A population-based nomogram to individualize treatment modality for pancreatic cancer patients underlying surgery

As the most aggressive tumor, TNM staging does not accurately identify patients with pancreatic cancer who are sensitive to therapy. This study aimed to identify associated risk factors and develop a nomogram to predict survival in pancreatic cancer surgery patients and to select the most appropriate comprehensive treatment regimen. First, the survival difference between radiotherapy and no radiotherapy was calculated based on propensity score matching (PSM). Cox regression was conducted to select the predictors of overall survival (OS). The model was constructed using seven variables: histologic type, grade, T stage, N stage, stage, chemotherapy and radiotherapy. All patients were classified into high-or low-risk groups based on the nomogram. The nomogram model for OS was established and showed good calibration and acceptable discrimination (C-index 0.721). Receiver operating characteristic curve (ROC) and DCA curves showed that nomograms had better predictive performance than TNM stage. Patients were divided into low-risk and high-risk groups according to nomogram scores. Radiotherapy is recommended for high-risk patients but not for low-risk patients. We have established a well-performing nomogram to effectively predict the prognosis of pancreatic cancer patients underlying surgery. The web version of the nomogram https:// rocke ric.shiny apps.io/ DynNo mapp/ may contribute to treatment optimization in clinical practice.

Automated summary

This study aimed to identify risk factors and develop a nomogram to predict survival in patients undergoing pancreatic cancer surgery. Radiotherapy was found to improve survival compared to no radiotherapy. A nomogram model was constructed using seven variables, and it showed good calibration and acceptable discrimination in predicting overall survival.