Effectiveness of R-CHOP versus R-CHOEP for treatment of young patients with high-risk diffuse large B-cell lymphoma: A Danish observational population-based study.

PURPOSE: Etoposide to standard R-CHOP is used for high-risk diffuse large B-cell lymphoma (DLBCL) in some countries. Due to the lack of randomized trials, a real-world data study using matching methods was used to test the potential effectiveness of R-CHOEP over R-CHOP. PATIENTS AND METHODS: This study included patients from the Danish Lymphoma Register diagnosed between 2006 and 2020 at the age of 18-60 years with de novo DLBCL and age-adjusted IPI ≥2. R-CHOEP treated patients were matched 1:1 without replacement to R-CHOP treated patients using a hybrid exact and genetic matching technique. Primary endpoints were progression-free survival (PFS) and overall survival (OS). RESULTS: In total, 396 patients were included; 213 received R-CHOEP and 183 received R-CHOP. Unadjusted 5-year PFS and OS for R-CHOEP were 69% (95% Confidence intervals [CI]; 63%-76%) and 79% (CI;73%-85%) versus 62% (CI;55%-70%) and 76% (CI;69%-82%) for R-CHOP (log-rank test, PFS p = .25 and OS p = .31). A total of 127 patients treated with R-CHOEP were matched to 127 patients treated with R-CHOP. Matching-adjusted 5-year PFS and OS were 65% (CI; 57%-74%) and 79% (CI; 72%-84%) for R-CHOEP versus 63% (CI; 55%-73%) and 79% (CI;72%-87%) for R-CHOP (log-rank test, PFS p = .90 and OS p = .63). CONCLUSION: The present study did not confirm superiority of R-CHOEP over R-CHOP for young patients with high-risk DLBCL.

Machine Learning-Based Survival Prediction Models for Progression-Free and Overall Survival in Advanced-Stage Hodgkin Lymphoma.

PURPOSE: Patients diagnosed with advanced-stage Hodgkin lymphoma (aHL) have historically been risk-stratified using the International Prognostic Score (IPS). This study investigated if a machine learning (ML) approach could outperform existing models when it comes to predicting overall survival (OS) and progression-free survival (PFS). PATIENTS AND METHODS: This study used patient data from the Danish National Lymphoma Register for model development (development cohort). The ML model was developed using stacking, which combines several predictive survival models (Cox proportional hazard, flexible parametric model, IPS, principal component, penalized regression) into a single model, and was compared with two versions of IPS (IPS-3 and IPS-7) and the newly developed aHL international prognostic index (A-HIPI). Internal model validation was performed using nested cross-validation, and external validation was performed using patient data from the Swedish Lymphoma Register and Cancer Registry of Norway (validation cohort). RESULTS: In total, 707 and 760 patients with aHL were included in the development and validation cohorts, respectively. Examining model performance for OS in the development cohort, the concordance index (C-index) for the ML model, IPS-7, IPS-3, and A-HIPI was found to be 0.789, 0.608, 0.650, and 0.768, respectively. The corresponding estimates in the validation cohort were 0.749, 0.700, 0.663, and 0.741. For PFS, the ML model achieved the highest C-index in both cohorts (0.665 in the development cohort and 0.691 in the validation cohort). The time-varying AUCs for both the ML model and the A-HIPI were consistently higher in both cohorts compared with the IPS models within the first 5 years after diagnosis. CONCLUSION: The new prognostic model for aHL on the basis of ML techniques demonstrated a substantial improvement compared with the IPS models, but yielded a limited improvement in predictive performance compared with the A-HIPI.