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Journal of Immunology Research
Volume 2017, Article ID 5813951, 16 pages
https://doi.org/10.1155/2017/5813951
Review Article

MRI in Glioma Immunotherapy: Evidence, Pitfalls, and Perspectives

1Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico “Carlo Besta”, Milan, Italy
2Postgraduate School in Radiodiagnostics, Università degli Studi di Milano, Milan, Italy
3Molecular Neuro-Oncology Unit, Fondazione IRCCS Istituto Neurologico “Carlo Besta”, Milan, Italy

Correspondence should be addressed to Maria Grazia Bruzzone; ti.atseb-otutitsi@enozzurb.airam

Received 1 December 2016; Revised 6 February 2017; Accepted 2 March 2017; Published 20 April 2017

Academic Editor: Cristina Maccalli

Copyright © 2017 Domenico Aquino et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Schuster, R. K. Lai, L. D. Recht et al., “A phase II, multicenter trial of rindopepimut (CDX-110) in newly diagnosed glioblastoma: the ACT III study,” Neuro-Oncology, vol. 17, no. 6, pp. 854–861, 2015. View at Publisher · View at Google Scholar · View at Scopus
  2. R. Stupp, W. P. Mason, M. J. van den Bent et al., “Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma,” The New England Journal of Medicine, vol. 352, no. 10, pp. 987–996, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. S. P. Weathers and M. R. Gilbert, “Current challenges in designing GBM trials for immunotherapy,” Journal of Neuro-Oncology, vol. 123, no. 3, pp. 331–337, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. N. Kamran, A. Calinescu, M. Candolfi et al., “Recent advances and future of immunotherapy for glioblastoma,” Expert Opinion on Biological Therapy, vol. 16, no. 10, pp. 1245–1264, 2016. View at Publisher · View at Google Scholar
  5. B. D. Liebelt, G. Finocchiaro, and A. B. Heimberger, “Principles of immunotherapy,” Handbook of Clinical Neurology, vol. 134, pp. 163–181, 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. E. Ishikawa, K. Tsuboi, T. Yamamoto et al., “Clinical trial of autologous formalin-fixed tumor vaccine for glioblastoma multiforme patients,” Cancer Science, vol. 98, no. 8, pp. 1226–1233, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. G. Finocchiaro and S. Pellegatta, “Novel mechanisms and approaches in immunotherapy for brain tumors,” Discovery Medicine, vol. 20, no. 108, pp. 7–15, 2015, November 2016, http://www.ncbi.nlm.nih.gov/pubmed/26321082. View at Google Scholar
  8. M. Preusser, M. Lim, D. A. Hafler, D. A. Reardon, and J. H. Sampson, “Prospects of immune checkpoint modulators in the treatment of glioblastoma,” Nature Reviews. Neurology, vol. 11, no. 9, pp. 504–514, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. L. W. Xu, K. K. H. Chow, M. Lim, and G. Li, “Current vaccine trials in glioblastoma: a review,” Journal of Immunology Research, vol. 2014, Article ID 796856, p. 10, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Brandsma, L. Stalpers, W. Taal, P. Sminia, and M. J. van den Bent, “Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas,” The Lancet Oncology, vol. 9, no. 5, pp. 453–461, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Radbruch, J. Fladt, P. Kickingereder et al., “Pseudoprogression in patients with glioblastoma: clinical relevance despite low incidence,” Neuro-Oncology, vol. 17, no. 1, pp. 151–159, 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. A. A. Brandes, E. Franceschi, A. Tosoni et al., “MGMT promoter methylation status can predict the incidence and outcome of pseudoprogression after concomitant radiochemotherapy in newly diagnosed glioblastoma patients,” Journal of Clinical Oncology, vol. 26, no. 13, pp. 2192–2197, 2008. