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.
Minimal relapse risk and early normalization of survival for patients with Burkitt lymphoma treated with intensive immunochemotherapy: an international study of 264 real‐world patients
SummaryNon‐endemic Burkitt lymphoma (BL) is a rare germinal centre B‐cell‐derived malignancy with the genetic hallmark of MYC gene translocation and with rapid tumour growth as a distinct clinical feature. To investigate treatment outcomes, loss of lifetime and relapse risk in adult BL patients treated with intensive immunochemotherapy, retrospective clinic‐based and population‐based lymphoma registries from six countries were used to identify 264 real‐world patients. The median age was 47 years and the majority had advanced‐stage disease and elevated LDH. Treatment protocols were R‐CODOX‐M/IVAC (47%), R‐hyper‐CVAD (16%), DA‐EPOCH‐R (11%), R‐BFM/GMALL (25%) and other (2%) leading to an overall response rate of 89%. The two‐year overall survival and event‐free survival were 84% and 80% respectively. For patients in complete remission/unconfirmed, the two‐year relapse risk was 6% but diminished to 0·6% for patients reaching 12 months of post‐remission event‐free survival (pEFS12). The loss of lifetime for pEFS12 patients was 0·4 (95% CI: −0·7 to 2) months. In conclusion, real‐world outcomes of adult BL are excellent following intensive immunochemotherapy. For pEFS12 patients, the relapse risk was low and life expectancy similar to that of a general population, which is important information for developing meaningful follow‐up strategies with increased focus on survivorship and less focus on routine disease surveillance.