US-Guided Fine-Needle Aspiration of Papillary Thyroid Microcarcinoma in Non-Screening Settings: Diagnostic Errors and Mitigation Strategies
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: Background Papillary thyroid microcarcinoma (PTMC, ≤1 cm) is increasingly diagnosed, and a subset behaves aggressively. Preoperative ultrasound (US) and fine-needle aspiration (FNA) accuracy in this size range, particularly with multinodular disease, is poorly defined outside East Asian screening cohorts. Materials and Methods We retrospectively analyzed 180 consecutive PTMC cases (2017– 2026) with preoperative US and FNA and compared them with 191 benign thyroidectomy controls. ROC analysis identified an optimal size cutoff. We compared sensitivity, specificity, PPV, NPV, and accuracy across tumor-size subgroups, before/after 2023, and with/without dominant nodules (DNs) ≥2 cm. Results ROC analysis identified 5 mm as the optimal cutoff (AUC 0.80). FNA sensitivity was 44.4% overall, falling to 14.1% for tumors <5 mm versus 61.2% for ≥5 mm, with specificity uniformly high (94.8%). US showed a similar pattern (sensitivity 23.4% vs. 64.7%) but lower specificity (69.6%), driven largely by calcification and Hashimoto’s-related hypoechogenicity. Combined FNA+US improved sensitivity to 59.4%. DNs ≥2 cm markedly reduced FNA true-positive rates (67.9% vs. 10.8%; OR 17.4). Excluding DN cases raised FNA sensitivity to 67%, NPV to 84.2%, and accuracy to 85.2%. Post-2023 experience significantly improved all metrics (sensitivity 34.4% to 54.4%). Conclusion FNA and US sensitivity for PTMC decline sharply below 5 mm and in multinodular glands with dominant nodules, while specificity/PPV remain high. A 5 mm threshold, operator experience, and selective avoidance of FNA in dominant-nodule–bearing multinodular goiters (favoring direct surgical referral) can meaningfully improve diagnostic performance and guide management.
Source: medRxiv
Authors: Imad A El Hag, Mohamed T Al Shammary, Raghad Tallab, Sharanamma Patil, Abrar Alghamdi
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7220964268
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