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Abdulla, J. Saada, G. Johnson, S. Jefferies, and T. Ajithkumar, “Tumour progression or pseudoprogression? A review of post-treatment radiological appearances of glioblastoma,” Clinical Radiology, vol. 70, no. 11, pp. 1299–1312, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Okada, M. Weller, R. Huang et al., “Immunotherapy response assessment in neuro-oncology: a report of the RANO working group,” The Lancet Oncology, vol. 16, no. 15, pp. e534–e542, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. R. J. Young, A. Gupta, A. D. Shah et al., “Potential utility of conventional MRI signs in diagnosing pseudoprogression in glioblastoma,” Neurology, vol. 76, no. 22, pp. 1918–1924, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Law, S. Yang, H. Wang et al., “Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging,” AJNR. American Journal of Neuroradiology, vol. 24, no. 10, pp. 1989–1998, 2003, http://papers3://publication/uuid/C58DFDF1-444E-440B-BD8E-55C3C32585DE. View at Google Scholar
  17. M.-A. Weber, F. L. Giesel, and B. Stieltjes, “MRI for identification of progression in brain tumors: from morphology to function,” Expert Review of Neurotherapeutics, vol. 8, no. 10, pp. 1507–1525, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. J. H. Kim, K. H. Chang, D. G. Na et al., “3T 1H-MR spectroscopy in grading of cerebral gliomas: comparison of short and intermediate echo time sequences,” American Journal of Neuroradiology, vol. 27, no. 7, pp. 1412–1418, 2006. View at Google Scholar
  19. E. Lopci, C. Franzese, M. Grimaldi et al., “Imaging biomarkers in primary brain tumours,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 42, no. 4, pp. 597–612, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. N. Galldiks, I. Law, W. B. Pope, J. Arbizu, and K.-J. Langen, “The use of amino acid PET and conventional MRI for monitoring of brain tumor therapy,” NeuroImage Clinical, vol. 13, pp. 386–394, 2017. View at Publisher · View at Google Scholar
  21. G. Pöpperl, C. Götz, W. Rachinger et al., “Serial O-(2-[(18)F]fluoroethyl)-L: -tyrosine PET for monitoring the effects of intracavitary radioimmunotherapy in patients with malignant glioma,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 33, no. 7, pp. 792–800, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Chiba, M. Kinoshita, Y. Okita et al., “Use of (11)C-methionine PET parametric response map for monitoring WT1 immunotherapy response in recurrent malignant glioma,” Journal of Neurosurgery, vol. 116, no. 4, pp. 835–842, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. T. J. Kruser, M. P. Mehta, and H. I. Robins, “Pseudoprogression after glioma therapy: a comprehensive review,” Expert Review of Neurotherapeutics, vol. 13, no. 4, pp. 389–403, 2013. View at Publisher · View at Google Scholar · View at Scopus
  24. J. D. Wolchok, A. Hoos, S. O’Day et al., “Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria,” Clinical Cancer Research, vol. 15, no. 23, pp. 7412–7420, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. M. M. Smith, J. E. Thompson, M. Castillo et al., “MR of recurrent high-grade astrocytomas after intralesional immunotherapy,” AJNR. American Journal of Neuroradiology, vol. 17, no. 6, pp. 1065–1071, 1996, November 2016, http://www.ncbi.nlm.nih.gov/pubmed/8791917. View at Google Scholar
  26. P. Y. Wen, D. R. Macdonald, D. A. Reardon et al., “Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group,” Journal of Clinical Oncology, vol. 28, no. 11, pp. 1963–1972, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. D.-S. Kong, S. T. Kim, E.-H. Kim et al., “Diagnostic dilemma of pseudoprogression in the treatment of newly diagnosed glioblastomas: the role of assessing relative cerebral blood flow volume and oxygen-6-methylguanine-DNA methyltransferase promoter methylation status,” American Journal of Neuroradiology, vol. 32, no. 2, pp. 382–387, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. H. Okada, P. Kalinski, R. Ueda et al., “Induction of CD8+ T-cell responses against novel glioma–associated antigen peptides and clinical activity by vaccinations with α-type 1 polarized dendritic cells and polyinosinic-polycytidylic acid stabilized by lysine and carboxymethylcellulose in patients with recurrent malignant glioma,” Journal of Clinical Oncology, vol. 29, no. 3, pp. 330–336, 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. L. Zach, D. Guez, D. Last et al., “Delayed contrast extravasation MRI: a new paradigm in neuro-oncology,” Neuro-Oncology, vol. 17, no. 3, pp. 457–465, 2015. View at Publisher · View at Google Scholar
