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Table of Contents
REVIEW
Year : 2020  |  Volume : 3  |  Issue : 2  |  Page : 61-66

Glioma characterization based on magnetic resonance imaging: Challenge overview and future perspective


Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong Province, China

Date of Submission03-May-2020
Date of Acceptance26-May-2020
Date of Web Publication27-Jun-2020

Correspondence Address:
Prof. Lijuan Zhang
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan BLVD, Shenzhen 518055, Guangdong Province
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/glioma.glioma_9_20

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  Abstract 

Brain imaging has been broadly applied in neuroscience for more than 40 years. A wide range of studies on glioma have been carried out based on structural and functional imaging to characterize tumor malignancy, search for biomarker, aid the therapeutic process, and predict the prognosis. As the mainstay of modern neuroimaging, magnetic resonance imaging provides superior resolution and multiple contrasts capturing the morphological, vascular, metabolic, and functional properties of glioma. Furthermore, the development of connectivity-based approach and network models innovates our understanding of glioma in terms of functional remodeling and plasticity at various levels. The focus of this presentation is to overview the challenges of glioma characterization based on conventional magnetic resonance imaging and the future perspective of incorporating connectivity-based approaches into the disease management of glioma.

Keywords: Brain network, connectivity, glioma, magnetic resonance imaging, tumor characterization


How to cite this article:
Zhang L. Glioma characterization based on magnetic resonance imaging: Challenge overview and future perspective. Glioma 2020;3:61-6

How to cite this URL:
Zhang L. Glioma characterization based on magnetic resonance imaging: Challenge overview and future perspective. Glioma [serial online] 2020 [cited 2022 Dec 8];3:61-6. Available from: http://www.jglioma.com/text.asp?2020/3/2/61/288183


  Introduction Top


Glioma is a collective category of primary tumors in the brain. Tumor characterization of glioma has gone through a history of more than 200 years from merely gross morphological description up to modern molecular specialization. Development of neuroimaging has greatly facilitated the clinical management and advanced our knowledge of glioma. In particular, parameters and texture features derived from conventional and functional magnetic resonance imaging (fMRI) were shown to be strongly associated with the malignancy grade and patient survival. However, challenges remain in glioma characterization with conventional neuroimaging. Tumors with tremendous heterogeneity in biology, histology, and genotype may share similar imaging properties or appear indiscernible from nonneoplastic changes. In addition, the clinical profile and behavioral phenotypes of glioma are not always interpretable by the imaging signs of the tumor itself. These scenarios not only imply the biological complexity of glioma but also question the established framework of how we comprehend the brain. Network neuroscience has transformed our understanding of the principle of brain organization from static modules to dynamic networks with plastic potential. The structural and/or functional remodeling in the context of glioma may open up a new horizon toward innovating tumor characterization and patient care. This article aims to overview the current challenges of magnetic resonance imaging (MRI) in glioma characterization and the future perspective of network-based approaches in the disease management of glioma.


  Database Search Strategy Top


Full-text articles in English in PubMed database between January 1990 and February 2020 discussing tumor characterization and treatment response of glioma were included in this nonsystematic review. Participants were human (males and females of any age) diagnosed with primary or recurrence glioma and animal model (mouse, rat, and nonhuman primate). The literature search strategy was summarized as follows: each of the phrases (1) glioma, (2) tumor characterization, and (3) treatment response were combined with each of (a) MRI, (b) connectivity-based, (c) brain network. Reference lists of included studies were carefully screened with titles and abstracts set as the first priority followed by full texts for keywords to identify the relevant studies and the potentially supportive materials including case reports. Most of the retrieved references (90% of all references) were published between 2000 and 2019.


  Estimation of Tumor Extent Top


Tumor boundary demarcation is essential for the morphological assessment and treatment planning of glioma. Better delineation of the tumor tissue from the nonneoplastic brain would greatly favor the maximized tumor removal while preserving the unaffected brain. T1-weighted imaging (T1WI), T2WI, gadolinium contrast-enhanced T1WI, fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging are generally included in the standardized MRI protocols for glioma imaging. Each protocol provides a unique contrast among the neoplastic tissue, necrosis, edema, and the unaffected brain and thus forms a tumor “boundary” that may differ among one another. Given the complexity of glioma morphology and lack of biological specificity of MRI, dilemma may arise when it comes to define the tumor extent.

