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Title: | Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma Group |
Author: | Mosquera Orgueira, Adrian González Pérez, Marta Sonia Diaz Arias, Jose Rosiñol, 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 |
Keywords: | Mieloma múltiple Diagnòstic Pronòstic mèdic Multiple myeloma Prognosis Diagnosis |
Issue Date: | 1-Apr-2022 |
Publisher: | Springer Science and Business Media LLC |
Abstract: | 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. |
Note: | Reproducció del document publicat a: https://doi.org/10.1038/s41408-022-00647-z |
It is part of: | Blood Cancer Journal, 2022, vol. 12, num. 76 |
URI: | http://hdl.handle.net/2445/185522 |
Related resource: | https://doi.org/10.1038/s41408-022-00647-z |
Appears in Collections: | Articles publicats en revistes (IDIBAPS: Institut d'investigacions Biomèdiques August Pi i Sunyer) Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL)) |
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