  30. D. Daniels, D. Guez, D. Last et al., “Early biomarkers from conventional and delayed-contrast MRI to predict the response to bevacizumab in recurrent high-grade gliomas,” American Journal of Neuroradiology, vol. 37, no. 11, pp. 2003–2009, 2016. View at Publisher · View at Google Scholar
  31. L. Ostergaard, A. G. Sorensen, K. K. Kwong, R. M. Weisskoff, C. Gyldensted, and B. R. Rosen, “High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: experimental comparison and preliminary results,” Magnetic Resonance in Medicine, vol. 36, no. 5, pp. 726–736, 1996, April 2016, http://www.ncbi.nlm.nih.gov/pubmed/8916023. View at Publisher · View at Google Scholar · View at Scopus
  32. J. Folkman, “Angiogenesis,” Annual Review of Medicine, vol. 57, pp. 1–18, 2006. View at Publisher · View at Google Scholar · View at Scopus
  33. H. J. Aronen, I. E. Gazit, D. N. Louis et al., “Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings,” Radiology, vol. 191, no. 1, pp. 41–51, 1994. View at Publisher · View at Google Scholar
  34. N. Morita, S. Wang, S. Chawla, H. Poptani, and E. R. Melhem, “Dynamic susceptibility contrast perfusion weighted imaging in grading of nonenhancing astrocytomas,” Journal of Magnetic Resonance Imaging, vol. 32, no. 4, pp. 803–808, 2010. View at Publisher · View at Google Scholar
  35. K. Mitsuya, Y. Nakasu, S. Horiguchi et al., “Perfusion weighted magnetic resonance imaging to distinguish the recurrence of metastatic brain tumors from radiation necrosis after stereotactic radiosurgery,” Journal of Neuro-Oncology, vol. 99, no. 1, pp. 81–88, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. M. Law, S. Oh, J. S. Babb et al., “Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging—prediction of patient clinical response,” Radiology, vol. 238, no. 2, pp. 658–667, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. M. Law, R. J. Young, J. S. Babb et al., “Gliomas: predicting time to progression or survival with cerebral blood volume measurements,” Radiology, vol. 247, no. 2, pp. 490–498, 2008. View at Publisher · View at Google Scholar · View at Scopus
  38. P. S. Tofts, “T1-weighted DCE imaging concepts: modelling, acquisition and analysis,” MAGNETOM Flash, vol. 45, no. 3, pp. 30–39, 2010. View at Google Scholar
  39. P. S. Tofts and A. G. Kermode, “Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts,” Magnetic Resonance in Medicine, vol. 17, no. 2, pp. 357–367, 1991. View at Publisher · View at Google Scholar · View at Scopus
  40. S. J. Mills, D. du Plessis, P. Pal et al., “Mitotic activity in glioblastoma correlates with estimated extravascular extracellular space derived from dynamic contrast-enhanced MR imaging,” AJNR. American Journal of Neuroradiology, vol. 37, no. 5, pp. 811–817, 2016. View at Publisher · View at Google Scholar · View at Scopus
  41. A. K. Heye, R. D. Culling, M. C. del Valdés Hernández, M. J. Thrippleton, and J. M. Wardlaw, “Assessment of blood–brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review,” NeuroImage Clinical, vol. 6, pp. 262–274, 2014. View at Publisher · View at Google Scholar · View at Scopus
  42. D. S. Williams, J. A. Detre, J. S. Leigh, and A. P. Koretsky, “Magnetic resonance imaging of perfusion using spin inversion of arterial water,” Proceedings of the National Academy of Sciences of the United States of America, vol. 89, no. 1, pp. 212–216, 1992, May 2016, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=48206&tool=pmcentrez&rendertype=abstract. View at Google Scholar
