首页> 中文期刊> 《世界胃肠病学杂志:英文版》 >Nomogram based on tumor-associated neutrophil-tolymphocyte ratio to predict survival of patients with gastric neuroendocrine neoplasms

Nomogram based on tumor-associated neutrophil-tolymphocyte ratio to predict survival of patients with gastric neuroendocrine neoplasms

         

摘要

AIM To assess the predictive value of the tumor-associated neutrophil-to-lymphocyte ratio in terms of the clinical outcomes of patients with gastric neuroendocrine neoplasms after radical surgery.METHODS Data were retrospectively collected from 142 patients who were diagnosed with gastric neuroendocrine neoplasms and who underwent radical gastrectomy at our department from March 2006 to March 2015. These data were retrospectively analyzed, and a receiver operating characteristic curve analysis was used to identify the optimal value of the tumorassociated neutrophil-to-lymphocyte ratio. Univariate and multivariate survival analyses were used to identify prognostic factors. A nomogram was then applied to predict clinical outcomes after surgery.RESULTS The tumor-associated neutrophil-to-lymphocyte ratio was significantly associated with tumor recurrence, especially with liver metastasis and lymph node metastasis(P 0.05 for all). A multivariate Cox regression analysis identified the tumor-associatedneutrophil-to-lymphocyte ratio as an independent prognostic factor for recurrence-free survival and overall survival(P < 0.05 for both). The concordance index of the nomograms, which included the tumorassociated neutrophil-to-lymphocyte ratio, Ki-67 index, and lymph node ratio, was 0.788(0.759) for recurrence-free survival(overall survival) and was higher than the concordance index of the traditional TNM staging system [0.672(0.663)].CONCLUSION The tumor-associated neutrophil-to-lymphocyte ratio is an independent prognostic factor in patients with gastric neuroendocrine neoplasms. Nomograms that include the tumor-associated neutrophil-to-lymphocyte ratio, Ki-67 index, and lymph node ratio have a superior ability to predict clinical outcomes of postoperative patients.

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