Year: 2025
Type: article
Study design: Observational Study
Abstract: INTRODUCTION: Paragangliomas (PGLs) are rare malignant non-epithelial neuroendocrine neoplasms characterized by a strong genetic determinism and heterogeneous metastatic potential with no reliable histopathological predictors. In this retrospective study we investigated the role of serum succinate as a biomarker for metastatic risk and developed a novel preoperative scoring tool. MATHERIALS AND METHODS: Seventy patients with PGLs evaluated between 2006 and 2023 were analysed. Clinical, biochemical, imaging, and genetic data were collected. Germline genetic variants were analysed via Sanger sequencing or NGS through a targeted panel of susceptibility genes. Serum succinate concentrations were quantified by gas chromatography-mass spectrometry. RESULTS: Succinate levels were significantly higher in patients with Cluster 1 genetic variants (p < 0.001), extra-adrenal PGLs (p = 0.006), and metastatic disease (p = 0.024). We developed a novel preoperative risk assessment tool, the P-SMART (Preoperative Succinate MetAstatic Risk Tool), combining serum succinate levels, tumour size, and location. P-SMART assigns: 3 points for extra-adrenal localization, 3.5 points for serum succinate ≥ 8.95 µM, and 3 points for tumour size ≥ 7.0 cm. In our cohort a P-SMART score > 4.75 predicted metastatic disease with 72.7% sensitivity and 83% specificity, outperforming the ASES score (Age, Size, Extra-adrenal, Secretory type; AUC 0.891 vs 0.752, p = 0.005). CONCLUSIONS: Though limited by sample size and retrospective design, our findings suggest that succinate is a minimally invasive biomarker that could enhance preoperative metastatic risk stratification, especially when integrated into a multiparametric score such as P-SMART. Larger prospective studies are needed to validate its role, but P-SMART could optimize clinical decision-making, refine patient selection for whole-body imaging, reduce unnecessary radiation exposure, and inform surveillance strategies.
Source: Endocrine Pathology
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,190
Awards: G1364224112, G1587486088
OpenAlex ID: W4414622391