Contrast-enhanced T1WI accentuates the parenchymal areas with disrupted blood–brain barrier (BBB), while T2WI and FLAIR identify tissue areas with more water content. In general, T2WI is more informative in demarcating the glioma tumor and the peritumoral abnormalities.[1] However, the territory of contrast-enhanced T1WI or hyperintense T2WI/FLAIR may not always reasonably outline the glioma extent. For example, the peritumoral region with glioma cell infiltration does not enhance when the local BBB is intact.[2] Densely cellularized gliomas with high nucleus–cytoplasm ratios may not enhance on T1WI or appear T2 hypointense due to relatively decreased water content,[3],[4] making it highly challenging to determine the tumor territory with conventional MRI. These types of glioma are usually associated with worse prognosis,[5],[6] more frequently IDH wild-typed,[7] and have been increasingly recognized as a new topic of glioma research.[8] Aggressive management has been reported to favor the overall survival,[9],[10] but challenges remain in the pre- and posttreatment tumor characterization for these gliomas.

Advanced imaging techniques such as diffusion tensor imaging (DTI), MR spectroscopy, and perfusion-weighted imaging provide extra amenities targeting tissue cellular, molecular, and vascular milieu to improve the delineation of the nonenhancing gliomas.[11],[12] Particularly, DTI resolves the microstructural integrity of the brain by measuring the diffusivity of tissue water molecules. The largest water diffusivity occurs in the direction along the least restrictive axonal pathway. By quantifying the diffusion anisotropy of each voxel, the orientation and continuity of neural tracts can be constructed and visualized. Nonenhancing areas with significant decrease or loss of diffusion anisotropy suggest neural fiber disruption or tumor infiltration, and therefore, should be included in the tumor extent estimation. Clinical studies proved that DTI tractography improves the surgical planning with a high concordance with direct electrical stimulation.[13] However, it is worth noting that the MRI properties of glioma vary from patient to patient. Tumor extent estimated with MRI could be largely complicated by the tumor complexity and the technique specifications. In addition, tumor extent defined with uni- or bidimensional measurement is inferior to volumetric matrix, thus less efficient in the growth assessment of glioma.[14],[15]


  Posttreatment Assessment Top


The MRI signs of the posttreated gliomas vary greatly with tumor biology, inflammatory and/or reactive changes, and the pharmacological specifications of drugs. Enhancement on the T1WI has served as one of the key features in Macdonald criteria for the posttreatment assessment.[16] In this framework, the product of the maximal orthogonal diameters of an enhancing lesion is measured on the image with the largest cross-sectional tumor area. Progression is assumed if an increase over 25% or new tumor is observed, while response is assumed if a sustained (≥1 month) and significant reduction (≥50%) of the enhancing tumor is confirmed. However, enhancement on MRI can be ascribed to multiple nonneoplastic etiologies with BBB disintegration, such as general inflammation, postoperative ischemia, and reactive changes to radiation.[17],[18],[19] Contrast-enhanced MRI performed no later than 72 h after surgery can reasonably avoid the confounding effect of host-mediated inflammation on the detection of residual tumor. In contrast, enhancement of the irradiated tissue usually occurs in weeks or months after radiation when both true progression and pseudoprogression (gliosis and inflammatory reaction) could possibly occur. On the other hand, nonenhancement is not uncommon in glioblastoma treated with antiangiogenic agents.[20],[21] In these cases, decreased enhancement rather indicates temporally declined vascular permeability than signifies BBB normalization or reduction of tumor load. Therefore, addressing the enhanced component only could substantially over- or underestimate the tumor progression or treatment response, especially when multimodality therapy is involved. The revised guidelines of Response Assessment in Neuro-Oncology take the FLAIR signs into account, and its utility was validated in multiple clinical trials.[22],[23] FLAIR adds extra value in the posttreatment assessment after antiangiogenic therapy, in which reduced enhancement with enlarged FLAIR signs suggests pseudoresponse.[24],[25],[26]

In addition, the relative cerebral blood volume per 100 g tissue (derived from perfusion imaging) of the nonenhancing region of glioblastoma was found to outperform the other features from regular MRI, α-[11]C-methyl-l-tryptophan positron emission tomography, and EGFR mutation in predicting the treatment response of the nonenhancing glioblastoma.[27],[28] Measurements from DWI and MR spectroscopy showed certain relevance to the posttreatment changes of glioma, but these techniques alone were not recommended for the posttreatment evaluation due to their susceptibility to a variety of confounding technical factors and lack of the optimal cutoffs.[29],[30],[31] Other advanced MR techniques such as blood-oxygen-level-dependent imaging (BOLD), hyperpolarized gas imaging, and amide chemical exchange transfer saturation targeting the metabolism of endogenous oxygen, carbon, or amide provide extra contrast between nonneoplastic changes and the progressed tumor.[32],[33],[34],[35] Nevertheless, these techniques are still awaiting further development and clinical verification. Advanced data analysis with radiomics and machine learning showed impressive performance in differentiating pseudoprogression and pseudoresponse.[36],[37] However, despite the imaging technique being progressed, a fundamental medical study is still the urgent need to link the temporal profile of the posttreatment MRI signs to their pathological relevance. Currently, timely follow-up with conventional MRI remains the option of choice to determine pseudoprogression and pseudoresponse in the clinical management of glioma, especially when the treatment involves antiangiogenesis or immunotherapy.[38]


