Machine learning enhances risk stratification and treatment failure prediction in diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma subtype worldwide. Existing prognostic models, including the National Comprehensive Cancer Network International Prognostic Index (NCCN IPI), rely on small predefined variable sets, discretized inputs, and do not incorporate longitudinal clinical histories or nonlinear relationships. To address these limitations, we present $M{L}_{\mathrm{All}}$ , a machine learning model developed using clinical and laboratory data from 14,832 patients from the Danish Lymphoid Cancer Research (DALY-CARE) resource (2005-2021). The primary objective was fixed-time risk prediction of treatment failure within 2 years of first-line therapy. Compared with the NCCN IPI at matched specificity, $M{L}_{\mathrm{All}}$ improved recall relatively by 22% (0.32-0.39), precision by 7% (0.56-0.60), and precision-recall AUC by 28% (0.46-0.59) in a blinded control population. Survival analyses were performed as a complementary evaluation and demonstrated reduced age-related bias in risk stratification. $M{L}_{\mathrm{All}}$ identified more than four times as many low-risk patients compared to the NCCN IPI, largely by correctly classifying older patients with favorable outcomes. Prognostic performance was nearly identical when evaluated on DLBCL alone and on a cohort including multiple aggressive lymphoma subtypes, suggesting that shared prognostic signals can be leveraged through joint modeling. These findings show that addressing key limitations of existing prognostic models enables more accurate and individualized risk stratification in DLBCL. $M{L}_{\mathrm{All}}$ provides a general framework for registry-based prognostic modeling that can be adapted to other lymphoma subtypes and healthcare systems.