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A survival prognostic model for gastro-intestinal diffuse large B-cell lymphomas: A multicenter retrospective study
Shaojie Wu1, Chengcheng Liu2, Junru Du3
1Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
Cancer Letters
|April 16, 2026
Summary
A new prognostic model accurately predicts survival for primary gastro-intestinal diffuse large B-cell lymphoma (GI-DLBCL) patients. The model uses age, Lugano stage, hemoglobin, and lactic dehydrogenase levels for improved patient outcome prediction.
Area of Science:
- Oncology
- Hematology
- Clinical Research
Background:
- Primary gastro-intestinal lymphomas (PGIL) are the most common extra-nodal lymphomas.
- Existing prognostic models for gastrointestinal diffuse large B-cell lymphoma (GI-DLBCL) require rigorous external validation.
Purpose of the Study:
- To establish and validate a robust prognostic model for primary gastro-intestinal diffuse large B-cell lymphoma (PGI-DLBCL).
- To identify key prognostic factors for survival in PGI-DLBCL patients treated with R-CHOP.
Main Methods:
- A multi-center retrospective study involving 1,023 PGIL patients from 9 Chinese medical centers.
- A training cohort of 462 PGI-DLBCL patients treated with R-CHOP and an external validation cohort of 192 PGI-DLBCL patients.
- Development of a survival prognosis nomogram model based on multivariate analysis.
Main Results:
- Patients receiving R-CHOP demonstrated significantly better survival than those receiving CHOP alone (P<0.001).
- Significant independent prognostic factors identified in the training cohort were Age (P=0.03), Lugano stage (P<0.001), Hemoglobin (P=0.04), and Lactic dehydrogenase (LDH) (P<0.001).
- The developed nomogram model achieved high predictive accuracy with C-statistics of 0.83 in the training cohort and 0.81 in the validation cohort.
Conclusions:
- The novel 4-variable nomogram model (Age, Lugano stage, LDH, Hemoglobin) is effective for predicting survival in PGI-DLBCL patients.
- This validated model offers a valuable tool for clinical decision-making and patient management in PGI-DLBCL.

