{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T15:08:31Z","timestamp":1780758511410,"version":"3.54.1"},"reference-count":63,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["HemaSphere"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    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\u2010driven prognostic scores for newly diagnosed MM (NDMM), with the goal of improving upon existing ones. To this end, we analyzed data from the EMN\u2013HARMONY 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, \u03b22\u2010microglobulin, albumin, 1q gain, and 17p deletion), and (3) a cytogenetics\u2010free model. All models were internally validated using out\u2010of\u2010bag cross\u2010validation and externally validated with data from the Myeloma XI trial. Model performance was evaluated using the concordance index (C\u2010index) and time\u2010dependent area under the receiver operating characteristic curve (ROC\u2010AUC). The comprehensive model achieved C\u2010index values of 0.666 (training) and 0.667 (test) for overall survival (OS) and 0.620\/0.627 for progression\u2010free survival (PFS). The reduced model maintained accuracy (OS: 0.658\/0.657; PFS: 0.608\/0.614). The cytogenetics\u2010free model showed C\u2010index values of 0.636\/0.643 for OS and 0.600\/0.610 for PFS. Incorporating treatment type and best response to first\u2010line treatment further improved performance. The new prognostic models improved over the International Staging System (ISS), Revised International Staging System (R\u2010ISS), and Second Revision of the International Staging System (R2\u2010ISS) and were reproducible in real\u2010world and relapsed\/refractory MM, including daratumumab\u2010treated patients. This ML\u2010based risk stratification strategy provides individualized risk predictions, surpassing traditional group\u2010based methods and demonstrating broad applicability across patient subgroups. An online calculator is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/taxonomy.harmony-platform.eu\/riskcalculator\/\">https:\/\/taxonomy.harmony-platform.eu\/riskcalculator\/<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1002\/hem3.70228","type":"journal-article","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T12:55:42Z","timestamp":1760014542000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine learning risk stratification strategy for multiple myeloma: Insights from the EMN\u2013HARMONY Alliance platform"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4838-6750","authenticated-orcid":false,"given":"Adrian","family":"Mosquera Orgueira","sequence":"first","affiliation":[{"name":"Department of Hematology, IDIS University Hospital of Santiago de 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United\u00a0Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandra","family":"Larocca","sequence":"additional","affiliation":[{"name":"Department of Molecular Biotechnology and Health Sciences, Division of Hematology University of Torino Torino Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan Jos\u00e9 Lahuerta","family":"Palacios","sequence":"additional","affiliation":[{"name":"Hospital Universitario 12 de Octubre CIBER\u2010ONC CB16\/12\/00369, CNIO Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruth","family":"Wester","sequence":"additional","affiliation":[{"name":"Department of Hematology Erasmus MC Cancer Institute Rotterdam The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uta","family":"Bertsch","sequence":"additional","affiliation":[{"name":"National Center for Tumor Diseases Heidelberg Heidelberg Germany"},{"name":"Department of Medicine V, Heidelberg Myeloma Center University Hospital Heidelberg Heidelberg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anders","family":"Waage","sequence":"additional","affiliation":[{"name":"Institute of Clinical and Molecular Medicine Norwegian University of Science and Technology and St. Olavs Hospital Trondheim Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Zamagni","sequence":"additional","affiliation":[{"name":"Department of Medical and Surgical Sciences, \u201cSer\u00e0gnoli\u201d Institute of Hematology University of Bologna Bologna Italy"},{"name":"IRCCS Azienda Ospedaliero\u2010Universitaria di Bologna Bologna Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"P\u00e9rez M\u00edguez","sequence":"additional","affiliation":[{"name":"Department of Hematology, IDIS University Hospital of Santiago de Compostela Santiago de Compostela 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