Improving Thyroid Nodule Diagnosis: A Comprehensive Investigation Utilizing Ultrasonography and Fine-Needle Aspiration Cytology at Derna Hospital in Libya

Abstract

Thyroid nodules present a common clinical challenge, ranging from benign to malignant lesions, necessitating accurate diagnostic strategies for appropriate management. This retrospective study conducted at Derna Hospital, Libya, aimed to evaluate the predictive efficacy of ultrasonography and fine-needle aspiration cytology (FNAC) in determining histological outcomes of thyroid nodules.  A cohort of 300 patients clinically diagnosed with thyroid nodules underwent thyroid function tests, ultrasonography, and FNAC. Histopathological examination was performed post-operatively for surgically managed cases. Data analysis involved comparing predicted and actual histological outcomes using various categories, assessing precision and overall accuracy. Initial evaluation showed an overall accuracy of 82%, with high precision for multinodular goiter, adenomatous nodules, and papillary thyroid carcinomas. Following re-categorization into clinically significant entities, expert ultrasound evaluation demonstrated an overall accuracy of 92% in distinguishing between colloid/adenomatoid nodules, follicular neoplasias, and papillary thyroid carcinomas, with precision values ranging from 70% to 98%. Notably, no cases of follicular, medullary, or neoplastic thyroid cancers were observed. Our findings underscore the pivotal role of ultrasonography in predicting histological outcomes of thyroid nodules, facilitating precise diagnosis and tailored management strategies. The high accuracy and precision values attained highlight the utility of expert ultrasound evaluation in enhancing diagnostic reliability, potentially reducing unnecessary surgeries and associated healthcare costs. However, challenges persist in differentiating indeterminate lesions and predicting the biological behavior of thyroid malignancies, warranting continued research efforts in thyroid pathology. Integration of advanced imaging modalities and artificial intelligence algorithms may further refine diagnostic algorithms, improving patient outcomes and optimizing clinical practice in thyroid nodule management.