Ultrasound-guided fine-needle aspiration cytology combined with washout fluid thyroglobulin measurement in the diagnosis of cervical lymph node metastasis in papillary thyroid carcinoma: research advances

Preoperative accurate assessment of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) is essential for optimal surgical planning and reducing recurrence risk. Conventional ultrasound has limited sensitivity, and although fine-needle aspiration cytology (FNAC) demonstrates high specificity (up to 97%), its diagnostic performance is compromised by sampling errors, cystic degeneration, and micrometastases, resulting in a false-negative rate of 10%–30%. Thyroglobulin measurement in fine-needle aspiration washout fluid (FNA-Tg), with its tissue-specific biochemical advantages and reported sensitivity of up to 92%, serves as an ideal complementary modality. This review systematically consolidates the theoretical foundations and evidence regarding the combination of FNAC and FNA-Tg for cervical LNM in PTC. These two modalities are orthogonal in their diagnostic dimensions—FNAC provides cytomorphological evidence, whereas FNA-Tg offers biochemical evidence of tissue origin—forming the basis for the incremental value of the combined strategy. Meta-analyses demonstrate a pooled sensitivity of 94% (95% CI: 91%–96%) and a negative predictive value exceeding 95%, with benefits particularly pronounced in suspicious/indeterminate cytology, micro-lymph nodes (<5 mm), and TgAb-positive patients. However, a negative combined FNAC/FNA-Tg result should be interpreted in the context of ultrasound features and clinical risk, because it reduces but does not definitively exclude the possibility of micrometastasis, particularly in persistently suspicious or small lymph nodes. However, translation to routine practice faces challenges, including lack of standardized FNA-Tg assays, inconsistent cut-off values, and absence of external quality assessment programs. Future efforts should prioritize multicenter collaboration and prospective validation under a unified framework, establishing a cut-off value reference system, and exploring artificial intelligence-based multimodal fusion models. The core issue has evolved from “whether there is an incremental benefit” to “how to standardize its application.” Standardization initiatives and high-quality prospective studies remain critical for future breakthroughs.