The Diagnostic Value of Chromogranin A in Neuroendocrine Neoplasms is Potentiated by Clinical Factors and Inflammatory Markers

Abstract

Objective: Neuroendocrine neoplasms (NENs) are a heterogenous group of indolent tumors, with variable clinical behavior and steadily rising incidence. The aim of this study is to investigate the clinical and laboratory factors that contribute in predicting the aggressiveness and invasiveness of NENs. Special focus is given to clinical parameters that would enhance the diagnostic value of chromogranin A (CgA), via formalizing an integrated probability model, which would contribute to the timely and accurate identification of patients at high risk for metastatic disease at initial diagnosis. Designs and Methods: We identified a total of 93 patients with NENs, recruited at a specialized academic center in Athens, Greece. Anthropometric, clinical, laboratory, and pathological data were obtained from every patient before any therapeutic intervention. Results: Age over 50 years and male gender were accompanied by increased risk for metastases at the time of initial diagnosis. Additionally, when these parameters were combined with CgA levels, they were shown to enhance the predictive capacity of CgA. Different patient scenarios combining age, gender, and CgA levels are associated with different probabilities for metastatic disease, demonstrated schematically in a gradually escalating model, as age and CgA levels increase in both males and females. The lowest risk is observed in women aged <50 years old with CgA levels <200 ng/dl (6.5%), while the highest one is in males over 50 years old with CgA > 200 ng/dl (62.9%). Finally, it was shown that c-reactive protein (CRP) can predict disease extent at the time of diagnosis. Conclusions: CgA levels can not only be used as a direct predictor of tumor load in patients with NENs, but also, when interpolated with the effects of age and gender, cumulatively predict whether a NEN would be metastatic or not at the time of initial diagnosis, via a risk-escalating probability model.