Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study

Simple Summary One of the main applications of in vivo magnetic resonance spectroscopy (MRS) is in the non-invasive monitoring of the metabolic pattern of brain tumors. MRS comes in two basic modalities, single-voxel (SV), from which the signal is obtained, and multivoxel (MV), in which one or more...

Descripción completa

Detalles Bibliográficos
Autores: Ungan, Gülnur, Pons Escoda, Albert, Ulinic, Daniel, Arús, Carles, Vellido, Alfredo, Julià Sapé, Margarida
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/204420
Acceso en línea:https://hdl.handle.net/2445/204420
Access Level:acceso abierto
Palabra clave:Espectroscòpia de ressonància magnètica nuclear
Tumors cerebrals
Nuclear magnetic resonance spectroscopy
Brain tumors
id ES_795ba720fc256b9dca45ad6e4e2d9e2f
oai_identifier_str oai:recercat.cat:2445/204420
network_acronym_str ES
network_name_str España
repository_id_str
spelling Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept StudyUngan, GülnurPons Escoda, AlbertUlinic, DanielArús, CarlesVellido, AlfredoJulià Sapé, MargaridaEspectroscòpia de ressonància magnètica nuclearTumors cerebralsNuclear magnetic resonance spectroscopyBrain tumorsSimple Summary One of the main applications of in vivo magnetic resonance spectroscopy (MRS) is in the non-invasive monitoring of the metabolic pattern of brain tumors. MRS comes in two basic modalities, single-voxel (SV), from which the signal is obtained, and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. The purpose of our proof-of-concept study was to test whether it would be possible to train machine learning models using SV data at 1.5T, and test them with MV 3T data from independent patients, obtaining color-coded images of pathology (nosological images) to help radiologists in their preoperative evaluation of patients. With sequential forward feature selection followed by linear discriminant analysis, we obtained AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal brain) in the MV test set. In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. Purpose: To test whether MV grids can be classified with models trained with SV. Methods: Retrospective study. Training dataset: Multicenter multiformat SV INTERPRET, 1.5T. Testing dataset: MV eTumour, 3T. Two classification tasks were completed: 3-class (meningioma vs. aggressive vs. normal) and 4-class (meningioma vs. low-grade glioma vs. aggressive vs. normal). Five different methods were tested for feature selection. The classification was implemented using linear discriminant analysis (LDA), random forest, and support vector machines. The evaluation was completed with balanced error rate (BER) and area under the curve (AUC) on both sets. The accuracy in class prediction was calculated by developing a solid tumor index (STI) and segmentation accuracy with the Dice score. Results: The best method was sequential forward feature selection combined with LDA, with AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal). STI was 66% (4-class task) and 71% (3-class task) because two cases failed completely and two more had suboptimal STI as defined by us. Discussion: The reasons for failure in the classification of the MV test set were related to the presence of artifacts.MDPI AG2023202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion24 p.application/pdfhttps://hdl.handle.net/2445/204420Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.3390/cancers15143709Cancers, 2023, vol. 15, num. 14https://doi.org/10.3390/cancers15143709cc by (c) Ungan, Gülnur et al, 2023http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/2044202026-05-29T05:05:01Z
dc.title.none.fl_str_mv Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study
title Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study
spellingShingle Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study
Ungan, Gülnur
Espectroscòpia de ressonància magnètica nuclear
Tumors cerebrals
Nuclear magnetic resonance spectroscopy
Brain tumors
title_short Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study
title_full Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study
title_fullStr Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study
title_full_unstemmed Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study
title_sort Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T: A Proof-of-Concept Study
dc.creator.none.fl_str_mv Ungan, Gülnur
Pons Escoda, Albert
Ulinic, Daniel
Arús, Carles
Vellido, Alfredo
Julià Sapé, Margarida
author Ungan, Gülnur
author_facet Ungan, Gülnur
Pons Escoda, Albert
Ulinic, Daniel
Arús, Carles
Vellido, Alfredo
Julià Sapé, Margarida
author_role author
author2 Pons Escoda, Albert
Ulinic, Daniel
Arús, Carles
Vellido, Alfredo
Julià Sapé, Margarida
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Espectroscòpia de ressonància magnètica nuclear
Tumors cerebrals
Nuclear magnetic resonance spectroscopy
Brain tumors
topic Espectroscòpia de ressonància magnètica nuclear
Tumors cerebrals
Nuclear magnetic resonance spectroscopy
Brain tumors
description Simple Summary One of the main applications of in vivo magnetic resonance spectroscopy (MRS) is in the non-invasive monitoring of the metabolic pattern of brain tumors. MRS comes in two basic modalities, single-voxel (SV), from which the signal is obtained, and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. The purpose of our proof-of-concept study was to test whether it would be possible to train machine learning models using SV data at 1.5T, and test them with MV 3T data from independent patients, obtaining color-coded images of pathology (nosological images) to help radiologists in their preoperative evaluation of patients. With sequential forward feature selection followed by linear discriminant analysis, we obtained AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal brain) in the MV test set. In vivo magnetic resonance spectroscopy (MRS) has two modalities, single-voxel (SV) and multivoxel (MV), in which one or more contiguous grids of SVs are acquired. Purpose: To test whether MV grids can be classified with models trained with SV. Methods: Retrospective study. Training dataset: Multicenter multiformat SV INTERPRET, 1.5T. Testing dataset: MV eTumour, 3T. Two classification tasks were completed: 3-class (meningioma vs. aggressive vs. normal) and 4-class (meningioma vs. low-grade glioma vs. aggressive vs. normal). Five different methods were tested for feature selection. The classification was implemented using linear discriminant analysis (LDA), random forest, and support vector machines. The evaluation was completed with balanced error rate (BER) and area under the curve (AUC) on both sets. The accuracy in class prediction was calculated by developing a solid tumor index (STI) and segmentation accuracy with the Dice score. Results: The best method was sequential forward feature selection combined with LDA, with AUCs = 0.95 (meningioma), 0.89 (aggressive), 0.82 (low-grade glioma), and 0.82 (normal). STI was 66% (4-class task) and 71% (3-class task) because two cases failed completely and two more had suboptimal STI as defined by us. Discussion: The reasons for failure in the classification of the MV test set were related to the presence of artifacts.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/204420
url https://hdl.handle.net/2445/204420
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/cancers15143709
Cancers, 2023, vol. 15, num. 14
https://doi.org/10.3390/cancers15143709
dc.rights.none.fl_str_mv cc by (c) Ungan, Gülnur et al, 2023
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc by (c) Ungan, Gülnur et al, 2023
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 24 p.
application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411342834728960
score 15,81155

Similares en LA Referencia