AUTHOR=Zhong Qiaofeng , Shi Yuankai 

TITLE=Development and Validation of a Novel Risk Stratification Model for Cancer-Specific Survival in Diffuse Large B-Cell Lymphoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.582567

DOI=10.3389/fonc.2020.582567

ISSN=2234-943X

ABSTRACT=Abstract
Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogenous disease. Identifying more precise and individual survival prognostic models are still needed. This study aimed to develop a predictive nomogram and a web-based survival rate calculator that can dynamically predict the long-term cancer-specific survival (CSS) of DLBCL patients.. A total of 3573 eligible patients with DLBCL from 2004 to 2015 were extracted from the Surveillance, Epidemiology and End Results (SEER) database. The entire group was randomly divided into the training (n = 2504) and validation (n = 1069) cohorts. We identified six independent predictors for survival including age, sex, marital status, Ann Arbor stage, B symptom and chemotherapy, which were used to construct the nomogram and the web-based survival rate calculator. The C-index of the nomogram was 0.709 (95%CI, 0.692-0.726) in the training cohort and 0.700 (95%CI, 0.671-0.729) in the validation cohort. The AUC values of the nomogram for predicting the 1-, 5-, and 10- year CSS rates range from 0.704 to 0.765 in both cohorts. All calibration curves revealed optimal consistency between predicted and actual survival. A risk stratification model generated based on the nomogram score showed a favorable level of predictive accuracy compared with the IPI, R-IPI and Ann Arbor stage in both cohorts according to the AUC values (training cohort: 0.715 vs 0.676, 0.652 and 0.648; validation cohort: 0.695 vs 0.692, 0.657 and 0.624) and K-M survival curves. In conclusion, we have established and validated a novel nomogram risk stratification model and a web-based survival rate calculator that can dynamically predict the long-term CSS in DLBCL with more discriminative and predictive accuracy than the IPI, R-IPI and Ann Arbor stage in the rituximab era.