Diagnostic Accuracy of Ultrasound and Fine-Needle Aspiration Cytology in Thyroid Malignancy.
Maria BoudinaMichael KatsamakasAngeliki ChortiPanagiotis PanousisEleni TzitziliGeorgios TzikosAlexandra ChrisoulidouRosalia ValeriAris IoannidisTheodosios S PapavramidisPublished in: Medicina (Kaunas, Lithuania) (2024)
Introduction : Thyroid nodule incidence is increasing due to the widespread application of ultrasonography. Fine-needle aspiration cytology is widely applied for the detection of malignancies. The aim of this study was to evaluate the predictive value of ultrasonography in thyroid cancer. Methods : This retrospective study included patients that underwent total thyroidectomy for benign thyroid disease or well-differentiated thyroid carcinoma from January 2017 to December 2022. The study population was divided into groups: the well-differentiated thyroid cancer group and the control group with benign histopathological reports. Results : In total, 192 patients were enrolled in our study; 159 patients were included in the well-differentiated thyroid cancer group and 33 patients in the control group. Statistical analysis demonstrated that ultrasonographic findings such as microcalcifications (90.4%), hypoechogenicity (89.3%), irregular margins (92.2%) and taller-than-wide shape (90.5%) were correlated to malignancy ( p < 0.001). Uni- and multivariate analysis revealed that both US score (OR: 2.177; p < 0.001) and Bethesda System (OR: 1.875; p = 0.002) could predict malignancies. In terms of diagnostic accuracy, the US score displayed higher sensitivity (64.2% vs. 33.3%) and better negative predictive value (34.5% vs. 24.4%) than the Bethesda score, while both scoring systems displayed comparable specificities (90.9% vs. 100%) and positive predictive values (97.1% vs. 100%). Discussion : The malignant potential of thyroid nodules is a crucial subject, leading the decision for surgery. Ultrasonography and fine-needle aspiration cytology are pivotal examinations in the diagnostic process, with ultrasonography demonstrating better negative predictive value.