A Nomogram for Predicting Overall Survival in Primary Central Nervous System Lymphoma: A Retrospective Study.

Abstract

Purpose: Current prognostic scoring systems for newly diagnosed primary central nervous system lymphoma (PCNSL), such as IELSG prognostic score and MSKCC prognostic score, are widely used but have limitations in clinical practice. This study aimed to develop a novel prognostic model based on real clinical data and compare it with existing systems.

Patients and methods: A total of 288 patients newly diagnosed with PCNSL were recruited. Patients were randomly allocated to the development and validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to identify the risk factors for overall survival (OS) and construct a nomogram. Additionally, Kaplan-Meier survival curves were plotted to show the stratification ability of the risk groups.

Results: Eastern Cooperative Oncology Group performance status (ECOG-PS), albumin, and two inflammatory biomarkers D-Dimer, and neutrophil-to-lymphocyte ratio (NLR)-were independent predictors of inferior OS. The prognostic model demonstrated concordance Index (C-index) of 0.731 and 0.679 in the development and validation cohorts, respectively. In terms of the time dependent area under the curve (AUC) values for OS, the development cohort exhibited values of 0.765, 0.762, and 0.812 for 1-year, 3-year, and 5-year OS, respectively. The corresponding AUC values in the validation cohort were 0.711, 0.731, and 0.840, respectively. The calibration curves showed excellent concordance. The novel prognostic model also provided superior risk stratification for patients with PCNSL compared with existing scoring systems.

Conclusion: This study presents a novel prognostic model for predicting the OS of patients with newly diagnosed PCNSL. The model accurately and effectively stratifies the prognosis of patients with PCNSL and offers valuable clinical guidance for decision making.