  Mapping Eloquent Areas With Blood-Oxygen-Level-Dependent Functional Magnetic Resonance Imaging Top


Task-based BOLD fMRI has been widely applied for the eloquent cortical mapping. This maneuver provides a relevant reference to preserve the critical brain function while maximizing the tumor removal to prevent recurrence or malignant transformation. The protocol of BOLD fMRI is tailored to heavy T2* weighting to accentuate the difference of concentration between oxygenated and deoxygenated hemoglobin as a result of hemodynamic response coupled to the local neural activation.[39] Of note, BOLD fMRI reflects the underlying neuronal activity based on the overall effect of vasoactive response instead of directly measuring the neural activity. The relationship between the neural activity and the resultant hemodynamic changes is termed neurovascular coupling[40] and assumed to underpin the physiological mechanism of BOLD signal through neurovascular unit.[41] A recent study showed that the caveola-mediated pathway in the arteriole pivot neurovascular coupling in the brain provides the cellular basis of BOLD fMRI.[42]

The spatial specificity of BOLD signal varies with the hierarchical vascular anatomy and physiology. Increased spatial resolution of BOLD fMRI allows better functional mapping, even to the laminar level,[43],[44] but the vascular point-spread function of BOLD fMRI typically ranged from 1.7–3.5 mm under standard experimental settings at the field strength across 1.5 to 9.4T.[45],[46] The close affinity of the capillary bed with neurons and astrocytes allows higher localization of BOLD signal, as compared with arteries and veins. Early response of “initial dip” is believed to be more spatially specific in colocalizing the neural activation, but the feasibility is hindered by technical challenges such as poor signal-to-noise ratio and task dependent variations.[47]

Glioma compromises the local brain vascular system to various extents, typically by co-option and angiogenesis.[48],[49] Proliferation of glioma cells impairs the interaction between endothelial cells, astrocytes, and pericytes, which subsequently alters the vascular tone within and around the tumor mass.[50],[51] Recently, a novel mechanism termed vascular mimicry was proposed to address the transdifferentiation of glioblastoma stem-like cells into endothelial-like cells to form vascular network.[52] In addition, progression of glioma alters the neuronal excitability, leading to decreased synchrony of spontaneous neuronal activity.[53] These effects collectively result in pathological neurovascular uncoupling that occurs across the histological grades of glioma, lower the reliability of eloquent cortex detection, or may even invalidate the functional mapping based on BOLD fMRI alone.[54],[55],[56] Alternative approaches were proposed to probe the transient changes of cerebrovascular reactivity in cortical regions, generally under hypercapnia or hyperoxia challenge.[57],[58] Impaired cerebrovascular reactivity and decreased BOLD signal are highly indicative of neurovascular uncoupling, especially for high-grade gliomas.[59] In addition, extensive brain volume loss and white matter disintegration were also observed in brains with neurovascular uncoupling.[60] These extensive structural changes could reduce the hemodynamic response beyond the tumor territory, leading to false-negative activation in the eloquent cortical mapping remote to the tumor.[61] On the other hand, factors such as neovascularization, glioma-associated arteriovenous shunting, gliosis, and increased nitric oxide level are sources of abnormal venous vasodilation and excessive deoxygenated hemoglobin in brain tissue,[62],[63] by which additional T2* decay and BOLD signal loss are introduced, leading to false-positive activation in the eloquent cortex mapping.

In short, BOLD signal is a function of hemodynamics and oxygen metabolism. The overall effectiveness of BOLD fMRI in the eloquent cortical mapping varies with glioma histology, task design, and patient's physiological state.[64] Interpretation of the cortical activation based on fMRI can be profoundly challenging in the context of tumor biology and neurovascular dissociation. Nevertheless, quantitative interpretation of BOLD fMRI is still possible by quantifying blood flow and oxygen metabolism.


