Clinical characteristics, prognostic factors, and a prediction model for overall survival of patients with primary gastrointestinal lymphoma: a single-center retrospective study

Background The gastrointestinal tract is a common site of extranodal non-Hodgkin lymphoma (NHL), with diffuse large B-cell lymphoma (DLBCL) representing the predominant pathological subtype of primary gastrointestinal lymphoma (PGIL). The International Prognostic Index (IPI) and National Comprehensive Cancer Network-IPI (NCCN-IPI) are widely used for prognostic assessment. However, primary gastrointestinal-diffuse large B-cell lymphoma (PGI-DLBCL) differs clinically from intranodal DLBCL, and its prognostic factors and optimal management strategies remain inadequately defined. Comprehensive characterization of the clinical features and development of effective predictive models are essential to improve the understanding and prognostic evaluation of PGI-DLBCL. Methods This retrospective study analyzed the clinical data of 253 patients newly diagnosed with PGIL between December 2012 and June 2021, with a particular focus on the DLBCL subtype. Among the 146 patients with DLBCL, patients were randomly assigned to training and validation cohorts at an 8:2 ratio using a randomization seed of “1234”. Independent prognostic factors were identified using Cox proportional hazards regression analyses. A novel prognostic model was subsequently developed to predict overall survival (OS) at 12, 36, and 60 months. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC) values, calibration curves, and Kaplan–Meier survival analyses. Results Age and the monocyte-to-high-density lipoprotein ratio (MHR) were identified as independent prognostic factors in patients with PGI-DLBCL. Compared with the IPI and NCCN-IPI, the nomogram demonstrated improved predictive performance, particularly for estimating 36- and 60-month OS, as indicated by ROC curve analyses. Conclusion This study systematically characterized the clinical features of PGIL and developed a novel prognostic model for PGI-DLBCL, which may improve the accuracy of patient outcome prediction.