Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group.

Autor: Mosquera Orgueira A; Hospital Clínico Universitario Santiago de Compostela, A Coruña, Spain., González Pérez MS; Hospital Clínico Universitario Santiago de Compostela, A Coruña, Spain., Diaz Arias J; Hospital Clínico Universitario Santiago de Compostela, A Coruña, Spain., Rosiñol L; Hospital Clínic, Institut d'investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain., Oriol A; Institut Català d'Oncologia I Institut Josep Carreras, Hospital Germans Trias i Pujol, Badalona, Spain., Teruel AI; Hospital Clínico de Valencia, Valencia, Spain., Martinez Lopez J; Hospital Universitario 12 de Octubre, i+12, Complutense University, CNIO, Madrid, Spain., Palomera L; Hospital Clínico Lozano Blesa, Zaragoza, Spain., Granell M; Hospital Sant Pau, Barcelona, Spain., Blanchard MJ; Hospital Ramón y Cajal, Madrid, Spain., de la Rubia J; Hospital Doctor Peset, Valencia, Spain., López de la Guia A; Hospital Universitario La Paz, Madrid, Spain., Rios R; Hospital Virgen de las Nieves, CIBERESP, Ibs, Granada, Spain., Sureda A; Institut Català d'Oncologia-Hospitalet, IDIBELL, Universitat de Barcelona, Barcelona, Spain., Hernandez MT; Hospital Universitario de Canarias, Santa Cruz de Tenerife, Spain., Bengoechea E; Hospital de Donostia, San Sebastian, Spain., Calasanz MJ; Clínica Universidad de Navarra, CIMA, CIBERONC, IDISNA, Pamplona, Spain., Gutierrez N; Hospital Universitario de Salamanca, Instituto de Investigación Biomédica de Salamanca, Instituto de Biología Molecular y Celular del Cáncer (Universidad de Salamanca-Consejo Superior de Investigaciones Científicas), CIBERONC, Salamanca, Spain., Martin ML; Hospital Universitario 12 de Octubre, i+12, Complutense University, CNIO, Madrid, Spain., Blade J; Hospital Clínic, Institut d'investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain., Lahuerta JJ; Hospital Universitario 12 de Octubre, i+12, Complutense University, CNIO, Madrid, Spain., San Miguel J; Clínica Universidad de Navarra, CIMA, CIBERONC, IDISNA, Pamplona, Spain., Mateos MV; Hospital Universitario de Salamanca, Instituto de Investigación Biomédica de Salamanca, Instituto de Biología Molecular y Celular del Cáncer (Universidad de Salamanca-Consejo Superior de Investigaciones Científicas), CIBERONC, Salamanca, Spain. mvmateos@usal.es.
Jazyk: angličtina
Zdroj: Blood cancer journal [Blood Cancer J] 2022 Apr 25; Vol. 12 (4), pp. 76. Date of Electronic Publication: 2022 Apr 25.
DOI: 10.1038/s41408-022-00647-z
Abstrakt: The International Staging System (ISS) and the Revised International Staging System (R-ISS) are commonly used prognostic scores in multiple myeloma (MM). These methods have significant gaps, particularly among intermediate-risk groups. The aim of this study was to improve risk stratification in newly diagnosed MM patients using data from three different trials developed by the Spanish Myeloma Group. For this, we applied an unsupervised machine learning clusterization technique on a set of clinical, biochemical and cytogenetic variables, and we identified two novel clusters of patients with significantly different survival. The prognostic precision of this clusterization was superior to those of ISS and R-ISS scores, and appeared to be particularly useful to improve risk stratification among R-ISS 2 patients. Additionally, patients assigned to the low-risk cluster in the GEM05 over 65 years trial had a significant survival benefit when treated with VMP as compared with VTD. In conclusion, we describe a simple prognostic model for newly diagnosed MM whose predictions are independent of the ISS and R-ISS scores. Notably, the model is particularly useful in order to re-classify R-ISS score 2 patients in 2 different prognostic subgroups. The combination of ISS, R-ISS and unsupervised machine learning clusterization brings a promising approximation to improve MM risk stratification.
(© 2022. The Author(s).)
Databáze: MEDLINE
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