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

Abstract

[EN]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. An online calculator is available at https://taxonomy.harmony-platform.eu/riskcalculator/.