Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models
Ortega-Martorell, Sandra (Liverpool John Moores University. Department of Applied Mathematics)
Candiota Silveira, Ana Paula (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular)
Thomson, Ryan (Liverpool John Moores University. Department of Applied Mathematics)
Riley, Patrick (Liverpool John Moores University. Department of Applied Mathematics)
Julià Sapé, Ma. Margarita (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular)
Olier, Iván (Liverpool John Moores University. Department of Applied Mathematics)
Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí"

Data:	2019
Resum:	Glioblastoma is the most frequent malignant intra-cranial tumour. Magnetic resonance imaging is the modality of choice in diagnosis, aggressiveness assessment, and follow-up. However, there are examples where it lacks diagnostic accuracy. Magnetic resonance spectroscopy enables the identification of molecules present in the tissue, providing a precise metabolomic signature. Previous research shows that combining imaging and spectroscopy information results in more accurate outcomes and superior diagnostic value. This study proposes a method to combine them, which builds upon a previous methodology whose main objective is to guide the extraction of sources. To this aim, prior knowledge about class-specific information is integrated into the methodology by setting the metric of a latent variable space where Non-negative Matrix Factorisation is performed. The former methodology, which only used spectroscopy and involved combining spectra from different subjects, was adapted to use selected areas of interest that arise from segmenting the T2-weighted image. Results showed that embedding imaging information into the source extraction (the proposed semi-supervised analysis) improved the quality of the tumour delineation, as compared to those obtained without this information (unsupervised analysis). Both approaches were applied to pre-clinical data, involving thirteen brain tumour-bearing mice, and tested against histopathological data. On results of twenty-eight images, the proposed Semi-Supervised Source Extraction (SSSE) method greatly outperformed the unsupervised one, as well as an alternative semi-supervised approach from the literature, with differences being statistically significant. SSSE has proven successful in the delineation of the tumour, while bringing benefits such as 1) not constricting the metabolomic-based prediction to the image-segmented area, 2) ability to deal with signal-to-noise issues, 3) opportunity to answer specific questions by allowing researchers/radiologists define areas of interest that guide the source extraction, 4) creation of an intra-subject model and avoiding contamination from inter-subject overlaps, and 5) extraction of meaningful, good-quality sources that adds interpretability, conferring validation and better understanding of each case.
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 imaging ; Mouse models ; Cancer treatment ; Imaging techniques ; Cancer detection and diagnosis ; Histopathology ; Lipid signaling ; Progressive diseases
Publicat a:	PloS one, Vol. 14, Núm. 8 (2019) , art. e0220809, ISSN 1932-6203

Dades de recerca relacionades amb l'article: https://ddd.uab.cat/record/201551
DOI: 10.1371/journal.pone.0220809
PMID: 31415601

21 p, 4.1 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències de la salut i biociències > Institut de Biotecnologia i de Biomedicina (IBB)
Articles > Articles de recerca
Articles > Articles publicats