Machine learning risk stratification strategy for multiple myeloma: Insights from the EMN–HARMONY Alliance platform

Traditional risk stratification in multiple myeloma (MM) relies on clinical and cytogenetic parameters but has limited predictive accuracy. Machine learning (ML) offers a novel approach by leveraging large datasets and complex variable interactions. This study aimed to develop and validate novel ML-driven prognostic scores for newly diagnosed MM (NDMM), with the goal of improving upon existing ones. To this end, we analyzed data from the EMN–HARMONY MM cohort, comprising 14,345 patients, including 10,843 NDMM patients enrolled across 16 clinical trials. Three ML models were developed: (1) a comprehensive model incorporating 20 variables, (2) a reduced model including six key variables (age, hemoglobin, β2-microglobulin, albumin, 1q gain, and 17p deletion), and (3) a cytogenetics-free model. All models were internally validated using out-of-bag cross-validation and externally validated with data from the Myeloma XI trial. Model performance was evaluated using the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (ROC-AUC). The comprehensive model achieved C-index values of 0.666 (training) and 0.667 (test) for overall survival (OS) and 0.620/0.627 for progression-free survival (PFS). The reduced model maintained accuracy (OS: 0.658/0.657; PFS: 0.608/0.614). The cytogenetics-free model showed C-index values of 0.636/0.643 for OS and 0.600/0.610 for PFS. Incorporating treatment type and best response to first-line treatment further improved performance. The new prognostic models improved over the International Staging System (ISS), Revised International Staging System (R-ISS), and Second Revision of the International Staging System (R2-ISS) and were reproducible in real-world and relapsed/refractory MM, including daratumumab-treated patients. This ML-based risk stratification strategy provides individualized risk predictions, surpassing traditional group-based methods and demonstrating broad applicability across patient subgroups.