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Predicting short-term survival after gross total or near total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI

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dc.contributor.authorCepeda, Santiago
dc.contributor.authorPérez Núñez, Ángel
dc.contributor.authorGarcía García, Sergio
dc.contributor.authorGarcía Pérez, Daniel
dc.contributor.authorArese, Ignacio
dc.contributor.authorJiménez Roldán, Luis
dc.contributor.authorGarcía Galindo, Manuel
dc.contributor.authorGonzález, Pedro
dc.contributor.authorVelasco Casares, María
dc.contributor.authorZamora, Tomás
dc.contributor.authorSarabia, Rosario
dc.contributor.departmentFarmacología
dc.contributor.otherFacultades de la UMU::Facultad de Medicina
dc.date.accessioned2026-02-20T07:02:28Z
dc.date.available2026-02-20T07:02:28Z
dc.date.copyright© 2021 by the authors
dc.date.issued2021-10-09
dc.description.abstractRadiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.
dc.formatapplication/pdf
dc.format.extent15
dc.identifier.citationCancers, 2021, Vol. 13(20), 5047
dc.identifier.doihttps://doi.org/10.3390/cancers13205047
dc.identifier.eissn2072-6694
dc.identifier.urihttp://hdl.handle.net/10201/208761
dc.languageeng
dc.publisherMDPI
dc.relationSin financiación externa a la Universidad
dc.relation.publisherversionhttps://www.mdpi.com/2072-6694/13/20/5047
dc.rightsAttribution 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectRadiomics
dc.subjectTexture analysis
dc.subjectSurvival
dc.subjectMachine learning
dc.subjectGlioblastoma
dc.subject.odsNo relacionado con ningún objetivo de desarrollo sostenible
dc.titlePredicting short-term survival after gross total or near total resection in glioblastomas by machine learning-based radiomic analysis of preoperative MRI
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
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relation.isAuthorOfPublication.latestForDiscovery2f2500a1-1b27-4420-820c-db8c16e79afd
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