Machine learning-based risk prediction model for central nervous system involvement in diffuse large B-cell lymphoma

Accurate prediction of CNS relapse in DLBCL remains challenging despite existing models like IPI and CNS-IPI. This study aimed to develop a machine learning (ML)-based prognostic model. A retrospective cohort of 664 R-CHOP-treated DLBCL patients was analyzed; 44 (6.6%) experienced CNS relapse at a median of 9.3 months. ML models, including Random Survival Forests (RSF) and Gradient Boosting Machines (GBM), were developed and validated using the entire cohort (<i>n</i> = 664), irrespective of CNS relapse. RSF demonstrated high discriminative ability (C-index: 0.91) and low prediction error (Integrated Brier Score [IBS]: 0.057), while GBM yielded comparable performance (C-index: 0.88, IBS: 0.042), both outperforming traditional scores such as IPI and CNS-IPI. Key predictors included extranodal site number, high-risk organ involvement, and ECOG performance status, although ECOG lost significance in Fine and Gray competing risks analysis, likely due to early mortality. ML-based models offer enhanced predictive accuracy and support personalized CNS risk assessment in DLBCL.