Significance of TIRADS classification in detection of thyroid gland cancer

Objective : Aim of this study was to retrospectively validate the effectiveness of TIRADS classification in diagnosis of thyroid cancer compared to cytological and pathohistological findings. Methods : This observational, retrospective study included adult patients of both genders who were diagnosed with thyroid cancer and underwent thyroidectomy. The study was conducted at the Clinic for Nuclear medicine and Endocrinology of the Clinical Center University of Sarajevo in the period from June 2018 to November 2018. All patients had ultrasound (US) and TIRADS classification, fine needle aspiration (FNA) biopsy of the suspected nodules, thyroidectomy and pathohistological (PHD) analysis. TIRADS classification was compared to the results of FNA and PHD findings. Results : A total of 100 nodules (from 76 patients) were included in the study. TIRADS classification showed that there was 1 (1.0%) nodule in TR2 class, 20 (20.0%) nodules in TR3 class, 72 (72.0%) nodules in TR4 class and 7 (7.0%) nodules in TR5 class. Comparing the results of FNA with TIRADS classification showed that there were no malignant nodules in TR2 class, in TR3 class there were 14 (70.0%) malignant nodules, in TR4 class there were 60 (83.3%) malignant nodules and in TR5 all nodules were malignant (7, 100.0%). Comparing the results of PHD with TIRADS classification showed that there were no malignant nodules in TR2 class, in TR3 class there were 17 (85.5%) malignant nodules, in TR4 class there were 71 (98.6%) malignant nodules and in TR5 class there were 7 (100.0%) malignant nodules. Conclusion : TIRADS classification showed valid efficacy in identifying malignant thyroid nodules, although fine needle aspiration remains the most effective method. With continious improvement of TIRADS classification system we can expect decrease in unnecessary thyroid biopsies and an overall improvement of thyroid cancer diagnostics. Keywords : Thyroid Nodule, Ultrasound Imaging, Thyroid Cancer, TIRADS, Risk Classification