Lipid and Macromolecules Quantitation in Differentiating Glioblastoma From Solitary Metastasis

Crisi, Girolamo; Orsingher, L.; Filice, Silvano

doi:10.1097/rct.0b013e318282d2ba

Cited by 25 publications

(27 citation statements)

References 44 publications

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“…For instance, the concentration of lipids and macromolecules may be higher in metastases than glioblastoma. Measurements based on this approach discriminated the two entities with an accuracy of approximately 80% in one report (Crisi et al, 2013). …”

Section: Differentiating Metastases From Similar-appearing Massesmentioning

confidence: 99%

Brain metastases: neuroimaging

Pope

2018

Handbook of Clinical Neurology

149

130

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Magnetic resonance imaging (MRI) is the cornerstone for evaluating patients with brain masses such as primary and metastatic tumors. Important challenges in effectively detecting and diagnosing brain metastases and in accurately characterizing their subsequent response to treatment remain. These difficulties include discriminating metastases from potential mimics such as primary brain tumors and infection, detecting small metastases, and differentiating treatment response from tumor recurrence and progression. Optimal patient management could be benefited by improved and well-validated prognostic and predictive imaging markers, as well as early response markers to identify successful treatment prior to changes in tumor size. To address these fundamental needs, newer MRI techniques including diffusion and perfusion imaging, MR spectroscopy, and positron emission tomography (PET) tracers beyond traditionally used 18-fluorodeoxyglucose are the subject of extensive ongoing investigations, with several promising avenues of added value already identified. These newer techniques provide a wealth of physiologic and metabolic information that may supplement standard MR evaluation, by providing the ability to monitor and characterize cellularity, angiogenesis, perfusion, pH, hypoxia, metabolite concentrations, and other critical features of malignancy. This chapter reviews standard and advanced imaging of brain metastases provided by computed tomography, MRI, and amino acid PET, focusing on potential biomarkers that can serve as problem-solving tools in the clinical management of patients with brain metastases.

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Section: Differentiating Metastases From Similar-appearing Massesmentioning

confidence: 99%

Brain metastases: neuroimaging

Pope

2018

Handbook of Clinical Neurology

149

130

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“…Short-echo time single-voxel MRS (102) and 1H-MRS (103) have also demonstrated utility in differentiating primary brain tumors from brain metastasis. Although these in-vivo imaging techniques show potential for the diagnosis and grading of tumors, histological examination is still considered the gold standard.…”

Section: Advanced Mri In Monitoring Physical and Biological Procesmentioning

confidence: 99%

Advanced Magnetic Resonance Imaging of the Physical Processes in Human Glioblastoma

et al. 2014

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The most common malignant primary brain tumor, glioblastoma (GBM) is a devastating disease with a grim prognosis. Patient survival is typically less than 2 years and fewer than 10% of patients survive more than 5 years. Magnetic Resonance Imaging (MRI) can have great utility in the diagnosis, grading and management of patients with GBM as many of the physical manifestations of the pathological processes in GBM can be visualized and quantified using MRI. Newer MRI techniques such as dynamic contrast enhanced (DCE) and dynamic susceptibility contrast (DSC) MRI provide functional information regarding the tumor hemodynamic status. Diffusion MRI can shed light on tumor cellularity and the disruption of white matter tracts in the proximity of tumors. MR spectroscopy can be used to study new tumor tissue markers such as IDH mutations. MRI is helping to noninvasively explore the link between the molecular basis of gliomas and the imaging characteristics of their physical processes. We will review several approaches to MR-based imaging and discuss the potential for these techniques to quantify the physical processes in glioblastoma including tumor cellularity and vascularity, metabolite expression, and patterns of tumor growth and recurrence. We will conclude with challenges and opportunities for further research in applying physical principles to better understand the biological process in this deadly disease.

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“…Moreover, lower classification rates (60-F o r P e e r R e v i e w 6 80%) or <0.8 average AUC were reported to distinguish GBM and MET in other previous studies (13,14,16,17,(22)(23)(24)(25).…”

mentioning

confidence: 99%

“…MR spectroscopy and perfusion MR have both been shown to provide good discrimination (classification accuracy of 60-80%) between GBMs and solitary METs, e.g., (14) and (10); however, both techniques require subjective definition of tumour and/or peri-tumour regions of interest (ROIs). In addition, both single-voxel and multi-voxel MR spectroscopy acquisitions require careful planning to avoid artefacts from scalp lipid signals, distortions by water signals from cerebrospinal fluid, and poor spectral quality due to B 0 inhomogeneity in regions near to sinuses; thereby MRS can be more limited in whole brain coverage than MRI methods.…”

mentioning

confidence: 99%

“…Since conventional structural MRI is ambiguous advanced techniques such as MR spectroscopy (7,13,14), perfusion MR (10,15) and Diffusion Tensor Imaging (DTI) (6,16), have been investigated for their accuracy in differentiation of GBM from solitary MET. Based on conventional structural and advanced MRI, a variety of sophisticated methods have been investigated to aid discrimination of GBMs and METs.…”

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confidence: 99%

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Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three‐dimensional shape analysis

Yang

Jones

Howe

et al. 2015

Magnetic Resonance in Med

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Purpose: Glioblastoma multiforme (GBM) and brain metastasis (MET) are the most common intra-axial brain neoplasms in adults and often pose a diagnostic dilemma using standard clinical MRI. These tumor types require different oncological and surgical management, which subsequently influence prognosis and clinical outcome. Methods: Here, we hypothesize that GBM and MET possess different three-dimensional (3D) morphological attributes based on their physical characteristics. A 3D morphological analysis was applied on the tumor surface defined by our diffusion tensor imaging (DTI) segmentation technique. It segments the DTI data into clusters representing different isotropic and anisotropic water diffusion characteristics, from which a distinct surface boundary between healthy and pathological tissue was identified. Morphometric features of shape index and curvedness were then computed for each tumor surface and used to build a morphometric model of GBM and MET pathology with the goal of developing a tumor classification method based on shape characteristics. Results: Our 3D morphometric method was applied on 48 untreated brain tumor patients. Cross-validation resulted in a 95.8% accuracy classification with only two shape features needed and that can be objectively derived from quantitative imaging methods. Conclusion: The proposed 3D morphometric analysis framework can be applied to distinguish GBMs from solitary METs

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Lipid and Macromolecules Quantitation in Differentiating Glioblastoma From Solitary Metastasis

Cited by 25 publications

References 44 publications

Brain metastases: neuroimaging

Brain metastases: neuroimaging

Advanced Magnetic Resonance Imaging of the Physical Processes in Human Glioblastoma

Morphometric model for discrimination between glioblastoma multiforme and solitary metastasis using three‐dimensional shape analysis

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