Stroke lesion outcome prediction based on MRI imaging combined with clinical information
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/65790 |
Resumo: | In developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion procedure. However, the decision process is an intricate task due to the variability of lesion size, shape, and location, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome, at a 3-month follow-up, would provide an important support to the physicians' decision process. In this work, we propose an automatic deep learning-based method for stroke lesion outcome prediction. Our main contribution resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy. In our proposal, this clinical information is considered at two levels. First, at a population level by embedding the clinical information in a custom loss function used during training of our deep learning architecture. Second, at a patient-level through an extra input channel of the neural network used at testing time for a given patient case. By merging imaging with non-imaging clinical information, we aim to obtain a model aware of the principal and collateral blood flow dynamics for cases where there is no perfusion beyond the point of occlusion and for cases where the perfusion is complete after the occlusion point. |
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Stroke lesion outcome prediction based on MRI imaging combined with clinical informationstrokemachine learningdeep learningMRIpredictionScience & TechnologyIn developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion procedure. However, the decision process is an intricate task due to the variability of lesion size, shape, and location, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome, at a 3-month follow-up, would provide an important support to the physicians' decision process. In this work, we propose an automatic deep learning-based method for stroke lesion outcome prediction. Our main contribution resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy. In our proposal, this clinical information is considered at two levels. First, at a population level by embedding the clinical information in a custom loss function used during training of our deep learning architecture. Second, at a patient-level through an extra input channel of the neural network used at testing time for a given patient case. By merging imaging with non-imaging clinical information, we aim to obtain a model aware of the principal and collateral blood flow dynamics for cases where there is no perfusion beyond the point of occlusion and for cases where the perfusion is complete after the occlusion point.AP was supported by a scholarship from the Fundacao para a Ciencia e Tecnologia (FCT), Portugal (scholarship number PD/BD/113968/2015). This work is supported by FCT with the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 Programa Operacional Competitividade e Internacionalizacao (POCI) with the reference project POCI-01-0145-FEDER-006941. We acknowledge support from the Swiss National Science Foundation - DACH320030L_163363.Frontiers MediaUniversidade do MinhoPinto, AdrianoMckinley, RichardAlves, VictorWiest, RolandSilva, Carlos A.Reyes, Mauricio20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/65790engPinto A, Mckinley R, Alves V, Wiest R, Silva CA and Reyes M (2018) Stroke Lesion Outcome Prediction Based on MRI Imaging Combined With Clinical Information. Front. Neurol. 9:1060. doi: 10.3389/fneur.2018.010601664-229510.3389/fneur.2018.01060https://www.frontiersin.org/articles/10.3389/fneur.2018.01060/fullinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:28:46Zoai:repositorium.sdum.uminho.pt:1822/65790Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:23:38.526903Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Stroke lesion outcome prediction based on MRI imaging combined with clinical information |
title |
Stroke lesion outcome prediction based on MRI imaging combined with clinical information |
spellingShingle |
Stroke lesion outcome prediction based on MRI imaging combined with clinical information Pinto, Adriano stroke machine learning deep learning MRI prediction Science & Technology |
title_short |
Stroke lesion outcome prediction based on MRI imaging combined with clinical information |
title_full |
Stroke lesion outcome prediction based on MRI imaging combined with clinical information |
title_fullStr |
Stroke lesion outcome prediction based on MRI imaging combined with clinical information |
title_full_unstemmed |
Stroke lesion outcome prediction based on MRI imaging combined with clinical information |
title_sort |
Stroke lesion outcome prediction based on MRI imaging combined with clinical information |
author |
Pinto, Adriano |
author_facet |
Pinto, Adriano Mckinley, Richard Alves, Victor Wiest, Roland Silva, Carlos A. Reyes, Mauricio |
author_role |
author |
author2 |
Mckinley, Richard Alves, Victor Wiest, Roland Silva, Carlos A. Reyes, Mauricio |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Pinto, Adriano Mckinley, Richard Alves, Victor Wiest, Roland Silva, Carlos A. Reyes, Mauricio |
dc.subject.por.fl_str_mv |
stroke machine learning deep learning MRI prediction Science & Technology |
topic |
stroke machine learning deep learning MRI prediction Science & Technology |
description |
In developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion procedure. However, the decision process is an intricate task due to the variability of lesion size, shape, and location, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome, at a 3-month follow-up, would provide an important support to the physicians' decision process. In this work, we propose an automatic deep learning-based method for stroke lesion outcome prediction. Our main contribution resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy. In our proposal, this clinical information is considered at two levels. First, at a population level by embedding the clinical information in a custom loss function used during training of our deep learning architecture. Second, at a patient-level through an extra input channel of the neural network used at testing time for a given patient case. By merging imaging with non-imaging clinical information, we aim to obtain a model aware of the principal and collateral blood flow dynamics for cases where there is no perfusion beyond the point of occlusion and for cases where the perfusion is complete after the occlusion point. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018 2018-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/65790 |
url |
http://hdl.handle.net/1822/65790 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Pinto A, Mckinley R, Alves V, Wiest R, Silva CA and Reyes M (2018) Stroke Lesion Outcome Prediction Based on MRI Imaging Combined With Clinical Information. Front. Neurol. 9:1060. doi: 10.3389/fneur.2018.01060 1664-2295 10.3389/fneur.2018.01060 https://www.frontiersin.org/articles/10.3389/fneur.2018.01060/full |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Frontiers Media |
publisher.none.fl_str_mv |
Frontiers Media |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132712816082944 |