# Connecting systemic inflammation to prognosis in neuroendocrine neoplasms: a biomarker-based approach

## TL;DR

This study finds that blood-based inflammation markers like SII, NLR, and PLR can predict survival in neuroendocrine tumor patients, offering affordable tools for prognosis.

## Contribution

The study introduces systemic inflammation indices as novel, cost-effective biomarkers for predicting mortality in neuroendocrine neoplasms.

## Key findings

- Elevated SII, NLR, and PLR were significant predictors of mortality in NEN patients.
- Multivariate analysis confirmed SII as a key marker associated with increased mortality.
- Combining these markers with clinicopathological data improves risk stratification.

## Abstract

Neuroendocrine neoplasms (NENs) comprise a diverse group of tumors with distinct clinical courses and prognoses. However, there remains a need to identify affordable biomarkers that can augment traditional prognostic indicators.

To address this clinical need, we performed a retrospective analysis of 60 patients with NENs treated at the University Emergency Hospital of Bucharest (2016–2023). The baseline neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) were derived from routine blood counts and were associated with demographic, histopathological, and mortality during follow-up. Logistic regression models assessed prognostic significance.

Of the 60 patients, 38.3% died during follow-up. Univariate analysis identified low lymphocyte counts, elevated platelet counts, PLR, NLR and SII as significant predictors of mortality. Building upon these findings, multivariate models revealed that advanced age, poorly differentiated NEC histology, and elevated SII were significantly associated with increased mortality.

In summary, this study identifies SII, NLR and PLR as accessible, reliable, and cost-effective biomarkers with prognostic value in NENs. Thus, integrating these indices with established clinicopathological features may improve risk stratification and inform personalized management. Nonetheless, validation in larger, prospective cohorts is necessary to substantiate these findings.