  43. W. Dai, D. Garcia, C. De Bazelaire, and D. C. Alsop, “Continuous flow-driven inversion for arterial spin labeling using pulsed radio frequency and gradient fields,” Magnetic Resonance in Medicine, vol. 60, no. 6, pp. 1488–1497, 2008. View at Publisher · View at Google Scholar · View at Scopus
  44. T. T. Batchelor, E. R. Gerstner, K. E. Emblem et al., “Improved tumor oxygenation and survival in glioblastoma patients who show increased blood perfusion after cediranib and chemoradiation,” Proceedings of the National Academy of Sciences of the United States of America, vol. 110, no. 47, pp. 19059–19064, 2013. View at Publisher · View at Google Scholar · View at Scopus
  45. R. F. Barajas, J. S. Chang, M. R. Segal et al., “Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging,” Radiology, vol. 253, no. 2, pp. 486–496, 2009. View at Publisher · View at Google Scholar · View at Scopus
  46. L. S. Hu, L. C. Baxter, K. A. Smith et al., “Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements,” American Journal of Neuroradiology, vol. 30, no. 3, pp. 552–558, 2009. View at Publisher · View at Google Scholar · View at Scopus
  47. E. R. Gerstner, K. E. Emblem, and G. A. Sorensen, “Vascular magnetic resonance imaging in brain tumors during antiangiogenic therapy - are we there yet?” The Cancer Journal, vol. 21, no. 4, pp. 337–342, 2015. View at Publisher · View at Google Scholar · View at Scopus
  48. R. Mangla, G. Singh, D. Ziegelitz et al., “Changes in relative cerebral blood volume 1 month after radiation-temozolomide therapy can help predict overall survival in patients with glioblastoma,” Radiology, vol. 256, no. 2, pp. 575–584, 2010. View at Publisher · View at Google Scholar · View at Scopus
  49. C. J. Galbán, T. L. Chenevert, C. R. Meyer et al., “The parametric response map is an imaging biomarker for early cancer treatment outcome,” Nature Medicine, vol. 15, no. 5, pp. 572–576, 2009. View at Publisher · View at Google Scholar · View at Scopus
  50. D. Aquino, A. L. Di Stefano, A. Scotti et al., “Parametric response maps of perfusion MRI may identify recurrent glioblastomas responsive to bevacizumab and irinotecan,” PloS One, vol. 9, no. 3, article e90535, 2014. View at Publisher · View at Google Scholar · View at Scopus
  51. Y. J. Choi, H. S. Kim, G.-H. Jahng, S. J. Kim, and D. C. Suh, “Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging,” Acta Radiologica, vol. 54, no. 4, pp. 448–454, 2013. View at Publisher · View at Google Scholar · View at Scopus
  52. S. Wang, M. Martinez-Lage, Y. Sakai et al., “Differentiating tumor progression from pseudoprogression in patients with glioblastomas using diffusion tensor imaging and dynamic susceptibility contrast MRI,” American Journal of Neuroradiology, vol. 37, no. 1, pp. 28–36, 2016. View at Publisher · View at Google Scholar · View at Scopus
  53. R. J. Young, A. Gupta, A. D. Shah et al., “MRI perfusion in determining pseudoprogression in patients with glioblastoma,” Clinical Imaging, vol. 37, no. 1, pp. 41–49, 2013. View at Publisher · View at Google Scholar · View at Scopus
  54. S. Gahramanov, A. M. Raslan, L. L. Muldoon et al., “Potential for differentiation of pseudoprogression from true tumor progression with dynamic susceptibility-weighted contrast-enhanced magnetic resonance imaging using ferumoxytol vs. gadoteridol: a pilot study,” International Journal of Radiation Oncology, Biology, Physics, vol. 79, no. 2, pp. 514–523, 2011. View at Publisher · View at Google Scholar · View at Scopus
  55. A. Xyda, U. Haberland, E. Klotz et al., “Diagnostic performance of whole brain volume perfusion CT in intra-axial brain tumors: preoperative classification accuracy and histopathologic correlation,” European Journal of Radiology, vol. 81, no. 12, pp. 4105–4111, 2012. View at Publisher · View at Google Scholar · View at Scopus
