Biomarker-Based Models Utilizing the Albumin-Fibrinogen Ratio Effectively Predict Peritoneal Metastasis in Patients with Gastric Cancer: A Retrospective Study.

OBJECTIVE

Peritoneal carcinomatosis (PC) is a common pattern of recurrence in gastric cancer patients and is associated with a poor prognosis. This study aimed to evaluate the predictive value of the albumin-fibrinogen ratio (AFR) for PC in patients with gastric cancer and to develop two preoperative prediction models.

METHODS

A total of 745 gastric cancer patients were included in this study. Preoperative AFR, along with other serum markers and clinical tumor characteristics, was assessed. Univariate and multivariate logistic regression analyses were performed to determine the odds ratios (ORs) and 95% confidence intervals (CIs) of the independent variables. Propensity score matching (PSM) was used to control for potential confounders, and one-way ANOVA was conducted to evaluate differences in distribution between groups. Two prediction models incorporating the independent predictive indicators were constructed and validated via receiver operating characteristic (ROC) curves.

RESULTS

Poorly differentiated type (OR 2.679; P = 0.001), nondiffuse morphological type (OR 2.123; P = 0.040), BMI < 23.550 kg/m2 (OR 4.635; P = 0.001), AFR < 11.275 (OR 2.895; P = 0.003) and CA199 ≥ 73.615 U/mL (OR 2.040; P = 0.037) were identified as independent risk factors for PC in patients with gastric cancer. After PSM, the AFR remained the only inflammatory marker that was independently associated with PC (P = 0.003). AFR demonstrated consistent robustness in predicting PC across multiple sample sets. Among all the independent risk factors, the AFR had the highest area under the curve (AUC) for ROC analysis (AUC 0.648; 95% CI 0.580-0.715). Two combination models incorporating the AFR demonstrated enhanced predictive ability: Combination Model 1 (AUC 0.759; 95% CI 0.699-0.820) and Combination Model 2 (AUC 0.801; 95% CI 0.744-0.859).

CONCLUSIONS

The preoperative AFR serves as a useful indicator for predicting PC. Two reliable prediction models based on the AFR have been developed.