Abstract
Medullary thyroid carcinoma (MTC) is a rare (~2-5% of all thyroid cancers) neuroendocrine thyroid malignancy originating from parafollicular C-cells of the thyroid gland with variable biological behavior and potential for early metastasis. Diagnosis, staging, and surveillance are heavily reliant on circulating biomarkers. We aimed to provide a comprehensive overview of circulating biomarkers in the management of MTC and propose an integrated, evidence-based algorithm to guide clinical decision-making using both established and emerging biomarkers. This is a narrative review on the evolving landscape of biomarker-driven management in MTC with emphasis on analytical advancements, clinical applications, and the prognostic implications of individual and combined biomarkers. Calcitonin remains the cornerstone biomarker for MTC, and new generation immunoassays have addressed several pre-analytical and analytical challenges such as pre-analytical degradation, inter-assay variability, and biological confounders. Procalcitonin (ProCT) has emerged as a stable and less interference-prone alternative or adjunct to calcitonin, which is particularly useful in cases with indeterminate calcitonin levels. Carcinoembryonic antigen (CEA) remains a useful complementary biomarker often correlating with aggressive behavior, advanced disease, and distant metastases. Kinetic evaluation (doubling times) of calcitonin and CEA offers independent prognostic information values and those < 6 months are associated with poor survival, whereas those > 2 years suggest favorable outcomes. Newer biomarkers such as pro-gastrin-releasing peptide (ProGRP) and carbohydrate antigen 19-9 (CA19-9) show potential in monitoring advanced disease and response to therapy. Their role is still under investigation but appears promising, particularly when used in conjunction with calcitonin and CEA. Our work advances a comprehensive and clinically pragmatic framework for the management of MTC by integrating established and emerging biomarkers with evidence-based algorithms, offering greater diagnostic precision, more reliable prognostic stratification, and improved personalization of follow-up and treatment strategies.