BACKGROUND

Gastric cancer (GC) is a prevalent malignant tumor of the digestive system, characterized by a poor prognosis and high recurrence rate. Perineural invasion (PNI), the neoplastic infiltration of nerves, is a significant predictor of survival outcome in GC. Accurate preoperative identification of PNI could facilitate patient stratification and optimal preoperative treatment. We therefore established and validated a preoperative risk assessment model for GC patients with PNI.

METHODS

We collected data from 1,195 patients who underwent surgical resection at our hospital between October 2020 and December 2023, with PNI confirmed by pathological examination. We gathered laboratory data, including blood cell count, blood type, coagulation index, biochemical indexes, and tumor markers. Eligible patients were randomly divided into a training set and a testing set at a ratio of 7:3. The important risk factors of PNI were evaluated by random forest package in RStudio. Receiver operating characteristic-area under the curve (ROC-AUC) analysis was used to evaluate the discriminatory ability of the factors for PNI. Univariate and multivariate logistic regression analyses were utilized to verity independent risk factors for patients with PNI, and the logistic regression model and nomogram were constructed based on the results. Calibration curve and decision curve analysis (DCA) were conducted to assess the predictive model. Finally, we verified the prediction equation model using the testing set.

RESULTS

In the training set, 416 GC patients were pathologically diagnosed with PNI. The top 5 important risk factors for PNI were identified as carcinoembryonic antigen (CEA), fibrinogen-to-lymphocyte ratio (FLR), D-dimer, platelet-to-lymphocyte ratio (PLR), and carbohydrate antigen 19-9 (CA19-9), with optimal cut-off values of 3.89 ng/mL, 2.08, 0.24 mg/L, 122.37, and 14.85 U/mL, respectively. Multivariate logistic regression analysis confirmed that CEA, FLR, D-dimer, PLR, CA19-9, and CA72-4 as independent risk factors for PNI (P<0.05). We formulated the following predictive equation: Logit(P) = -1.211 + 0.695 × CEA + 0.546 × FLR + 0.686 × D-dimer + 0.653 × PLR + 0.515 × CA19-9 + 0.518 × CA72-4 (χ=105.675, P<0.001). The model demonstrated an ROC-AUC value of 0.719 [95% confidence interval (CI): 0.681-0.757] in the training set, with a sensitivity of 68.51% and a specificity of 67.60%. The ROC-AUC value was 0.791 (95% CI: 0.750-0.831) in the testing set (sensitivity: 69.57%, specificity: 56.41%). Calibration curve and DCA confirmed that the model has good discrimination and accuracy.

CONCLUSIONS

We successfully established and validated a prediction model for GC patients with PNI based on hematological indicators, hoping that this model can provide an adjunctive tool for predicting PNI in clinical work.