Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group
Adrian Mosquera Orgueira # 1 , Marta Sonia González Pérez # 1 , Jose Diaz Arias 1 , Laura Rosiñol 2 , Albert Oriol 3 , Ana Isabel Teruel 4 , Joaquin Martinez Lopez 5 , Luis Palomera 6 , Miguel Granell 7 , Maria Jesus Blanchard 8 , Javier de la Rubia 9 , Ana López de la Guia 10 , Rafael Rios 11 , Anna Sureda 12 , Miguel Teodoro Hernandez 13 , Enrique Bengoechea 14 , María José Calasanz 15 , Norma Gutierrez 16 , Maria Luis Martin 5 , Joan Blade 2 , Juan-Jose Lahuerta 5 , Jesús San Miguel 15 , Maria Victoria Mateos 17 , PETHEMA/GEM Cooperative Group
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.
CITATION Blood Cancer J. 2022 Apr 25;12(4):76. doi: 10.1038/s41408-022-00647-z