Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma

Belmonte, José María; Blanquer Blanquer, Miguel; Bernabé García, Gregorio; Jiménez Barrionuevo, Fernando; García Carrasco, José Manuel

Publication:
Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma

dc.contributor.author	Belmonte, José María
dc.contributor.author	Blanquer Blanquer, Miguel
dc.contributor.author	Bernabé García, Gregorio
dc.contributor.author	Jiménez Barrionuevo, Fernando
dc.contributor.author	García Carrasco, José Manuel
dc.contributor.department	Ingeniería y Tecnología de Computadores	es
dc.date.accessioned	2025-06-06T10:14:15Z
dc.date.available	2025-06-06T10:14:15Z
dc.date.issued	2025-06-03
dc.description	© 2025 the author(s). This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by/4.0/ This document is the Published Manuscript, version of a Published Work that appeared in final form in Journal of Integrative Bioinformatics. To access the final edited and published work see https://doi.org/10.1515/jib-2024-0053
dc.description.abstract	This paper investigates the application of Survival Analysis (SA) techniques to forecast outcomes after autologous Hematopoietic Stem Cell Transplantation (aHSCT) for Multiple Myeloma (MM). By leveraging six SA models, we examine their predictive capabilities, measured through the Concordance Index (C-index) metric. Beyond evaluating model performance, we analyze feature importance using permutation and SHAP methods, highlighting key clinical factors such as treatment history, disease stage, and prior disease progression or relapse as critical predictors of survival. The findings suggest that while all models performed well based on the C-index, a detailed examination revealed variations in how each model processed data. Specifically, the Coxnet and Random Survival Forest models exhibited a more thorough use of clinical variables, whereas the gradient boosting models appeared to rely on a narrower range of features, potentially limiting their ability to differentiate between patients with comparable profiles. Risk predictions categorized patients into low, moderate, and high-risk levels. For lower-risk patients, the procedure showed positive outcomes, while higher-risk individuals were predicted to have limited survival benefits, recommending alternative treatments. Lastly, we propose future research to expand these models into time-to-event estimations, offering additional support for decision-making by predicting patient life expectancy post-transplant, considering their pre-transplant clinical attributes.	es
dc.format	application/pdf	es
dc.format.extent	12	es
dc.identifier.citation	Journal of Integrative Bioinformatics, 2025, pp. 20240053
dc.identifier.doi	https://doi.org/10.1515/jib-2024-0053
dc.identifier.issn	Electronic: 1613-4516
dc.identifier.uri	http://hdl.handle.net/10201/155655
dc.language	eng	es
dc.publisher	De Gruyter	es
dc.relation	This work has been partially funded by Grant TED2021-129221B-I00, funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.	es
dc.relation.publisherversion	https://www.degruyterbrill.com/document/doi/10.1515/jib-2024-0053/html	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Atribución 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	Artificial Intelligence	es
dc.subject	Feature importance	es
dc.subject	Machine Learning	es
dc.subject	Survival Analysis	es
dc.subject	Survival Prediction	es
dc.title	Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es
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