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Updated: Jul 19, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Prognostic value of POD18 combined with improved IELSG in primary central nervous system lymphoma
Kai-Xin Du1,2,3, Hao-Rui Shen1,2,3, Bi-Hui Pan1,2,3
1Department of Hematology, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Purpose:
The International Extranodal Lymphoma Study Group (IELSG) score is widely used in clinical practice to stratify the risk of primary central nervous system lymphoma (PCNSL) patients. Our study aims to confirm and improve the IELSG score in PCNSL patients based on Chinese populations.
Materials And Methods:
A total of 79 PCNSL patients were retrospectively analyzed. All patients treated with high-dose methotrexate (HD-MTX)-based therapy collected clinical data. The receiver-operating characteristic (ROC) curve was used to determine the optimal cut-off values for the factors in IELSG score. Progression of disease (POD) at the most landmark time point was determine by Epanechnikov kernel and the area under the ROC curve (AUROC). Kaplan-Meier and multivariable regression methods were used to analyze survival data. Nomogram was generated for calculating the weight of each selected factor.
Results:
The traditional IELSG score had no significant difference on OS and PFS except ECOG ≥ 2 and could not stratify the risk groups in PCNSL. The improved IELSG scoring system was established, which incorporated age ≥ 54 years, ECOG ≥ 2, deep brain structure, elevated CSF protein, and LDH/ULN > 0.75. On the other hand, POD18 was identified as a new powerful prognostic factor for PCNSL. In multivariate analysis, POD18 and the improved IELSG scoring system were independent prognostic factors for OS. Nomogram including the two significant variables showed the best performance (C-index = 0.828).
Conclusions:
In this study, the IELSG score was improved and a new prognostic indicator POD18 was incorporated to construct a nomogram prognostic model, thereby further improving the predictive ability of the model.
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