphological study

摘要

Abstract Objective The Italian reporting system for thyroid cytology classifies indeterminate lesions as TIR 3A (low risk) or TIR 3B (high risk) and is meant to provide practical guidance rather than a detailed consideration of morphological features. We aimed to assess which cytological features have the most diagnostic value and whether they are effective in classifying nodules as either TIR 3A or TIR 3B and in predicting histological outcomes. Methods Thyroid fine‐needle aspirates from 111 indeterminate nodules were reviewed blinded to clinical information, TIR 3A/ TIR 3B classification, and histology in order to assess which cytological features (pooled into artefacts, smear background, architectural and nuclear atypia, and oncocytes) differentiate TIR 3A from TIR 3B, and benign from malignant histological outcomes. Results Of the cytological features examined, those specific for TIR 3B included high cellularity, nuclear atypia, oncocyte predominance and transgressing vessels. Features specific for TIR 3A included artefacts, low cellularity and oncocyte sparseness. Other features, such as microfollicules/trabeculae, were non‐specific. Due to the different distributions of these features, three TIR 3B subgroups were identifiable: follicular lesions with oncocytic changes, pure follicular lesions, and follicular lesions with nuclear atypia, whereas no subgroups were identifiable in TIR 3A. Nuclear atypia was a significant indicator of malignancy, whereas oncocyte predominance was not a reliable predictor of malignancy. High cellularity and microfollicules/trabeculae were not indicative of any histological outcome. Conclusions The majority of the assessed features were good predictors of histological outcomes. The TIR 3A category included undefined nodules due to the absence of characterising features, whereas the TIR 3B category included nodules with a greater number of distinguishing features.