A site-specific risk stratification model for extranodal diffuse large B-cell lymphoma in the oral cavity and maxillofacial region
摘要
Extranodal diffuse large B-cell lymphoma (DLBCL) of the oral cavity and maxillofacial region (OC-MR) is rare compared to Waldeyer’s ring (WR) DLBCL, characterized by marked aggressiveness and prognostic heterogeneity. This study aims to develop a special risk stratification model for extranodal DLBCL in OC-MR to overcome the limitations of the conventional International Prognostic Index (IPI) to improve outcome prediction in this high-risk population.
MethodWe conducted a retrospective analysis of 76 OC-MR DLBCL patients (43 extranodal and 33 WR-DLBCL) diagnosed between January 2015 and March 2022. Survival outcomes were assessed using Kaplan-Meier methodology with log-rank testing. Prognostic factors were identified through Cox regression and LASSO analysis, with model validation via concordance index (C-index).
ResultPatients with extranodal OC-MR DLBCL demonstrated significantly greater multisite involvement (p < 0.001) and worse clinical outcomes compared to WR-DLBCL, including shorter median overall survival (17 vs. 26 months, p = 0.0329), shorter progression free survival (13 vs. 25 months, p = 0.0136) and lower objective response rates (67.4% vs. 84.4%, p = 0.003). Hypoalbuminemia (< 37 g/L), elevated C-reactive protein (> 5.83 mg/L), and high Ki67 index (> 80%) predicted poor prognosis. These clinicopathological biomarkers were integrated with IPI to develop the novel exploratory KACIPI model, which demonstrated a potentially improved discriminative ability than IPI (C-index 0.768 vs. 0.680, p < 0.001) and effectively stratified extranodal OC-MR DLBCL risk groups (p < 0.05).
ConclusionExtranodal OC-MR DLBCL exhibits distinct clinicopathological features and inferior survival outcomes. The exploratory KACIPI model may provide improved risk prediction for extranodal OC-MR DLBCL by incorporating tumor proliferation, inflammatory markers, nutritional status into conventional IPI score, enabling more accurate identification of high-risk patients.