Abstract
Introduction Accurate diagnosis of deep-seated abdominopelvic masses is crucial to distinguish malignant from non-malignant lesions for proper treatment and prognosis. Ultrasonography-guided fine needle aspiration cytology (USG-FNAC) is a cost-effective and straightforward procedure that offers rapid diagnosis, facilitating early initiation of treatment. This study aimed to determine the diagnostic accuracy of USG-FNAC in comparison to the biopsy diagnosis of various abdominopelvic masses in a resource-limited setting. Materials and methods This prospective study enrolled 208 patients with clinically and ultrasonographically confirmed abdominopelvic masses over two years. Of these, 64 cases were excluded from the study because of the non-availability of biopsy specimens. The remaining 144 cases comprised 88 males and 56 females, with a male-to-female ratio of 1.57:1. Patients' ages ranged from 1.5 to 65 years, with most male patients aged 51 to 60 years and female patients aged 41 to 50 years. USG-FNAC was performed on these patients using a 22G spinal needle and a 10cc disposable syringe, and no complications were reported during the procedure. The cytological findings were compared to histopathological results when available. Dry smears were stained with May-Grunwald-Giemsa stain, while fixed smears were stained with Papanicolaou stain for cytological investigation. Results A total of 144 cases had both cytological and histological specimens available for comparison. The overall diagnostic accuracy of USG-FNAC was 90.97%, with 91.8% sensitivity for malignant lesions, 83.33% for benign lesions, and 85.7% for inflammatory lesions. Conclusions USG-FNAC provides high diagnostic accuracy for abdominopelvic masses, making it a valuable diagnostic tool in resource-limited settings. The technique allows for rapid diagnosis, triaging specimens for ancillary immunohistochemical and molecular studies, and in many cases, obviates the need for more expensive and time-consuming procedures like laparotomy and open biopsy.