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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. Machine-Learning based advanced-stage Hodgkin lymphoma (aHL) prognosis surpasses International Prognostic Score, competes with aHL international prognostic index in overall survival and progression-free survival prediction. [ABSTRACT FROM AUTHOR]

We evaluated the predictive value of interim positon emission tomography (I-PET) after one course of chemoimmunotherapy in patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL). One hundred and twelve patients with DLBCL were enrolled. All patients had PET/computed tomography (CT) scans performed after one course of chemotherapy (PET-1). I-PET scans were categorized according to International Harmonization Project criteria (IHP), Deauville 5-point scale (D 5PS) with scores 1-3 considered negative (D 5PS > 3) and D 5PS with scores 1-4 considered negative (D 5PS = 5). Ratios of tumor maximum standardized uptake value (SUVmax) to liver SUVmax were also analyzed. We found no difference in progression-free survival (PFS) between PET-negative and PET-positive patients according to IHP and D 5PS > 3. The 2-year PFS using D 5PS = 5 was 50.9% in the PET-positive group and 84.8% in the PET-negative group (p = 0.002). A tumor/liver SUVmax cut-off of 3.1 to distinguish D 5PS scores of 4 and 5 provided the best prognostic value. PET after one course of chemotherapy was not able to safely discriminate PET-positive and PET-negative patients in different prognostic groups.