Abstract
BACKGROUND: Thyroid nodules are a common clinical challenge, with a significant proportion being cancerous. Fine-needle aspiration cytology (FNAC) is widely used for diagnosis but has limitations. Ultrasound has emerged as a promising tool for distinguishing between benign and malignant nodules. This study aims to compare the diagnostic accuracy of ultrasonography (USG) and FNAC in diagnosing malignant thyroid swelling using postoperative histopathological examinations as the gold standard. METHOD: A diagnostic accuracy study was conducted over 1.5 years at Rajendra Institute of Medical Sciences, Ranchi, India. A total of 132 patients with thyroid swellings were included. Patients underwent USG and FNAC, and 99 patients subsequently underwent surgery and histopathological examination. Statistical analysis was performed to evaluate the performance of USG and FNAC, including sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The study encompassed 132 patients, predominantly 94 (71.21%) females. Most patients, i.e., 76 out of 132 (57.58%), were aged 30-50 years, with an average age of presentation at 41 years. Socioeconomic status revealed 120 (90.9%) belonging to Classes II and III. USG and FNAC exhibited sensitivities of 77.4% and 90.3%, specificities of 94.1% and 98.5%, and accuracies of 88.9% and 96.0%, respectively. FNAC demonstrated superior diagnostic performance metrics compared to USG, with higher PPV and NPV, indicating its stronger ability to correctly identify true-positive cases. Ultrasound features and FNAC findings showed significant associations with biopsy results, reaffirming their utility in diagnosing thyroid nodules. CONCLUSION: FNAC emerged as a highly accurate diagnostic tool for distinguishing between benign and malignant thyroid nodules, outperforming USG. Understanding demographic and clinical characteristics can aid in the timely diagnosis and management of thyroid disorders. Further research is warranted to enhance diagnostic algorithms and optimize patient care in resource-constrained settings.