Background: Relevant serum tumor markers have been indicated to be associated with peritoneal dissemination (PD) of gastric cancer (GC). Fibrinogen has been shown to play an important role in the systemic inflammatory response (SIR) and in tumor progression. However, the clinical significance of the fibrinogen-to-lymphocyte ratio (FLR) in GC with PD has not been studied.Methods: The clinical data of 391 patients with GC were collected, including 86 cases of PD. Then, 1:3 matching was performed by propensity score matching (PSM), and the clinical data of the matched 344 patients were analyzed by univariate and multivariate conditional logistic regression. Classification tree analysis was used to obtain the decision rules and a random forest algorithm to extract the important risk factors of PD in GC. A nomogram model for risk assessment of PD in GC was established by using the rms package of R software.Results: Univariate analysis showed that the factors related to PD in GC were: carbohydrate antigen (CA) 125 (P < 0.0001), CA19-9 (P < 0.0001), CA72-4 (P < 0.0001), FLR (P < 0.0001), neutrophil-to-lymphocyte ratio (NLR) (P < 0.0001), albumin-to- lymphocyte ratio (ALR) (P < 0.0001), platelet-to-lymphocyte ratio (PLR) (P = 0.013), and carcinoembryonic antigen (CEA) (P = 0.031). Conditional logistic regression found that CA125 (OR: 1.046; P < 0.0001), CA19-9 (OR: 1.002; P < 0.0001), and FLR (OR: 1.266; P = 0.024) were independent risk factors for GC with PD. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the decision rules for detecting PD of GC were 89.5, 77.4, 94.0, 82.8, and 91.8%, respectively. According to the important variables identified by the classification tree and random forest algorithm, the risk assessment model of PD in GC was established. The accuracy, sensitivity, and specificity of the model were 91, 89.5, and 79.5%, respectively.Conclusion: CA125 > 17.3 U/ml, CA19-9 > 27.315 U/ml, and FLR > 2.555 were the risk factors for GC with PD. The decision rules and nomogram model constructed by CA125, CA19-9, CA72-4, and FLR can correctly predict the risk of PD in GC.

Background The preoperative platelet count and serum tumor markers have been shown to correlate with the lymph node metastasis (LNM) of gastric cancer (GC).The aim of this study was to establish a risk assessment model that incorporated the platelet-to-albumin ratio (PAR) for LNM of GC and to evaluate its clinical significance. Methods The clinical data of 314 patients with GC diagnosed by postoperative pathology were collected in our hospital. According to whether there was LNM in the pathological specimens of the operation, the patients were divided into the group without LNM and the group with LNM. Univariate analysis and multivariate logistic regression were used to analyze the relevant factors affecting LNM of GC and to identify independent risk factors for LNM of GC. The random forest algorithm was used to extract the important risk factors of LNM in GC. A nomogram model of the risk assessment of LNM of GC was constructed by the “rms” package of R software. The receiver operating characteristic (ROC) curve was used to evaluate the accuracy, sensitivity and specificity of the model for predicting LNM of GC. Results Univariate analysis showed that the factors associated with LNM of GC were sex (P=0.015), smoking (P=0.027), lesion size (P=0.000), pathological type (P=0.001), differentiation degree (P=0.000), infiltration depth (P=0.000), PAR (P=0.005), carbohydrate antigen (CA) 19-9 (P=0.017), CA125 (P=0.000) and CA72-4 (P=0.005). Multivariate logistic regression showed that lesion size [odds ratio (OR): 1.322; P = 0.000], differentiation degree (OR: 0.582; P = 0.001), and depth of invasion (OR: 1.734; P = 0.000) were independent risk factors for LNM in GC. The risk assessment model of LNM in GC was established according to the ranking of variables shown by the random forest algorithm. The C statistic of the model evaluation was 0.827, the sensitivity was77.2%, and the specificity was 74.8%. Conclusion Lesion diameter larger than 2.65 cm, poor differentiation and deep infiltration were high-risk factors for LNM in GC. The nomogram model constructed by PAR, lesion size, infiltration depth, CA125, CA19-9, CA72-4, and differentiation degree, can well predict the risk of LNM in GC.