RT Journal Article
T1 Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group.
A1 Mosquera Orgueira, Adrian
A1 Gonzalez Perez, Marta Sonia
A1 Diaz Arias, Jose
A1 Rosiñol, Laura
A1 Oriol, Albert
A1 Teruel, Ana Isabel
A1 Martinez Lopez, Joaquin
A1 Palomera, Luis
A1 Granell, Miguel
A1 Blanchard, Maria Jesus
A1 de la Rubia, Javier
A1 Lopez de la Guia, Ana
A1 Rios, Rafael
A1 Sureda, Anna
A1 Hernandez, Miguel Teodoro
A1 Bengoechea, Enrique
A1 Calasanz, Maria Jose
A1 Gutierrez, Norma
A1 Martin, Maria Luis
A1 Blade, Joan
A1 Lahuerta, Juan-Jose
A1 San Miguel, Jesus
A1 Mateos, Maria Victoria
K1 Humans
K1 Multiple Myeloma
K1 Neoplasm Staging
K1 Prognosis
K1 Risk Assessment
K1 Unsupervised Machine Learning
AB 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.
PB Nature Publishing Group
YR 2022
FD 2022-03-11
LK http://hdl.handle.net/10668/19515
UL http://hdl.handle.net/10668/19515
LA en
NO Mosquera Orgueira A, González Pérez MS, Diaz Arias J, Rosiñol L, Oriol A, Teruel AI, et al. Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group. Blood Cancer J. 2022 Apr 25;12(4):76.
DS RISalud
RD Apr 10, 2025