Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography

[EN] Purpose: This study aims to develop a Radiomics-based Supervised Machine-Learning model to predict mortality in patients with spontaneous intracerebral hemorrhage (sICH).<br /> Methods: Retrospective analysis of a prospectively collected clinical registry of patients with sICH con...

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Autores: López-Rueda, Antonio, Rodríguez-Sánchez, María De Los Ángeles, Serrano, Elena, Moreno, Javier, Rodríguez, Alejandro, Llull, Laura, Amaro, Sergi, Oleaga, Laura
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/214679
Acceso en línea:https://riunet.upv.es/handle/10251/214679
Access Level:acceso abierto
Palabra clave:Radiomics
Machine learning
Intracerebral hemorrhage
Computed tomography
Stroke
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spelling Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomographyLópez-Rueda, AntonioRodríguez-Sánchez, María De Los ÁngelesSerrano, ElenaMoreno, JavierRodríguez, AlejandroLlull, LauraAmaro, SergiOleaga, LauraRadiomicsMachine learningIntracerebral hemorrhageComputed tomographyStroke[EN] Purpose: This study aims to develop a Radiomics-based Supervised Machine-Learning model to predict mortality in patients with spontaneous intracerebral hemorrhage (sICH).<br /> Methods: Retrospective analysis of a prospectively collected clinical registry of patients with sICH consecutively admitted at a single academic comprehensive stroke center between January-2016 and April-2018. We conducted an in-depth analysis of 105 radiomic features extracted from 105 patients. Following the identification and handling of missing values, radiomics values were scaled to 0-1 to train different classifiers. The sample was split into 80-20 % training-test and validation cohort in a stratified fashion. Random Forest(RF), K-Nearest Neighbor(KNN), and Support Vector Machine(SVM) classifiers were evaluated, along with several feature selection methods and hyperparameter optimization strategies, to classify the binary outcome of mortality or survival during hospital admission. A tenfold stratified cross-validation method was used to train the models, and average metrics were calculated.<br /> Results: RF, KNN, and SVM, with the "DropOut+SelectKBest" feature selection strategy and no hyperparameter optimization, demonstrated the best performances with the least number of radiomic features and the most simplified models, achieving a sensitivity range between 0.90 and 0.95 and AUC range from 0.97 to 1 on the validation dataset. Regarding the confusion matrix, the SVM model did not predict any false negative test (negative predicted value 1).<br /> Conclusion: Radiomics-based Supervised Machine Learning models can predict mortality during admission in patients with sICH. SVM with the "DropOut+SelectKBest" feature selection strategy and no hyperparameter optimization was the best simplified model to detect mortality during admission in patients with sICH.ElsevierRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-12-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/214679reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2146792026-06-13T07:49:27Z
dc.title.none.fl_str_mv Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography
title Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography
spellingShingle Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography
López-Rueda, Antonio
Radiomics
Machine learning
Intracerebral hemorrhage
Computed tomography
Stroke
title_short Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography
title_full Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography
title_fullStr Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography
title_full_unstemmed Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography
title_sort Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography
dc.creator.none.fl_str_mv López-Rueda, Antonio
Rodríguez-Sánchez, María De Los Ángeles
Serrano, Elena
Moreno, Javier
Rodríguez, Alejandro
Llull, Laura
Amaro, Sergi
Oleaga, Laura
author López-Rueda, Antonio
author_facet López-Rueda, Antonio
Rodríguez-Sánchez, María De Los Ángeles
Serrano, Elena
Moreno, Javier
Rodríguez, Alejandro
Llull, Laura
Amaro, Sergi
Oleaga, Laura
author_role author
author2 Rodríguez-Sánchez, María De Los Ángeles
Serrano, Elena
Moreno, Javier
Rodríguez, Alejandro
Llull, Laura
Amaro, Sergi
Oleaga, Laura
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Radiomics
Machine learning
Intracerebral hemorrhage
Computed tomography
Stroke
topic Radiomics
Machine learning
Intracerebral hemorrhage
Computed tomography
Stroke
description [EN] Purpose: This study aims to develop a Radiomics-based Supervised Machine-Learning model to predict mortality in patients with spontaneous intracerebral hemorrhage (sICH).<br /> Methods: Retrospective analysis of a prospectively collected clinical registry of patients with sICH consecutively admitted at a single academic comprehensive stroke center between January-2016 and April-2018. We conducted an in-depth analysis of 105 radiomic features extracted from 105 patients. Following the identification and handling of missing values, radiomics values were scaled to 0-1 to train different classifiers. The sample was split into 80-20 % training-test and validation cohort in a stratified fashion. Random Forest(RF), K-Nearest Neighbor(KNN), and Support Vector Machine(SVM) classifiers were evaluated, along with several feature selection methods and hyperparameter optimization strategies, to classify the binary outcome of mortality or survival during hospital admission. A tenfold stratified cross-validation method was used to train the models, and average metrics were calculated.<br /> Results: RF, KNN, and SVM, with the "DropOut+SelectKBest" feature selection strategy and no hyperparameter optimization, demonstrated the best performances with the least number of radiomic features and the most simplified models, achieving a sensitivity range between 0.90 and 0.95 and AUC range from 0.97 to 1 on the validation dataset. Regarding the confusion matrix, the SVM model did not predict any false negative test (negative predicted value 1).<br /> Conclusion: Radiomics-based Supervised Machine Learning models can predict mortality during admission in patients with sICH. SVM with the "DropOut+SelectKBest" feature selection strategy and no hyperparameter optimization was the best simplified model to detect mortality during admission in patients with sICH.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-12-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/214679
url https://riunet.upv.es/handle/10251/214679
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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