Advanced MR techniques for preoperative glioma characterization: Part 1

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BackgroundThe clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.Methodology/Principal FindingsNon-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.Conclusions/SignificanceWe show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing

The Role of Arterial Spin Labelling (ASL) in Classification of Primary Adult Gliomas

Currently, the histological biopsy is the gold standard for classifying gliomas using the most recent histomolecular features. However, this process is both invasive and challenging, mainly when the lesion is in eloquent brain regions. Considering the complex interaction between the presence of the isocitrate dehydrogenase (IDH)-mutation, the upregulation of the hypoxia-induced factor (HIF), the neo-angiogenesis and the increased cellularity, perfusion MRI may be used indirectly for gliomas staging and further to predict the presence of key mutations, such as IDH. Recently, several studies have reported the subsidiary role of perfusion MRI in the prediction of gliomas histomolecular class. The three most common perfusion MRI methods are dynamic susceptibility contrast (DSC), dynamic contrast enhancement (DCE) and arterial spin labelling (ASL). Both DSC and DCE use exogenous contrast agent (CA) while ASL uses magnetically labelled blood water as an inherently diffusible tracer. ASL has begun to feature more prominently in clinical settings, as this method eliminates the need for CA and facilitates quantification of absolute cerebral blood flow (CBF). As a non-invasive, CA-free test, it can also be performed repeatedly where necessary. This makes it ideal for vulnerable patients, e.g. post-treatment oncological patients, who have reduced tolerance for high rate contrast injections and those suffering from renal insufficiency. This thesis performed a systematic review and critical appraisal of the existing ASL techniques for brain perfusion estimation, followed by a further systematic review and meta-analysis of the published studies, which have quantitatively assessed the diagnostic performance of ASL for grading preoperative adult gliomas. The repeatability of absolute tumour blood flow (aTBF) and relative TBF (rTBF) ASL-derived measurements were estimated to investigate the reliability of these ASL biomarkers in the clinical routine. Finally, utilising the radiomics pipeline analysis, the added diagnostic performance of ASL compared with CA-based MRI perfusion techniques, including DSC and DCE, and diffusion-weighted imaging (DWI) was investigated for glioma class prediction according to the WHO-2016 classification

The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge, part II

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study

Ανάπτυξη συστημάτων τεχνητής νοημοσύνης για την υποβοήθηση διαφορικής διάγνωσης νόσων εγκεφάλου με χρήση μαγνητικής φασματοσκοπίας και τεχνικών διάχυσης και αιμάτωσης σε υψηλό πεδίο 3Τ

Σημείωση: διατίθεται συμπληρωματικό υλικό σε ξεχωριστό αρχείο