Purpose: Gastric cancer (GC) has a poor prognosis and high rate of recurrence. Perineural invasion (PNI) is a prognostic factor in GC that is associated with a high risk of systemic recurrence. Preoperative identification of PNI may facilitate patient stratification and optimal preoperative treatment. We therefore developed and validated a nomogram for the preoperative prediction of PNI.
Methods: We retrospectively collected clinical data from 261 GC patients, who were randomly assigned to training (n = 185) and validation (n = 76) sets. The least absolute shrinkage and selection operator regression model was used to identify potentially relevant clinical parameters, and multivariable logistic regression analysis was used to develop the nomogram.
Results: The nomogram consisted of body mass index, immunoglobulin A level, and computed tomography-based T- and N-stages. Good calibration was observed for both the training and validation sets, with areas under the curve of 0.77 and 0.79, respectively. Decision curve analysis revealed that the nomogram was clinically relevant.
Conclusion: We developed and validated a nomogram for the preoperative prediction of PNI in patients with GC. Our nomogram may facilitate the identification of high-risk patients and optimization of preoperative decision-making.
Keywords: Nomogram; decision curve analysis; gastric cancer; perineural invasion; prediction model; preoperative decision-making.