  56. T. B. Nguyen, G. O. Cron, K. Perdrizet et al., “Comparison of the diagnostic accuracy of DSC- and dynamic contrast-enhanced MRI in the preoperative grading of astrocytomas,” AJNR. American Journal of Neuroradiology, vol. 36, no. 11, pp. 2017–2022, 2015. View at Publisher · View at Google Scholar · View at Scopus
  57. C. Santarosa, A. Castellano, G. M. Conte et al., “Dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging for glioma grading: preliminary comparison of vessel compartment and permeability parameters using hotspot and histogram analysis,” European Journal of Radiology, vol. 85, no. 6, pp. 1147–1156, 2016. View at Publisher · View at Google Scholar · View at Scopus
  58. D. Bonekamp, K. Deike, B. Wiestler et al., “Association of overall survival in patients with newly diagnosed glioblastoma with contrast-enhanced perfusion MRI: comparison of intraindividually matched T1 - and T2* -based bolus techniques,” Journal of Magnetic Resonance Imaging, vol. 42, no. 1, pp. 87–96, 2015. View at Publisher · View at Google Scholar · View at Scopus
  59. A. A. Thomas, J. Arevalo-Perez, T. Kaley et al., “Dynamic contrast enhanced T1 MRI perfusion differentiates pseudoprogression from recurrent glioblastoma,” Journal of Neuro-Oncology, vol. 125, no. 1, pp. 183–190, 2015. View at Publisher · View at Google Scholar · View at Scopus
  60. M.-A. Weber, A. Kroll, M. Günther et al., “Nichtinvasive Messung des relativen zerebralen Blutflusses mit der MR-Blutbolusmarkierungstechnik (arterial-spin-labeling): Physikalische Grundlagen und klinische Anwendungen,” Radiologe, vol. 44, no. 2, pp. 164–174, 2004. View at Publisher · View at Google Scholar · View at Scopus
  61. J. Petr, I. Platzek, A. Seidlitz et al., “Early and late effects of radiochemotherapy on cerebral blood flow in glioblastoma patients measured with non-invasive perfusion MRI,” Radiotherapy and Oncology, vol. 118, no. 1, pp. 24–28, 2016. View at Publisher · View at Google Scholar · View at Scopus
  62. D. Van Westen, E. T. Petersen, R. Wirestam et al., “Correlation between arterial blood volume obtained by arterial spin labelling and cerebral blood volume in intracranial tumours,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 24, no. 4, pp. 211–223, 2011. View at Publisher · View at Google Scholar · View at Scopus
  63. T. Abe, Y. Mizobuchi, W. Sako et al., “Clinical significance of discrepancy between arterial spin labeling images and contrast-enhanced images in the diagnosis of brain tumors,” Magnetic Resonance in Medical Sciences, vol. 14, no. 4, pp. 313–319, 2015. View at Publisher · View at Google Scholar · View at Scopus
  64. C. M. White, W. B. Pope, T. Zaw et al., “Regional and voxel-wise comparisons of blood flow measurements between dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) and arterial spin labeling (ASL) in brain tumors,” Journal of Neuroimaging, vol. 24, no. 1, pp. 23–30, 2014. View at Publisher · View at Google Scholar · View at Scopus
  65. E. Nyberg, J. Honce, B. K. Kleinschmidt-DeMasters, B. Shukri, S. Kreidler, and L. Nagae, “Arterial spin labeling: pathologically proven superiority over conventional MRI for detection of high-grade glioma progression after treatment,” The Neuroradiology Journal, vol. 29, no. 5, pp. 377–383, 2016. View at Publisher · View at Google Scholar
  66. A. Seeger, C. Braun, M. Skardelly et al., “Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease,” Academic Radiology, vol. 20, no. 12, pp. 1557–1565, 2013. View at Publisher · View at Google Scholar · View at Scopus
  67. M. Vrabec, S. Van Cauter, U. Himmelreich et al., “MR perfusion and diffusion imaging in the follow-up of recurrent glioblastoma treated with dendritic cell immunotherapy: a pilot study,” Neuroradiology, vol. 53, no. 10, pp. 721–731, 2011. View at Publisher · View at Google Scholar · View at Scopus
  68. L. Stenberg, E. Englund, R. Wirestam, P. Siesjo, L. G. Salford, and E. M. Larsson, “Dynamic susceptibility contrast-enhanced perfusion magnetic resonance (MR) imaging combined with contrast-enhanced MR imaging in the follow-up of immunogene-treated glioblastoma multiforme,” Acta Radiologica, vol. 47, no. 8, pp. 852–861, 2006. View at Publisher · View at Google Scholar · View at Scopus
