E Smedby, Karin

PURPOSE In large B-cell lymphoma (LBCL), use of high-dose methotrexate (HD-MTX) to prevent CNS relapse (so-called CNS prophylaxis) remains controversial. International guidelines continue to recommend HD-MTX in patients deemed ultra high risk (UHR) because of a lack of robust data specific to these subgroups. This study was designed to examine the impact of HD-MTX specifically in UHR patients. METHODS Data from two previous retrospective analyses were combined to produce a cohort of UHR patients (≥1 of the following criteria: CNS international prognostic index score 5-6; testicular, renal/adrenal, or breast involvement; ≥3 extranodal sites), treated with or without HD-MTX. The primary outcome was CNS relapse rate (isolated or concurrent with systemic relapse) measured from diagnosis with additional landmark analysis of all responding patients with no progression event at 6 months. RESULTS Analyses were performed on 1,923 patients meeting UHR criteria. No significant difference in 3-year CNS relapse rate was observed between no HD-MTX (n = 1,051) versus HD-MTX patients (n = 872), including in multivariable analyses adjusting for baseline characteristics (3-year rate, 9.3% v 8.1%; adjusted hazard ratio [HR], 1.13 [95% CI, 0.82 to 1.57]). Analyses restricted to isolated CNS relapse also confirmed no difference (5.9% v 5.7%; adjusted HR, 1.03 [95% CI, 0.69 to 1.53]). In the landmark analysis (no HD-MTX n = 782, HD-MTX n = 773), no difference in 3-year CNS relapse was observed (6.7% v 6.6%, adjusted HR, 0.95 [95% CI, 0.62 to 1.44]). CONCLUSION To our knowledge, this is the largest international comparative data set of patients with LBCL with UHR features showing no significant reduction in CNS relapse with HD-MTX in all UHR or in any individual subgroup. Despite inherent limitations of retrospective analyses, the data strongly support previous studies in suggesting HD-MTX prophylaxis has no meaningful benefit for most patients.

PURPOSE Although most patients with follicular lymphoma (FL) can expect an indolent course, progressive lymphoma remains the primary cause of death during the first decade after diagnosis. Progression of disease within 24 months (POD24) of starting first-line (1L) immunochemotherapy defines a high-risk population with poor survival, but better risk stratification at diagnosis is needed. METHODS The FLIPI24 model was developed and internally validated to predict 24-month event rates using individual data from 4,485 patients treated with 1L immunochemotherapy from 10 observational cohorts of FL. Overall and cause-specific survival was further evaluated in FLIPI24 risk groups. External validation in the 1L immunochemotherapy setting was performed using the prospective observational Lymphoma Epidemiology of Outcomes (LEO) cohort (N = 565) and three randomized phase III trials (N = 3,192); extension to all patients with FL (any 1L therapy) was performed in the LEO cohort (N = 1,445) and its Molecular Epidemiology Resource subcohort (N = 1,074). RESULTS The FLIPI24 model uses age and four blood-based variables (hemoglobin, lactate dehydrogenase, beta-2 microglobulin, and WBC count). FLIPI24 showed consistent performance across validation and extension data sets, which was superior to existing prognostic tools. Across the four external immunochemotherapy validation data sets, patients with high-risk FLIPI24 (23%-32% of patients) had significantly higher 24-month event rates (22%-35%) and inferior 5-year overall survival (77%-83%) compared with patients with low-risk FLIPI24 (29%-31% of patients, 24-month event rates: 10%-12%; 5-year OS: 96%-97%). Results were consistent when evaluating lymphoma-related death and when extended to all patients with FL. CONCLUSION The FLIPI24 model robustly stratifies, at diagnosis, patients with FL at increased risk of lymphoma-related death versus patients with very low lymphoma-related mortality during the first decade after diagnosis. FLIPI24 can be used to enrich future clinical trial designs in newly diagnosed FL.

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.