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

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.