Unsupervised machine learning improves risk stratification in newly diagnosed multiple myeloma: an analysis of the Spanish Myeloma 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 newl...
| Autores: | , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Recursos: | Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau) |
| Repositorio: | r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau |
| OAI Identifier: | oai:iibsantpau.fundanetsuite.com:p9910 |
| Acesso em linha: | https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=9910 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128861737&doi=10.1038%2fs41408-022-00647-z&partnerID=40&md5=4963cfd10d203ea184a8a2cdf7adc88a |
| Access Level: | acceso abierto |
| Palavra-chave: | alpha2b interferon bortezomib busulfan carmustine cyclophosphamide dexamethasone doxorubicin ixazomib lenalidomide melphalan prednisone thalidomide vincristine Article biochemistry cancer patient cancer prognosis cancer risk cancer survival clinical feature clinical trial (topic) cohort analysis controlled study cytogenetics data clustering diagnostic accuracy drug response human human tissue International Staging System low risk patient major clinical study multiple cycle treatment multiple myeloma overall survival progression free survival scoring system statistical error survival analysis treatment outcome unsupervised machine learning cancer staging prognosis risk assessment Humans Multiple Myeloma Neoplasm Staging Prognosis Risk Assessment Unsupervised Machine Learning |
| Resumo: | 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. |
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