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Lookup NU author(s): Professor Graham Jackson
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2025 The Author(s). HemaSphere published by John Wiley & Sons Ltd on behalf of European Hematology Association.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/.
Author(s): Mosquera Orgueira A, Gonzalez Perez MS, D'Agostino M, Cairns DA, Larocca A, Palacios JJL, Wester R, Bertsch U, Waage A, Zamagni E, Perez Miguez C, Rojas Martinez JA, Mai EK, Crucitti D, Salwender H, Dall'Olio D, Castellani G, Pineiro Fiel M, Bringhen S, Zweegman S, Cavo M, Iqbal S, Hernandez Rivas JM, Bruno B, Cook G, Kaiser MF, Goldschmidt H, Van De Donk NWCJ, Jackson G, San-Miguel JF, Boccadoro M, Mateos M-V, Sonneveld P
Publication type: Article
Publication status: Published
Journal: HemaSphere
Year: 2025
Volume: 9
Issue: 10
Print publication date: 01/10/2025
Online publication date: 09/10/2025
Acceptance date: 18/08/2025
Date deposited: 10/02/2026
ISSN (electronic): 2572-9241
Publisher: John Wiley and Sons Inc
URL: https://doi.org/10.1002/hem3.70228
DOI: 10.1002/hem3.70228
Data Access Statement: Data collected for this analysis and related documents are available upon reasonably justified request, which needs to be written and addressed to the attention of Dr Adrian Mosquera Orgueira at the following e-mail address: adrian.mosquera.orgueira@sergas.es. The HARMONY Alliance, via Dr Adrian Mosquera Orgueira, is responsible for evaluating and eventually accepting or refusing every request to disclose data and their related documents, in compliance with the ethical approval conditions, in compliance with applicable laws and regulations, and in conformance with the agreements in place with the involved subjects, the participating institutions, and all other parties directly or indirectly involved in the participation, conduct, development, management, and evaluation of this analysis.
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