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s41408-022-00647-z.pdf (1.63 MB)

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2022-04-01

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cc by (c) Mosquera Orgueira, Adrian et al., 2022
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/185522

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

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https://doi.org/10.1038/s41408-022-00647-z

Resum

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.

Matèries

Mieloma múltiple, Diagnòstic, Pronòstic mèdic

Matèries (anglès)

Multiple myeloma, Prognosis, Diagnosis

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Col·leccions

Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer)

Citació

MOSQUERA ORGUEIRA, Adrian, GONZÁLEZ PÉREZ, Marta sonia, DIAZ ARIAS, Jose, ROSIÑOL DACHS, Laura, ORIOL, Albert, TERUEL, Ana isabel, MARTINEZ LOPEZ, Joaquin, PALOMERA, Luis, GRANELL, Miguel, BLANCHARD, Maria jesus, RUBIA, Javier de la, LÓPEZ DE LA GUÍA, Ana, RIOS, Rafael, SUREDA, Anna, HERNANDEZ, Miguel teodoro, BENGOECHEA, Enrique, CALASANZ, María josé, GUTIERREZ, Norma, LUIS MARTIN, Maria, BLADÉ, J. (joan), LAHUERTA, Juan jose, SAN MIGUEL, Jesús, MATEOS, María victoria, The Pethema/gem Cooperative Group. Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group. _Blood Cancer Journal_. 2022. Vol. 12, núm. 76. [consulta: 23 de gener de 2026]. [Disponible a: https://hdl.handle.net/2445/185522]

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