Enhancing Diagnostic Accuracy in Bethesda III Thyroid Nodules: The Predictive Value of Serum IL-17A and Calprotectin
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: Thyroid nodules are common in clinical practice, with malignancy detected in about 5% of cases. Current risk-stratification approaches, which rely on sonographic features and cytopathological assessment, have notable limitations, especially for indeterminate nodules categorized as Bethesda III. Aims: To address these diagnostic challenges, this study evaluated the utility of preoperative serum interleukin (IL)-17A and calprotectin levels as potential biomarkers for distinguishing malignant from benign Bethesda III thyroid nodules. Study Design: This single-center prospective methodological study. Methods: In this prospective study, 76 patients with Bethesda III nodules scheduled for thyroidectomy were enrolled. Based on histopathological findings, nodules were classified as benign (n = 41) or malignant (n = 35). Comprehensive patient information was collected, including demographics, medical history, and detailed clinical parameters related to nodule characteristics and laboratory results. Circulating biomarkers measured included thyroid-stimulating hormone, free T3, free T4, thyroglobulin (TG), anti-TG, anti-thyroid peroxidase, calcitonin, IL-17A, and calprotectin. Results: = 0.038, respectively). Receiver operating characteristic analysis demonstrated promising diagnostic performance for IL-17A [area under the curve (AUC) = 0.733] and calprotectin (AUC = 0.639). Conclusion: IL-17A and calprotectin emerge as promising biomarkers for refining Bethesda III nodule stratification. Incorporating these inflammatory markers into existing diagnostic protocols may substantially reduce unnecessary surgical interventions, thereby alleviating patient anxiety, surgical risks, and healthcare costs.
Source: Balkan Medical Journal
Authors: Muzaffer Serdar Deniz, Fatih Karataş, Öykü Uludağ
Institutions: Karabük University, Eskişehir Osmangazi University
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Funder: Gilead Sciences
OpenAlex ID: W4415099027
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