Prognostic value of systemic inflammation score in patients with esophageal cancer

Abstract

Introduction: The systemic inflammatory score (SIS), a new inflammatory marker based on a combination of the lymphocyte-to-monocyte ratio (LMR) and serum albumin concentration, has been reported to be a useful prognostic marker for several malignancies. The authors conducted this retrospective study on data from a cohort of esophageal cancer patients undergoing potentially curative resection to clarify the value of SIS as a prognostic marker for clinical outcome in this population. Methods: This retrospective cohort study included 32 patients who underwent thoracoscopic esophagectomy after neoadjuvant chemotherapy for esophageal cancer between January 2016 and December 2019. Blood samples were collected within one week prior to the initiation of preoperative chemotherapy. Three inflammatory and nutritional markers; SIS, the neutrophil-to-lymphocyte ratio (NLR), and prognostic nutrition index (PNI) were examined in this study. Disease-free survival was assessed using the Kaplan–Meier method, and univariable and multivariable Cox models were applied to evaluate the predictive value of SIS, NLR and PNI. Results: NLR and PNI were not associated with recurrence, while SIS scores of 1 and 2 were significantly associated with recurrence. In multivariate analysis, SIS scores of 1 or 2 were found to be independently associated with recurrence, each with a hazard ratio of 1.98. In addition, when examining immunologic and nutritional factors and survival rates, there was no significant difference in the survival rate for NLR and PNI; for SIS, however, the survival rate was significantly worse in patients with SIS scores of 1 or 2. Conclusions: The authors demonstrated that a novel and easily obtained prognostic score, termed SIS, based on pre-treatment serum albumin and LMR, can serve as an independent prognostic factor in postoperative esophageal cancer patients. It could be incorporated into conventional clinical and pathological algorithms to enhance the prognostic accuracy in this population.