  a Network Perspective Toward Glioma Characterization Top


Human brain features complex and dynamic networks that link multiple anatomical areas and evolve with time.[65],[66] Brain regions that occupy central position and form critical connections in a network are defined as hubs. Hubs ensure high efficiency of neuronal signaling but are otherwise vulnerable to attacks from brain disorders. Interaction between neurons and glioma cells alters the excitability of brain tissue, which subsequently trigger extensive structural and functional remodeling at the network level.[67],[68],[69] Tumors with direct or indirect hub involvement are likely to cause more significant disturbance to brain networks. Functional connectivity between hubs and nonhubs was found to increase in patients with low-grade glioma, while the internetwork connectivity in the contralesional hemisphere was enhanced in patients with advanced glioma.[68],[70] Changes in the contralesional functional connectivity are closely associated with the malignancy grade and the genetic specifications of glioma as well as the cognitive performance of the patient. Low-grade glioma features reduced potential for local information transfer and more distributed module processing relative to the high-grade ones.[68],[71] Participants with IDH1 mutation have a better presurgical neurocognitive performance represented by higher functional connectivity.[72],[73] These evidences indicate that the effect of a focal lesion on the brain is not restricted to the tumor area but could be extensive or even global. Furthermore, connectivity profile may substrate the comorbid affective dysfunctions of glioma patients that cannot be fully interpreted by clinicotopographical approach.[74],[75] Default mode and executive control networks engaging in internal mentation, socioemotional processing, and higher-order cognitive control were modified or dysregulated in patients with glioma.[76],[77] High-grade glioma patients have a higher level of distress than those with low-grade glioma.[78],[79] Although the underlying mechanism of affective dysfunctions in glioma patients is not well known, the slow growth of low-grade gliomas is assumed to favor an efficient functional reorganization and rebalance of network integrity by recruiting remote brain areas across networks, while the rapid growth of high-grade gliomas is rather destructive to brain networks without constructive remodeling.

Putting together, these studies indicate that functional network alterations at the local and global scales are part of the disease of glioma with great clinical significance. Radiation to glioblastoma close to network hub (area with widespread connectivity) led to a global improvement of functional connectivity,[80] suggesting that brain network could serve as a potential target of glioma treatment. Moreover, the fluctuation of network connectivity across time domain may provide extra information for evaluating the malignancy transformation and the posttreatment changes. It is worth postulating to explore whether renormalization of brain network could pose an inhibitory effect on glioma progress. Novel interventional scheme targeting pathological network connectivity may bear therapeutic promise for innovating glioma management and patient care.


  Conclusions Top


Glioma is likely a disease more than neoplastic entity. In addition to the biomarkers derived from conventional MRI, network alterations are promising to serve as new markers to signify the tumor kinetics and clinical profile of glioma. As novel treatment options are being translated into clinical practice, the scope of modern neuroimaging will be subjected to upgrades incorporating network science and biomedical informatics in the multidisciplinary team practice and research of glioma. In combination with data analysis tailored to individual patient's tumor, this would provide an opportunity to transform the disease management of glioma.

Financial support and sponsorship

This work was supported by the National Basic Research Program of China (No. 2015CB755500) and the National Natural Science Foundation of China (No. 81627901).

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Harward S, Harrison Farber S, Malinzak M, Becher O, Thompson EM. T2-weighted images are superior to other MR image types for the determination of diffuse intrinsic pontine glioma intratumoral heterogeneity. Childs Nerv Syst 2018;34:449-55.  Back to cited text no. 1
    
2.
Sarkaria JN, Hu LS, Parney IF, Pafundi DH, Brinkmann DH, Laack NN, et al. Is the blood-brain barrier really disrupted in all glioblastomas? A critical assessment of existing clinical data. Neuro Oncol 2018;20:184-91.  Back to cited text no. 2
    
3.
Cohen-Gadol AA, DiLuna ML, Bannykh SI, Piepmeier JM, Spencer DD. Non-enhancing de novo glioblastoma: Report of two cases. Neurosurg Rev 2004;27:281-5.  Back to cited text no. 3
    
4.
Eidel O, Burth S, Neumann JO, Kieslich PJ, Sahm F, Jungk C, et al. Tumor infiltration in enhancing and non-enhancing parts of glioblastoma: A correlation with histopathology. PLoS One 2017;12:e0169292.  Back to cited text no. 4
    
5.
Jain R, Poisson LM, Gutman D, Scarpace L, Hwang SN, Holder CA, et al. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: Focus on the non-enhancing component of the tumor. Radiology 2014;272:484-93.  Back to cited text no. 5
    
6.
Lasocki A, Gaillard F, Tacey M, Drummond K, Stuckey S. Incidence and prognostic significance of non-enhancing cortical signal abnormality in glioblastoma. J Med Imaging Radiat Oncol 2016;60:66-73.  Back to cited text no. 6
    
7.
Lasocki A, Tsui A, Gaillard F, Tacey M, Drummond K, Stuckey S. Reliability of noncontrast-enhancing tumor as a biomarker of IDH1 mutation status in glioblastoma. J Clin Neurosci 2017;39:170-5.  Back to cited text no. 7
    
