Prognostic importance of the preoperative New-Naples prognostic score for patients with gastric cancer

Abstract

Background: The wide applicability of the Naples prognostic score (NPS) is still worthy of further study in gastric cancer (GC). This study aimed to construct a New-NPS based on the differences in immunity and nutrition in patients with upper and lower gastrointestinal tumors to help obtain an individualized prediction of prognosis.

Methods: This study retrospectively analyzed patients who underwent radical gastrectomy from April 2014 to September 2016. The cutoff values of the preoperative neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), serum albumin (Alb), and total cholesterol (TC) were calculated by ROC curve analysis. ROC and t-ROC were used to evaluate the accuracy of the prognostic markers. The Kaplan-Meier method and log-rank test were used to analyze the overall survival probability. Univariate and multivariate analyses based on Cox risk regression were used to show the independent predictors. The nomogram was made by R studio. The predictive accuracy of nomogram was assessed using a calibration plot, concordance index (C-index), and decision curve.

Results: A total of 737 patients were included in training cohort, 411 patients were included in validation cohort. ROC showed that the New-NPS was more suitable for predicting the prognosis of GC patients. NPS = 2 indicated a poor prognosis. Multivariate analysis showed that CEA (P = 0.026), Borrmann type (P = 0.001), pTNM (P < 0.001), New-NPS (P < 0.001), and nerve infiltration (P = 0.035) were independent risk factors for prognosis.

Conclusion: The New-NPS based on the cutoff values of NLR, LMR, Alb, and TC is not only suitable for predicting prognosis but can also be combined with clinicopathological characteristics to construct a nomogram model for GC patients.

Keywords: GC; Naples; nomogram; prognosis.

FIGURE 1

Study protocol design according to the criteria.

FIGURE 2

(A) Receiver operating characteristic (ROC) curves of the GC‐NPS and CRC‐NPS. (B) Receiver operating characteristic (ROC) curves of the NPS, SII, PLR, PNI, SIS, and BMI among all patients.

FIGURE 3

Time‐dependent ROC curves for the NPS, SII, PLR, PNI, SIS, and BMI. The horizontal axis represents the months after surgery, and the vertical axis represents the estimated AUC for survival at the time of interest.

FIGURE 4

Survival curve subgroup analyses of patients. (A) Overall patients. (B) StageI. (C) StageII. (D) StageIII. (E) Upper third. (F) Middle third. (G) Lower third.

FIGURE 5

(A) Nomogram model predicting the 3‐ and 5‐year survival of all patients. (B) ROC curve of the nomogram model predicting the 3‐year survival of all patients. (C) ROC curve of the nomogram model predicting the 5‐year survival of all patients.

FIGURE 6

(A,B) Calibration plots for the nomogram. Correlationship between the predicted probabilities based on the nomogram and actual values. (A) 3 years. (B) 5 years. (C) Concordance Index of Nomogram. (D) Decision curve analysis for 5‐year survival prediction.

FIGURE 7

Results of New‐NPS in the validation cohort. (A) Receiver operating characteristic (ROC) curves of the GC‐NPS and CRC‐NPS. (B) ROC curve of the nomogram model predicting the 3‐year survival of all patients. (C) ROC curve of the nomogram model predicting the 5‐year survival of all patients. (D) Survival curve subgroup analyses of overall patients. (E) Concordance Index of Nomogram. (F,G) Calibration plots for the nomogram. (F) 3 years. (G) 5 years. (H) Decision curve analysis for 5‐year survival prediction.