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Updated: Jan 16, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Establishment and Evaluation of Prognostic Prediction Model for Diffuse Large B-Cell Lymphoma Patients Based on
Letian Shao1, Zhen Kou1, Renaguli Abulaiti1
1Department of Hematology, The People's Hospital of Xinjiang Uygur Autonomous Region, Nanjing, China.
Introduction:
Diffuse large B-cell lymphoma (DLBCL) is a common type of non-Hodgkin lymphoma with clinical and genetic heterogeneity, resulting in significant differences in patient prognosis.
Methods:
High-throughput sequencing was performed on 155 newly diagnosed DLBCL patients, and 12 genes with high mutation rates related to DLBCL were selected. Cox regression analysis was used to determine prognostic factors associated with progression-free survival (PFS) and overall survival (OS) in patients. A new prognostic model was established based on these factors, and its performance was validated using the concordance index (C-index), receiver operating characteristic curve, and calibration curve. Clinical utility was evaluated using decision curve analysis (DCA).
Results:
Multivariable Cox regression analysis showed that the prognostic factors for PFS and OS in DLBCL patients were IPI, FAT4 mutation, and TP53 mutation, leading to the development of the final prognostic model (FAT4-TP53-IPI model). The FAT4-TP53-IPI model demonstrated better discriminative ability than the IPI model, as indicated by the C-index. The calibration curve showed good discriminatory ability and accuracy, and DCA confirmed the clinical value of the FAT4-TP53-IPI model. Based on the cutoff values obtained from the FAT4-TP53-IPI model, patients were divided into two different risk groups, and survival analysis for PFS and OS demonstrated significantly worse prognosis in the high-risk group compared to the low-risk group (p < 0.01).
Conclusion:
This study demonstrates that integrating genetic mutation status enhances the prognostic value of the IPI scoring system. Our model may serve as a valuable tool for predicting the prognosis of DLBCL patients receiving rituximab-based immunotherapy.