8.
Lasocki A, Gaillard F. Non-contrast-enhancing tumor: A new frontier in glioblastoma research. AJNR Am J Neuroradiol 2019;40:758-65.  Back to cited text no. 8
    
9.
Li YM, Suki D, Hess K, Sawaya R. The influence of maximum safe resection of glioblastoma on survival in 1229 patients: Can we do better than gross-total resection? J Neurosurg 2016;124:977-88.  Back to cited text no. 9
    
10.
Pessina F, Navarria P, Cozzi L, Ascolese AM, Simonelli M, Santoro A, et al. Maximize surgical resection beyond contrast-enhancing boundaries in newly diagnosed glioblastoma multiforme: Is it useful and safe? A single institution retrospective experience. J Neurooncol 2017;135:129-39.  Back to cited text no. 10
    
11.
Autry A, Phillips JJ, Maleschlijski S, Roy R, Molinaro AM, Chang SM, et al. Characterization of metabolic, diffusion, and perfusion properties in GBM: Contrast-enhancing versus non-enhancing tumor. Transl Oncol 2017;10:895-903.  Back to cited text no. 11
    
12.
Boonzaier NR, Larkin TJ, Matys T, van der Hoorn A, Yan JL, Price SJ. Multiparametric MR imaging of diffusion and perfusion in contrast-enhancing and nonenhancing components in patients with glioblastoma. Radiology 2017;284:180-90.  Back to cited text no. 12
    
13.
Costabile JD, Alaswad E, D'Souza S, Thompson JA, Ormond DR. Current applications of diffusion tensor imaging and tractography in intracranial tumor resection. Front Oncol 2019;9:426.  Back to cited text no. 13
    
14.
Dempsey MF, Condon BR, Hadley DM. Measurement of tumor “size” in recurrent malignant glioma: 1D, 2D, or 3D? AJNR Am J Neuroradiol 2005;26:770-6.  Back to cited text no. 14
    
15.
Bette S, Barz M, Wiestler B, Huber T, Gerhardt J, Buchmann N, et al. Prognostic value of tumor volume in glioblastoma patients: Size also matters for patients with incomplete resection. Ann Surg Oncol 2018;25:558-64.  Back to cited text no. 15
    
16.
Macdonald DR, Cascino TL, Schold SC Jr, Cairncross JG. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990;8:1277-80.  Back to cited text no. 16
    
17.
de Wit MC, de Bruin HG, Eijkenboom W, Sillevis Smitt PA, van den Bent MJ. Immediate post-radiotherapy changes in malignant glioma can mimic tumor progression. Neurology 2004;63:535-7.  Back to cited text no. 17
    
18.
Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol 2008;9:453-61.  Back to cited text no. 18
    
19.
Hygino da Cruz LC Jr., Rodriguez I, Domingues RC, Gasparetto EL, Sorensen AG. Pseudoprogression and pseudoresponse: Imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol 2011;32:1978-85.  Back to cited text no. 19
    
20.
Huang RY, Neagu MR, Reardon DA, Wen PY. Pitfalls in the neuroimaging of glioblastoma in the era of antiangiogenic and immuno/targeted therapy – Detecting illusive disease, defining response. Front Neurol 2015;6:33.  Back to cited text no. 20
    
21.
Batchelor TT, Reardon DA, de Groot JF, Wick W, Weller M. Antiangiogenic therapy for glioblastoma: Current status and future prospects. Clin Cancer Res 2014;20:5612-9.  Back to cited text no. 21
    
22.
Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, et al. Updated response assessment criteria for high-grade gliomas: Response assessment in neuro-oncology working group. J Clin Oncol 2010;28:1963-72.  Back to cited text no. 22
    
23.
Eisele SC, Wen PY, Lee EQ. Assessment of brain tumor response: RANO and its offspring. Curr Treat Options Oncol 2016;17:35.  Back to cited text no. 23
    
24.
Huang RY, Rahman R, Ballman KV, Felten SJ, Anderson SK, Ellingson BM, et al. The impact of T2/FLAIR evaluation per RANO criteria on response assessment of recurrent glioblastoma patients treated with bevacizumab. Clin Cancer Res 2016;22:575-81.  Back to cited text no. 24
    
25.
Wen PY, Chang SM, Van den Bent MJ, Vogelbaum MA, Macdonald DR, Lee EQ. Response assessment in neuro-oncology clinical trials. J Clin Oncol 2017;35:2439-49.  Back to cited text no. 25
    
26.
Boxerman JL, Zhang Z, Safriel Y, Rogg JM, Wolf RL, Mohan S, et al. Prognostic value of contrast enhancement and FLAIR for survival in newly diagnosed glioblastoma treated with and without bevacizumab: Results from ACRIN 6686. Neuro Oncol 2018;20:1400-10.  Back to cited text no. 26
    
