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=10 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=

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 3,573 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 = 2,504) and validation (n = 1,069) 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 ranged 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 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, which revealed more discriminative and predictive accuracy than the IPI, R-IPI, and Ann Arbor stage in the rituximab era.