Construction and validation of a risk prediction model for perineural invasion in early gastric cancer based on multivariate analysis.

[OBJECTIVE] We aimed to identify independent risk factors for perineural invasion (PNI) in early gastric cancer (EGC) and to construct the first individualized nomogram for predicting PNI risk.

[METHODS] We retrospectively analyzed clinicopathological data from 416 EGC patients who underwent radical gastrectomy between December 2019 and August 2025. The optimal set of risk predictors for PNI was selected using the LASSO regression model with ten-fold cross-validation. Independent risk factors were subsequently identified via multivariable logistic regression analysis. For internal validation, we randomly selected 30% of the sample as a validation set using R software (version 4.4.2). The model's performance was comprehensively evaluated by assessing its discrimination (area under the receiver operating characteristic curve, AUC), calibration (Hosmer-Lemeshow test and calibration curve), and clinical utility (decision curve analysis, DCA).

[RESULTS] A total of 416 patients were included in the final analysis, among whom 30 (7.21%) had PNI. LASSO regression analysis identified eight predictors for PNI: age, CEA level (ng/mL), tumor location, maximum tumor thickness, tumor differentiation, lymphovascular invasion, Lauren classification, and pT stage. These variables were subsequently incorporated into a multivariable logistic regression model. The analysis revealed that age (OR = 1.105, 95% CI: 1.029-1.187, P = 0.006), CEA level (OR = 59.489, 95% CI: 3.456-1023.871, P = 0.005), maximum tumor thickness (OR = 38.807, 95% CI: 3.408-441.872, P = 0.003), and lymphovascular invasion (OR = 4.131, 95% CI: 1.337-12.768, P = 0.014) were independent risk factors for PNI in EGC (all P < 0.05). The nomogram demonstrated strong discriminative ability, with AUC values of 0.895 (95% CI: 0.839-0.950) in the training cohort and 0.783 (95% CI: 0.625-0.940) in the validation cohort. The Hosmer-Lemeshow test indicated good model calibration in both the training (χ² = 11.994, P = 0.152) and validation cohorts (χ² = 3.833, P = 0.872). DCA showed substantial clinical net benefits across a wide range of threshold probabilities.

[CONCLUSION] In conclusion, this study identified age, CEA level, maximum tumor thickness, and lymphovascular invasion as independent predictors of PNI in EGC. We developed the first nomogram for individualized PNI risk assessment, which demonstrated strong predictive performance, good calibration, and clinical usefulness. Although this tool offers a reliable approach for personalized risk evaluation, further multicenter validation is necessary to enhance its clinical applicability.