27.
Mangla R, Singh G, Ziegelitz D, Milano MT, Korones DN, Zhong J, et al. Changes in relative cerebral blood volume 1 month after radiation-temozolomide therapy can help predict overall survival in patients with glioblastoma. Radiology 2010;256:575-84.  Back to cited text no. 27
    
28.
Kim SH, Cho KH, Choi SH, Kim TM, Park CK, Park SH, et al. Prognostic predictions for patients with glioblastoma after standard treatment: Application of contrast leakage information from DSC-MRI within nonenhancing FLAIR high-signal-intensity lesions. AJNR Am J Neuroradiol 2019;40:2052-8.  Back to cited text no. 28
    
29.
Sahin N, Melhem ER, Wang S, Krejza J, Poptani H, Chawla S, et al. Advanced MR imaging techniques in the evaluation of nonenhancing gliomas: Perfusion-weighted imaging compared with proton magnetic resonance spectroscopy and tumor grade. Neuroradiol J 2013;26:531-41.  Back to cited text no. 29
    
30.
Zhang H, Ma L, Wang Q, Zheng X, Wu C, Xu BN. Role of magnetic resonance spectroscopy for the differentiation of recurrent glioma from radiation necrosis: A systematic review and meta-analysis. Eur J Radiol 2014;83:2181-9.  Back to cited text no. 30
    
31.
Nguyen HS, Milbach N, Hurrell SL, Cochran E, Connelly J, Bovi JA, et al. Progressing bevacizumab-induced diffusion restriction is associated with coagulative necrosis surrounded by viable tumor and decreased overall survival in patients with recurrent glioblastoma. AJNR Am J Neuroradiol 2016;37:2201-8.  Back to cited text no. 31
    
32.
White DA, Zhang Z, Li L, Gerberich J, Stojadinovic S, Peschke P, et al. Developing oxygen-enhanced magnetic resonance imaging as a prognostic biomarker of radiation response. Cancer Lett 2016;380:69-77.  Back to cited text no. 32
    
33.
Day SE, Kettunen MI, Cherukuri MK, Mitchell JB, Lizak MJ, Morris HD, et al. Detecting response of rat C6 glioma tumors to radiotherapy using hyperpolarized [1- 13C] pyruvate and 13C magnetic resonance spectroscopic imaging. Magn Reson Med 2011;65:557-63.  Back to cited text no. 33
    
34.
Ma B, Blakeley JO, Hong X, Zhang H, Jiang S, Blair L, et al. Applying amide proton transfer-weighted MRI to distinguish pseudoprogression from true progression in malignant gliomas. J Magn Reson Imaging 2016;44:456-62.  Back to cited text no. 34
    
35.
Jones KM, Michel KA, Bankson JA, Fuller CD, Klopp AH, Venkatesan AM. Emerging magnetic resonance imaging technologies for radiation therapy planning and response assessment. Int J Radiat Oncol Biol Phys 2018;101:1046-56.  Back to cited text no. 35
    
36.
Elshafeey N, Kotrotsou A, Hassan A, Elshafei N, Hassan I, Ahmed S, et al. Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma. Nat Commun 2019;10:3170.  Back to cited text no. 36
    
37.
Jang BS, Jeon SH, Kim IH, Kim IA. Prediction of pseudoprogression versus progression using machine learning algorithm in glioblastoma. Sci Rep 2018;8:12516.  Back to cited text no. 37
    
38.
Thust SC, Heiland S, Falini A, Jäger HR, Waldman AD, Sundgren PC, et al. Glioma imaging in Europe: A survey of 220 centres and recommendations for best clinical practice. Eur Radiol 2018;28:3306-17.  Back to cited text no. 38
    
39.
Ogawa S, Menon RS, Tank DW, Kim SG, Merkle H, Ellermann JM, et al. Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. Biophys J 1993;64:803-12.  Back to cited text no. 39
    
40.
Allen EA, Pasley BN, Duong T, Freeman RD. Transcranial magnetic stimulation elicits coupled neural and hemodynamic consequences. Science 2007;317:1918-21.  Back to cited text no. 40
    
41.
Attwell D, Iadecola C. The neural basis of functional brain imaging signals. Trends Neurosci 2002;25:621-5.  Back to cited text no. 41
    
42.
Chow BW, Nuñez V, Kaplan L, Granger AJ, Bistrong K, Zucker HL, et al. Caveolae in CNS arterioles mediate neurovascular coupling. Nature 2020;579:106-10.  Back to cited text no. 42
    
