Semi-supervised source extraction methodology for the nosological imaging of glioblastoma response to therapy

Ortega‐Martorell, Sandra; Olier, Iván; Delgado-Goñi, Teresa; Ciezka, Magdalena; Julià‐Sapé, Margarida; Lisboa, Paulo J. G.; Arús, Carles

doi:10.1109/cidm.2014.7008653

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Time allocation in the Servei de Ressonància Magnètica Nuclear at the Universitat Autònoma de Barcelona is gratefully acknowledged. Results shown in this work were partially communicated in two oral presentations (50,51).…”

Section: Acknowledgementsmentioning

confidence: 89%

MRSI-based molecular imaging of therapy response to temozolomide in preclinical glioblastoma using source analysis

et al. 2016

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Characterization of glioblastoma (GB) response to treatment is a key factor for improving patients' survival and prognosis. MRI and magnetic resonance spectroscopic imaging (MRSI) provide morphologic and metabolic profiles of GB but usually fail to produce unequivocal biomarkers of response. The purpose of this work is to provide proof of concept of the ability of a semi‐supervised signal source extraction methodology to produce images with robust recognition of response to temozolomide (TMZ) in a preclinical GB model. A total of 38 female C57BL/6 mice were used in this study. The semi‐supervised methodology extracted the required sources from a training set consisting of MRSI grids from eight GL261 GBs treated with TMZ, and six control untreated GBs. Three different sources (normal brain parenchyma, actively proliferating GB and GB responding to treatment) were extracted and used for calculating nosologic maps representing the spatial response to treatment. These results were validated with an independent test set (7 control and 17 treated cases) and correlated with histopathology. Major differences between the responder and non‐responder sources were mainly related to the resonances of mobile lipids (MLs) and polyunsaturated fatty acids in MLs (0.9, 1.3 and 2.8 ppm). Responding tumors showed significantly lower mitotic (3.3 ± 2.9 versus 14.1 ± 4.2 mitoses/field) and proliferation rates (29.8 ± 10.3 versus 57.8 ± 5.4%) than control untreated cases. The methodology described in this work is able to produce nosological images of response to TMZ in GL261 preclinical GBs and suitably correlates with the histopathological analysis of tumors. A similar strategy could be devised for monitoring response to treatment in patients. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Acknowledgementsmentioning

confidence: 89%

MRSI-based molecular imaging of therapy response to temozolomide in preclinical glioblastoma using source analysis

et al. 2016

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Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models

et al. 2019

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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.

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Semi-supervised source extraction methodology for the nosological imaging of glioblastoma response to therapy

Cited by 2 publications

References 16 publications

MRSI-based molecular imaging of therapy response to temozolomide in preclinical glioblastoma using source analysis

MRSI-based molecular imaging of therapy response to temozolomide in preclinical glioblastoma using source analysis

Embedding MRI information into MRSI data source extraction improves brain tumour delineation in animal models

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