Beyond traditional biopsy: integrating liquid biopsy, molecular panels, and advanced imaging techniques in thyroid cancer management.

Abstract

The incidence of thyroid cancer has increased dramatically over the past four decades, making it the most common endocrine malignancy worldwide. Although well-differentiated thyroid carcinomas generally exhibit excellent survival rates following standard surgical and radioactive iodine interventions, the clinical management of high-risk variants, recurrent disease, and indeterminate nodules remains a significant challenge. Historically, the evaluation of thyroid nodules has relied on B-mode ultrasonography and fine-needle aspiration biopsy (FNAB). However, spatial and temporal tumor heterogeneity, coupled with the high rate of indeterminate cytology (Bethesda Categories III and IV), often leads to unnecessary diagnostic thyroidectomies. In recent years, liquid biopsy has emerged as a promising diagnostic approach, offering a non-invasive, dynamic, and comprehensive method for disease monitoring. This comprehensive review evaluates the biological mechanisms and clinical utility of circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), extracellular vesicles (EVs), and non-coding RNAs in thyroid oncology. Furthermore, it explores the clinical role of commercial molecular diagnostic panels (e.g., Afirma GSC, ThyroSeq v3) in guiding surgical decisions. This review highlights the diagnostic synergy achieved when these molecular biomarkers are integrated with advanced radiological modalities - including strain elastography, quantitative region-of-interest (ROI) analysis, ACR TI-RADS, and cross-sectional volumetric tracking - as well as nuclear medicine techniques like technetium-99m MIBI scintigraphy. Emerging applications of artificial intelligence and radiomics in image analysis, alongside the concept of minimal residual disease monitoring, are also discussed. Ultimately, we emphasize the need for a multidisciplinary team (MDT) approach in translating these multi-omics and multimodal imaging data into precision medicine, while acknowledging current limitations and charting future directions.