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Updated: Jan 22, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A prognostic nomogram constructed for relapsed or refractory diffuse large B-cell lymphoma patients
Shiyu Jiang1, Yan Qin1, Peng Liu1
1Department of Medical Oncology, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Aim:
The clinical course of diffuse large B-cell lymphoma (DLBCL) is variable and there is a lack of prognostic markers and models for relapsed or refractory (r/r) DLBCL. Hence, we conducted this study to identify independent factors that can predict the survival rate of r/r DLBCL patients.
Methods:
A total of 416 r/r DLBCL patients who were pretreated with first-line anthracycline-based chemotherapy at the National Cancer Center in China between 2006 and 2016 were divided into the primary (n = 291) and validation (n = 125) cohorts. The effect of preclinical and clinical indicators on DLBCL survival rates of the two cohorts were evaluated by univariate and multivariate analyses. Factors showing good correlation with patient survival rates were used to construct a prognostic nomogram.
Results:
Multivariate analysis of the primary cohort revealed five independent prognostic factors: lactate dehydrogenase level at diagnosis, response to front line treatment, progression/recurrence pattern, location, and invasion on progression/recurrence, which were then used to construct a nomogram. The nomogram was shown to have a C-index of 0.76 and AUC values of 0.81 and 0.80 for the primary and validation cohorts, respectively, suggesting good prognostic power. We further stratified the r/r DLBCL patients into four risk groups according to the newly developed nomogram.
Conclusion:
The prognostic nomogram constructed using the five identified clinical indicators can potentially be applied in the clinical setting to guide treatment decision.
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