TBSRTC provides valuable information for the appropriate management of nodular thyroid lesions, both in a university and a large regional hospital.

Objective: To investigate the potential of Classification and Regression Trees (CARTs) for the diagnosis of thyroid lesions based on cell block immunocytochemistry and cytological outcome. Study Design: A total of 956 histologically confirmed cases (673 benign and 283 malignant) from patients with thyroid nodules were prepared via liquid-based cytology and evaluated; 4 additional slides were stained for cytokeratin 19 (CK-19), galectin 3 (Gal-3), Hector Battifora mesothelial cell 1 (HBME-1), and thyroglobulin. On the basis of immunocytochemistry and the cytological diagnosis, a CART algorithm was constructed and used for evaluation. Results: The major important factors contributing to the diagnostic CART model were: cytological outcome, CK-19, Gal-3, and HBME-1. The sensitivity and specificity of the cytological diagnosis were 96.27% and 88.26%, respectively (cut-off: category 3 of The Bethesda System [TBS-3]). The introduction of immunocytochemistry and the CART model increased the sensitivity and specificity to 98.88% and 99.11%, respectively. CK-19 presented the best performance for discriminating papillary thyroid carcinomas, followed by HBME-1 and Gal-3. In the TBS-2 cases, CK-19 and, subsequently, Gal-3 were important immunocytochemistry markers. Ultimately, CK-19 and HBME-1 on TBS-5 or TBS-6 cases demonstrated the best results. Conclusions: The hierarchical structure of the CART model provides a diagnostic algorithm linked with the risk of malignancy at every step of the procedure. It also provides guidance on the use of ancillary examinations as it goes by simple, human understandable rules.

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Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.

Background: This study investigates the potential of classification and regression trees (CARTs) for the evaluation of thyroid lesions. Methods:The study was performed on 521, histologically confirmed cytological specimens prepared via liquid based cytology. For each specimen, contextual and cellular morphology features were recorded by experienced cytopathologists, as described in everyday cytological practice andThe Bethesda System (TBS); these features were subsequently used to construct two CART models, viz. CART-C for the prediction of the cytological diagnosis (according to TBS) and CART-H for the prediction of the histological diagnosis (hereby expressed as either benign or malignant).Results: CART-C had no statistically significant performance from the cytologists' evaluations and CART-H had a very good predictive performance for the histological status.Conclusion: CARTs provide a methodological framework capable for data mining and knowledge extraction. They created simple human understandable rules and classification algorithms that may assist cytopathologists towards decisions based on classification steps, each one linked with a specific risk and moreover by applying cytomorphological characteristics in hierarchical order according to their importance. The two CARTs may be a useful tool for the training of nonexperienced cytopathologists; moreover, they may act as ancillary methods to avoid misdiagnoses and assist quality assurance procedures in the everyday practice of the cytopathology laboratory. K E Y W O R D S classification and regression trees, liquid based cytology, machine learning, morphology, thyroid cytopathology 1 | I N TR ODU C TI ON Fine needle aspiration (FNA) was shown to be a simple, cost-effective, well-established technique in the investigation of thyroid nodules and an optimal tool for clinical management of patients. Cytological classification of thyroid lesions using FNA material provides information concerning the risk of malignancy, thus helping the clinicians to provide appropriate management/treatment of the patient's lesions and reduce unnecessary surgical interventions. Several studies confirm the diagnostic accuracy of FNA, with both high sensitivity (80% to 90.8% 1,2 )and specificity (60% to 100% 3-5 ).The Bethesda 2009 System for Reporting Thyroid Cytopathology (TBS 2009) constitutes a well-established classification system for cytopatholgical evaluation of thyroid lesions. 6-9 Its aim is to guide cytopathologists in the classification of thyroid lesions via established criteria and provides the implied risk of malignancy, in order to guide *The first three authors had equal contribution.