  69. J. V. Cohen, A. K. Alomari, A. O. Vortmeyer et al., “Melanoma brain metastasis pseudoprogression after pembrolizumab treatment,” Cancer Immunology Research, vol. 4, no. 3, pp. 179–182, 2016. View at Publisher · View at Google Scholar · View at Scopus
  70. C. Brekke Rygh, J. Wang, M. Thuen et al., “Dynamic contrast enhanced MRI detects early response to adoptive NK cellular immunotherapy targeting the NG2 proteoglycan in a rat model of glioblastoma,” PloS One, vol. 9, no. 9, article e108414, 2014. View at Publisher · View at Google Scholar · View at Scopus
  71. R. J. McDonald, J. S. McDonald, D. F. Kallmes et al., “Intracranial gadolinium deposition after contrast-enhanced MR imaging,” Radiology, vol. 275, no. 3, pp. 772–782, 2015. View at Publisher · View at Google Scholar · View at Scopus
  72. D. Le Bihan, R. Turner, P. Douek, and N. Patronas, “Diffusion MR imaging: clinical applications,” AJR. American Journal of Roentgenology, vol. 159, no. 3, pp. 591–599, 1992. View at Publisher · View at Google Scholar
  73. T. Sugahara, Y. Korogi, M. Kochi et al., “Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas,” Journal of Magnetic Resonance Imaging, vol. 9, no. 1, pp. 53–60, 1999. View at Publisher · View at Google Scholar
  74. F. W. Crawford and S. J. Nelson, “Evaluation of MR markers that predict survival in patients with newly diagnosed GBM prior to adjuvant therapy,” Perfusion, vol. 91, no. 1, pp. 69–81, 2011. View at Publisher · View at Google Scholar · View at Scopus
  75. B. A. Moffat, T. L. Chenevert, T. S. Lawrence et al., “Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response,” in Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 15, pp. 5524–5529, 2005. View at Publisher · View at Google Scholar · View at Scopus
  76. P. J. Basser, J. Mattiello, and D. LeBihan, “MR diffusion tensor spectroscopy and imaging,” Biophysical Journal, vol. 66, no. 1, pp. 259–267, 1994. View at Publisher · View at Google Scholar
  77. J. Hrabe, G. Kaur, and D. N. Guilfoyle, “Principles and limitations of NMR diffusion measurements,” Journal of Medical Physics, vol. 32, no. 1, pp. 34–42, 2007. View at Publisher · View at Google Scholar
  78. V. Z. Miloushev, D. S. Chow, and C. G. Filippi, “Meta-analysis of diffusion metrics for the prediction of tumor grade in gliomas,” American Journal of Neuroradiology, vol. 36, no. 2, pp. 302–308, 2015. View at Publisher · View at Google Scholar · View at Scopus
  79. P. C. Sundgren, X. Fan, P. Weybright et al., “Differentiation of recurrent brain tumor versus radiation injury using diffusion tensor imaging in patients with new contrast-enhancing lesions,” Magnetic Resonance Imaging, vol. 24, no. 9, pp. 1131–1142, 2006. View at Publisher · View at Google Scholar · View at Scopus
  80. S. Wang and J. Zhou, “Diffusion tensor magnetic resonance imaging of rat glioma models: a correlation study of MR imaging and histology,” Journal of Computer Assisted Tomography, vol. 36, no. 6, pp. 739–744, 2012. View at Publisher · View at Google Scholar · View at Scopus
  81. W. Yuan, S. K. Holland, B. V. Jones, K. Crone, and F. T. Mangano, “Characterization of abnormal diffusion properties of supratentorial brain tumors: a preliminary diffusion tensor imaging study,” Journal of Neurosurgery. Pediatrics, vol. 1, no. 4, pp. 263–269, 2008. View at Publisher · View at Google Scholar · View at Scopus
  82. A. Jakab, P. Molnár, M. Emri, and E. Berényi, “Glioma grade assessment by using histogram analysis of diffusion tensor imaging-derived maps,” Neuroradiology, vol. 53, no. 7, pp. 483–491, 2011. View at Publisher · View at Google Scholar · View at Scopus
  83. A. Castellano and A. Falini, “Progress in neuro-imaging of brain tumors,” Current Opinion in Oncology, vol. 28, no. 6, pp. 484–493, 2016. View at Publisher · View at Google Scholar
