Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group
Por:
Orgueira, AM, Perez, MSG, Arias, JD, Rosinol, L, Oriol, A, Teruel, AI, Lopez, JM, Palomera, L, Granell, M, Blanchard, MJ, de la Rubia, J, de la Guia, AL, Rios, R, Sureda, A, Hernandez, MT, Bengoechea, E, Calasanz, MJ, Gutierrez, N, Martin, ML, Blade, J, Lahuerta, JJ, San Miguel, J, Mateos, MV
Publicada:
25 abr 2022
Resumen:
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.
Filiaciones:
Orgueira, AM:
Hosp Clin Univ Santiago Compostela, La Coruna, Spain
Perez, MSG:
Hosp Clin Univ Santiago Compostela, La Coruna, Spain
Arias, JD:
Hosp Clin Univ Santiago Compostela, La Coruna, Spain
Rosinol, L:
Inst Invest Biomed August Pi i Sunyer, Hosp Clin, Barcelona, Spain
Oriol, A:
Hosp Germans Trias i Pujol, Inst Josep Carreras, Inst Catala Oncol, Badalona, Spain
Teruel, AI:
Hosp Clin Valencia, Valencia, Spain
Lopez, JM:
Univ Complutense Madrid, CNIO, Hosp Univ 12 Octubre, Madrid, Spain
Palomera, L:
Hosp Clin Lozano Blesa, Zaragoza, Spain
Granell, M:
Hosp Santa Creu & Sant Pau, Barcelona, Spain
Blanchard, MJ:
Hosp Ramon & Cajal, Madrid, Spain
de la Rubia, J:
Hosp Doctor Peset, Valencia, Spain
de la Guia, AL:
Hosp Univ La Paz, Madrid, Spain
Rios, R:
Hosp Virgen Nieves, CIBERESP, Ibs, Granada, Spain
Sureda, A:
Univ Barcelona, IDIBELL, Inst Catala Oncol Hosp, Barcelona, Spain
Hernandez, MT:
Hosp Univ Canarias, Santa Cruz De Tenerife, Spain
Bengoechea, E:
Hosp Donostia, San Sebastian, Spain
Calasanz, MJ:
Clin Univ Navarra, CIMA, CIBERONC, IDISNA, Pamplona, Spain
Gutierrez, N:
Univ Salamanca, Hosp Univ Salamanca, Inst Invest Biomed Salamanca, Inst Biol Mol & Celular Canc,CSIC,CIBERONC, Salamanca, Spain
Martin, ML:
Univ Complutense Madrid, CNIO, Hosp Univ 12 Octubre, Madrid, Spain
Blade, J:
Inst Invest Biomed August Pi i Sunyer, Hosp Clin, Barcelona, Spain
Lahuerta, JJ:
Univ Complutense Madrid, CNIO, Hosp Univ 12 Octubre, Madrid, Spain
San Miguel, J:
Clin Univ Navarra, CIMA, CIBERONC, IDISNA, Pamplona, Spain
Mateos, MV:
Univ Salamanca, Hosp Univ Salamanca, Inst Invest Biomed Salamanca, Inst Biol Mol & Celular Canc,CSIC,CIBERONC, Salamanca, Spain
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