Prognostic factors and clinical survival outcome in patients with primary mediastinal diffuse large B-cell lymphoma in rituximab era: A population-based study

Abstract

The goal of this study was to investigate the clinical characteristics, prognostic variables, and survival of patients with primary mediastinal diffuse large B cell lymphoma (PMBCL) in the rituximab era. The Surveillance, Epidemiology, and End Results (SEER) database was used to identify PMBCL patients diagnosed between 2000 and 2019. The Kaplan–Meier (K-M) technique and log-rank test were used to assess overall survival (OS) and disease-specific survival (DSS). The independent prognostic variables for OS and DSS were identified using univariate and multivariate Cox regression analysis. Nomograms were created to predict survival prospects according to identified prognostic indicators. Totally, 841 patients were enrolled with PMBCL. One-year, 5-year, and 10-year OS rates were 93.99%, 85.04%, and 81.76%, and the corresponding DSS rates were 95.27%, 87.37%, and 85.98%. The results of multivariate Cox regression analysis demonstrated that age, years of diagnosis, Ann arbor staging, and chemotherapy were independent prognostic factors for survival. Nomograms designed exclusively for PMBCL were created to forecast the likelihood of 1-year, 5-year, and 10-year OS and DSS, respectively. The Harrell concordance index (C-index) for the nomograms predictions of OS and DSS were 0.704 and 0.733, respectively, which showed the established model harboring powerful and accurate performance. The present study revealed that incidence of PMBCL has been consistently rising over the last 20 years. Simultaneously, survival rates have improved tremendously. Rituximab based immunochemotherapy has emerged as an effective treatment option, leading to enhanced OS and DSS outcomes. Furthermore, the nomograms specifically developed for PMBCL have demonstrated robustness and accuracy in forecasting OS and DSS rates at 1, 5, and 10 years. These predictive tools can be valuable for clinicians in accurately estimating prognosis and establishing personalized treatment plans and follow-up protocols.