Outcome of Thyroidectomy Procedures and Accuracy of Fine-Needle Aspiration Biopsy with Postoperative Histopathological Reports: A Retrospective Study
Year: 2026
Type: article
Study design: Observational Study
Abstract: Background: Fine-needle aspiration biopsy (FNAB) is widely used for preoperative thyroid nodule evaluation, though it has recognized limitations. Objective: This study described histologic patterns and evaluated FNAB accuracy in thyroidectomy patients. Methods: A retrospective study was conducted at Ibb University Hospital, Yemen, from January 2014 to December 2022. All consecutive patients who underwent thyroidectomy during the study period were included. FNAB performance metrics (sensitivity, specificity, positive predictive value [PPV], negative predictive value [NPV], and overall accuracy) were calculated against final histopathology. Results: Of 260 patients, the mean age was 40.1 ± 13.2 years; 234 (90.0%) were female. Benign disease comprised 190 cases (73.1%), with nodular colloid goiter being the most frequent diagnosis (63.1%). Malignancy occurred in 70 patients (26.9%), predominantly papillary thyroid carcinoma (53/70; 75.7%). Total thyroidectomy was the most common operation (45.0%). Postoperative complications were infrequent, occurring in 12 patients (4.6%), most commonly transient hypocalcemia (1.5%). Overall FNAB diagnostic accuracy was 88.1%, with a sensitivity of 81.4%, specificity of 99.5%, PPV of 98.3%, and NPV of 93.6%. Notably, the FNAB sensitivity for follicular thyroid carcinoma was markedly lower (40.0%). Conclusions: Nodular colloid goiter and papillary thyroid carcinoma were the most common diagnoses. FNAB showed high accuracy and specificity but limited sensitivity for follicular carcinoma. Postoperative complication rates were low, indicating a safe procedure. These findings support ongoing use of FNAB for preoperative assessment, though its limitations for follicular lesions should be recognized. The study was limited by its retrospective, single-center design and use of descriptive cytology without formal Bethesda classification.
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7202296196