Objective: To construct a nomogram-based model for predicting the postoperative survival of patients with linitis plastica of the stomach, and verify its accuracy. 
Methods: The clinical and pathological data of 203 patients with gastric linitis plastica who underwent R0 resection in the Department of Gastric Surgery of Fujian Medical University Union Hospital from December 2008 to September 2014 were collected. The independent prognostic factors were determined by Cox regression analysis, and then the nomogram predictive model was constructed by using R software. Further, the accuracy of the model in predicting the prognosis of patients with gastric linitis plastica was analyzed.
Results: In the whole group of patients, 152 cases were males and 51 cases were females, with an average age of 60.3 (21–89) years; 25 cases (12.3%) had stage II disease and 178 cases (87.7%) had stage III disease; the median follow-up time was 38 (2–111) months; the 3- and 5-year overall survival rates were 31.2% and 18.7% respectively. Results of multivariate analysis showed that BMI (P=0.006), tumor differentiation (P=0.042), T stage (P=0.032), N stage (P=0.032) and ASA score (P=0.016) were independent prognostic factors. Based on these independent risk factors, a nomogram model was established. The risk stratification analysis of the patients according to their scores from the nomogram showed that there was a significant statistical difference among high-risk group (>16), intermediate-risk group (>8–16) and low-risk group (≤8) (P<0.001). The values of χ2 for linear trend, likelihood ratio, and Akaike information criterion of the nomogram were all superior to those of the 7th edition AJCC staging system (68.99 vs. 58.58, 70.18 vs. 58.36, 1 473.38 vs. 1 485.04).
Conclusion: The established nomogram can effectively predict the postoperative survival of the patients with gastric linitis plastica, and its accuracy is better than that of the 7th edition AJCC staging system. However, but the results still need to be further verified by large sample size and multicenter studies.