43.
Moon CH, Fukuda M, Park SH, Kim SG. Neural interpretation of blood oxygenation level-dependent fMRI maps at submillimeter columnar resolution. J Neurosci 2007;27:6892-902.  Back to cited text no. 43
    
44.
Shi Z, Wu R, Yang PF, Wang F, Wu TL, Mishra A, et al. High spatial correspondence at a columnar level between activation and resting state fMRI signals and local field potentials. Proc Natl Acad Sci U S A 2017;114:5253-8.  Back to cited text no. 44
    
45.
Shmuel A, Yacoub E, Chaimow D, Logothetis NK, Ugurbil K. Spatio-temporal point-spread function of fMRI signal in human gray matter at 7 Tesla. Neuroimage 2007;35:539-52.  Back to cited text no. 45
    
46.
Park JC, Ronen I, Toth LJ, Ugurbil K, Kim DS. Comparison of point spread functions of BOLD and ASL fMRI at an ultra-high magnetic field 9.4T. Proc Int Soc Mag Reson Med 2004:1014.  Back to cited text no. 46
    
47.
Hu X, Yacoub E. The stody of the initial dip in fMRI. Neuroimage 2012;62:1103-8.  Back to cited text no. 47
    
48.
Seano G, Jain RK. Vessel co-option in glioblastoma: Emerging insights and opportunities. Angiogenesis 2020;23:9-16.  Back to cited text no. 48
    
49.
Onishi M, Ichikawa T, Kurozumi K, Date I. Angiogenesis and invasion in glioma. Brain Tumor Pathol 2011;28:13-24.  Back to cited text no. 49
    
50.
Caspani EM, Crossley PH, Redondo-Garcia C, Martinez S. Glioblastoma: A pathogenic crosstalk between tumor cells and pericytes. PLoS One 2014;9:e101402.  Back to cited text no. 50
    
51.
Chow DS, Horenstein CI, Canoll P, Lignelli A, Hillman EM, Filippi CG, et al. Glioblastoma induces vascular dysregulation in nonenhancing peritumoral regions in humans. AJR Am J Roentgenol 2016;206:1073-81.  Back to cited text no. 51
    
52.
Chiao MT, Yang YC, Cheng WY, Shen CC, Ko JL. CD133+ glioblastoma stem-like cells induce vascular mimicry in vivo. Curr Neurovasc Res 2011;8:210-9.  Back to cited text no. 52
    
53.
Montgomery MK, Kim SH, Dovas A, Zhao HT, Goldberg AR, Xu W, et al. Glioma-induced alterations in neuronal activity and neurovascular coupling during disease progression. Cell Rep 2020;31:107500.  Back to cited text no. 53
    
54.
Azad TD, Duffau H. Limitations of functional neuroimaging for patient selection and surgical planning in glioma surgery. Neurosurg Focus 2020;48:E12.  Back to cited text no. 54
    
55.
Pak RW, Hadjiabadi DH, Senarathna J, Agarwal S, Thakor NV, Pillai JJ, et al. Implications of neurovascular uncoupling in functional magnetic resonance imaging (fMRI) of brain tumors. J Cereb Blood Flow Metab 2017;37:3475-87.  Back to cited text no. 55
    
56.
Metwali H, Raemaekers M, Kniese K, Kardavani B, Fahlbusch R, Samii A. Reliability of functional magnetic resonance imaging in patients with brain tumors: A critical review and meta-analysis. World Neurosurg 2019;125:183-90.  Back to cited text no. 56
    
57.
Liu P, De Vis JB, Lu H. Cerebrovascular reactivity (CVR) MRI with CO2 challenge: A technical review. Neuroimage 2019;187:104-15.  Back to cited text no. 57
    
58.
Champagne AA, Bhogal AA, Coverdale NS, Mark CI, Cook DJ. A novel perspective to calibrate temporal delays in cerebrovascular reactivity using hypercapnic and hyperoxic respiratory challenges. Neuroimage 2019;187:154-65.  Back to cited text no. 58
    
59.
Iranmahboob A, Peck KK, Brennan NP, Karimi S, Fisicaro R, Hou B, et al. Vascular reactivity maps in patients with gliomas using breath-holding BOLD fMRI. J Neuroimaging 2016;26:232-9.  Back to cited text no. 59
    
60.
Sorond FA, Hurwitz S, Salat DH, Greve DN, Fisher ND. Neurovascular coupling, cerebral white matter integrity, and response to cocoa in older people. Neurology 2013;81:904-9.  Back to cited text no. 60
    
61.
Hubbard NA, Turner M, Hutchison JL, Ouyang A, Strain J, Oasay L, et al. Multiple sclerosis-related white matter microstructural change alters the BOLD hemodynamic response. J Cereb Blood Flow Metab 2016;36:1872-84.  Back to cited text no. 61
    
