Establishment of a stratified recurrence risk model for patients with synchronous gastrointestinal cancer peritoneal metastasis after surgery

Abstract

Abstract Purpose Peritoneal metastasis (PM) is a common distant metastasis in gastrointestinal malignancies. The combination of hyperthermic intraperitoneal chemotherapy (HIPEC) and cytoreductive surgery (CRS) has significantly increased the chances of recovery for patients suffering from peritoneal cancer. The objective of this research is to create a model for assessing the likelihood of recurrence after surgery for peritoneal metastasis in patients with concurrent gastrointestinal malignancies. This will be done by analyzing the risk factors and using the Nomogram. Methods Our study analyzed data from 5887 peritoneal metastases from the Surveillance, Epidemiology, and End Results database (SEER) from 2018-2020. Our goal was to identify predictors of overall survival (OS) using Cox regression analysis. The Nomogram model underwent validation through a calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results Multivariate Cox regression analysis identified age, tumor size, grade at diagnosis, pathology type, TNM stage, and chemotherapy as independent predictors of OS. A predictive model was constructed using these factors and visualized through the Nomogram model. The ROC curve demonstrated good discriminatory ability and discriminant performance of the Nomogram model. The calibration curve showed good agreement between actual observation and Nomogram model prediction, and DCA indicated good clinical utility. A system was developed to classify patients into three risk groups based on their likelihood of recurrence. The low-risk group had a median overall survival of 24 months, the intermediate-risk group had a median OS of 11 months, and the high-risk group had a median OS of 2 months. Conclusion A Nomogram model and corresponding recurrence risk classification system were constructed for patients with concurrent gastrointestinal malignancy, providing a risk assessment model with good clinical predictive value. With the assistance of this model, it is possible to identify patients who are at high risk and develop personalized treatment plans to meet their individual needs.