Using Single-Voxel Magnetic Resonance Spectroscopy Data Acquired at 1.5T to Classify Multivoxel Data at 3T : A Proof-of-Concept Study
Ungan, Gulnur Semahat (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Pons-Escoda, Albert (Institut d'Investigació Biomèdica de Bellvitge)
Ulinic, Daniel (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular)
Arús i Caraltó, Carles (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Vellido, Alfredo (Centro de Investigación Biomédica en Red)
Julià Sapé, Ma. Margarita (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")

Data:	2023
Resum:	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.
Ajuts:	European Commission 813120 Instituto de Salud Carlos III PI20/00064 Instituto de Salud Carlos III PI20/00360 Ministerio de Economía y Competitividad SAF2014-52332-R Ministerio de Sanidad y Consumo CB06/01/0010 Agencia Estatal de Investigación PID2019-104551RB-I00
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Magnetic resonance spectroscopy ; Brain tumors ; Glioblastoma multiforme ; Decision support systems ; Nosologic imaging ; Metabolic pattern
Publicat a:	Cancers, Vol. 15, Issue 14 (July 2023) , art. 3709, ISSN 2072-6694

DOI: 10.3390/cancers15143709
PMID: 37509372

24 p, 6.0 MB

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