62.
Goodkin R, Zaias B, Michelsen WJ. Arteriovenous malformation and glioma: Coexistent or sequential? J Neurosurg 1990;72:798-805.  Back to cited text no. 62
    
63.
Tran AN, Boyd NH, Walker K, Hjelmeland AB. NOS Expression and NO function in glioma and implications for patient therapies. Antioxid Redox Signal 2017;26:986-99.  Back to cited text no. 63
    
64.
Silva MA, See AP, Essayed WI, Golby AJ, Tie Y. Challenges and techniques for presurgical brain mapping with functional MRI. Neuroimage Clin 2018;17:794-803.  Back to cited text no. 64
    
65.
Sporns O, Chialvo DR, Kaiser M, Hilgetag CC. Organization, development and function of complex brain networks. Trends Cogn Sci 2004;8:418-25.  Back to cited text no. 65
    
66.
Park HJ, Friston K. Structural and functional brain networks: From connections to cognition. Science 2013;342:1238411.  Back to cited text no. 66
    
67.
Venkatesh HS, Morishita W, Geraghty AC, Silverbush D, Gillespie SM, Arzt M, et al. Electrical and synaptic integration of glioma into neural circuits. Nature 2019;573:539-45.  Back to cited text no. 67
    
68.
De Baene W, Rutten G, Sitskoom MM. Low-grade and high-grade glioma patients show different remote effects of the brain tumor on the functional network topology of the contralesional hemisphere. Neuro Oncol 2017;19 Suppl 3:iii41-2.  Back to cited text no. 68
    
69.
Yuan T, Zuo Z, Ying J, Jin L, Kang J, Gui S, et al. Structural and functional alterations in the contralesional medial temporal lobe in glioma patients. Front Neurosci 2020;14:10.  Back to cited text no. 69
    
70.
De Baene W, Rutten G, Sitskoom MM. Cognitive functional in glioma patients is related to functional connectivity measures of the non-tumoral hemisphere. Eur J Neurosci 2019;50:3921-33.  Back to cited text no. 70
    
71.
Derks J, Dirkson AR, de Witt Hamer PC, van Geest Q, Hulst HE, Barkhof F, et al. Connectomic profile and clinical phenotype in newly diagnosed glioma patients. Neuroimage Clin 2017;14:87-96.  Back to cited text no. 71
    
72.
Wefel JS, Noll KR, Rao G, Cahill DP. Neurocognitive function varies by IDH1 genetic mutation status in patients with malignant glioma prior to surgical resection. Neuro Oncol 2016;18:1656-63.  Back to cited text no. 72
    
73.
Derks J, Kulik S, Wesseling P, Numan T, Hillebrand A, van Dellen E, et al. Understanding cognitive functioning in glioma patients: The relevance of IDH-mutation status and functional connectivity. Brain Behav 2019;9:e01204.  Back to cited text no. 73
    
74.
Grisold A, Guekht A, Ruda R, Grisold S, Grisold W. Psychiatric alterations and behavioural changes in brain tumor patients. Neuro Oncol 2018;20:Iii253-4.  Back to cited text no. 74
    
75.
Menon V. Large-scale brain networks and psychopathology: A unifying triple network model. Trends Cogn Sci 2011;15:483-506.  Back to cited text no. 75
    
76.
Sheline YI, Barch DM, Price JL, Rundle MM, Vaishnavi SN, Snyder AZ, et al. The default mode network and self-referential processes in depression. Proc Natl Acad Sci U S A 2009;106:1942-7.  Back to cited text no. 76
    
77.
Esposito R, Mattei PA, Briganti C, Romani GL, Tartaro A, Caulo M. Modifications of default-mode network connectivity in patients with cerebral glioma. PLoS One 2012;7:e40231.  Back to cited text no. 77
    
78.
Singer S, Roick J, Danker H, Kortmann RD, Papsdorf K, Taubenheim S, et al. Psychiatric co-morbidity, distress, and use of psycho-social services in adult glioma patients-a prospective study. Acta Neurochir (Wien) 2018;160:1187-94.  Back to cited text no. 78
    
79.
Halkett GK, Lobb EA, Rogers MM, Shaw T, Long AP, Wheeler HR, et al. Predictors of distress and poorer quality of life in High Grade Glioma patients. Patient Educ Couns 2015;98:525-32.  Back to cited text no. 79
    
80.
Hahn A, Bode J, Krüwel T, Solecki G, Heiland S, Bendszus M, et al. Glioblastoma multiforme restructures the topological connectivity of cerebrovascular networks. Sci Rep 2019;9:11757.  Back to cited text no. 80
    



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