Dhanya Ramachandran* and Madhumita Kumar
Background: Thyroid nodules are very commonly encountered in clinical practice and there exists a need to effectively diagnose malignancy. Fine Needle Aspiration Cytology (FNAC) plays an important role in the initial evaluation of these patients. However, reports of FNAC samples are limited by subjective error, reporter bias, inter-observer variability, and terminology confusion. The Bethesda System of Reporting of Thyroid Cytology (BSRTC) was developed in 2007 as consensus recommendations to overcome these limitations. Six diagnostic Categories were formed which provided the information regarding the risk of malignancy in each. This study aims to assess the accuracy of the BSRTC in the detection of malignancy at a tertiary referral institute.
Method: A cross-sectional diagnostic accuracy study of 110 patients with thyroid swelling, with diagnostic FNAC and underwent surgical intervention at a tertiary care centre between January 2015 and March 2016. The results were obtained by calculating validity parameters (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) for cytology with respect to histopathology. Rates of malignancy in each category (II, V, and VI) were also calculated.26 cases of Category III lesions were analyzed separately to calculate the rates of malignancy on histopathology in this category
Results: Validity parameters calculated for cytology with respect to histopathology showed sensitivity of 66%, specificity of 88%, positive predictive value of 87%, negative predictive value of 69%, and accuracy of 76%. 20 of the 64 patients in Category II (31%) were diagnosed to be malignant on histopathology. 25 of 29 in Category V lesions (86%), and 16 of the 17 Category VI lesions (94%) were malignant on histopathology. Of the 26 patients in Category III, 12 (46%) were benign and 14 (54%) were malignant
Conclusion: The risk of malignancy in each of the six diagnostic categories should be independently defined at each institution
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