  84. V. Baliyan, C. J. Das, R. Sharma, and A. K. Gupta, “Diffusion weighted imaging: technique and applications,” World Journal of Radiology, vol. 8, no. 9, pp. 785–798, 2016. View at Publisher · View at Google Scholar
  85. K. M. Schmainda, “Diffusion-weighted MRI as a biomarker for treatment response in glioma,” CNS Oncology, vol. 1, no. 2, pp. 169–180, 2012. View at Publisher · View at Google Scholar
  86. M. Nowosielski, W. Recheis, G. Goebel et al., “ADC histograms predict response to anti-angiogenic therapy in patients with recurrent high-grade glioma,” Neuroradiology, vol. 53, no. 4, pp. 291–302, 2011. View at Publisher · View at Google Scholar · View at Scopus
  87. B. M. Ellingson, T. F. Cloughesy, A. Lai et al., “Graded functional diffusion map-defined characteristics of apparent diffusion coefficients predict overall survival in recurrent glioblastoma treated with bevacizumab,” Neuro-Oncology, vol. 13, no. 10, pp. 1151–1161, 2011. View at Publisher · View at Google Scholar · View at Scopus
  88. R. Rahman, A. Hamdan, R. Zweifler et al., “Histogram analysis of apparent diffusion coefficient within enhancing and nonenhancing tumor volumes in recurrent glioblastoma patients treated with bevacizumab,” Journal of Neuro-Oncology, vol. 119, no. 1, pp. 149–158, 2014. View at Publisher · View at Google Scholar · View at Scopus
  89. Y. S. Song, S. H. Choi, C. K. Park et al., “True progression versus pseudoprogression in the treatment of glioblastomas: a comparison study of normalized cerebral blood volume and apparent diffusion coefficient by histogram analysis,” Korean Journal of Radiology, vol. 14, no. 4, pp. 662–672, 2013. View at Publisher · View at Google Scholar · View at Scopus
  90. M. Bulik, T. Kazda, P. Slampa, and R. Jancalek, “The diagnostic ability of follow-up imaging biomarkers after treatment of glioblastoma in the temozolomide era: implications from proton MR spectroscopy and apparent diffusion coefficient mapping,” BioMed Research International, vol. 2015, Article ID 641023, p. 9, 2015. View at Publisher · View at Google Scholar · View at Scopus
  91. T. Kazda, M. Bulik, P. Pospisil et al., “Advanced MRI increases the diagnostic accuracy of recurrent glioblastoma: single institution thresholds and validation of MR spectroscopy and diffusion weighted MR imaging,” NeuroImage Clinical, vol. 11, pp. 316–321, 2016. View at Publisher · View at Google Scholar · View at Scopus
  92. H. Kashimura, T. Inoue, T. Beppu, K. Ogasawara, and A. Ogawa, “Diffusion tensor imaging for differentiation of recurrent brain tumor and radiation necrosis after radiotherapy-three case reports,” Clinical Neurology and Neurosurgery, vol. 109, no. 1, pp. 106–110, 2007. View at Publisher · View at Google Scholar · View at Scopus
  93. S. Wang, M. Martinez-Lage, Y. Sakai et al., “Differentiating tumor progression from pseudoprogression in patients with glioblastomas using diffusion tensor imaging and dynamic susceptibility contrast MRI,” AJNR. American Journal of Neuroradiology, vol. 37, no. 1, pp. 28–36, 2016. View at Publisher · View at Google Scholar · View at Scopus
  94. X. Qian, H. Tan, J. Zhang, W. Zhao, M. D. Chan, and X. Zhou, “Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation,” Medical Physics, vol. 43, no. 11, pp. 5889–5902, 2016. View at Publisher · View at Google Scholar
  95. V. Sawlani, “Diffusion-weighted imaging and apparent diffusion coefficient evaluation of herpes simplex encephalitis and Japanese encephalitis,” Journal of the Neurological Sciences, vol. 287, no. 1-2, pp. 221–226, 2009. View at Publisher · View at Google Scholar · View at Scopus
  96. G. Luthra, A. Parihar, K. Nath et al., “Comparative evaluation of fungal, tubercular, and pyogenic brain abscesses with conventional and diffusion MR imaging and proton MR spectroscopy,” American Journal of Neuroradiology, vol. 28, no. 7, pp. 1332–1338, 2007. View at Publisher · View at Google Scholar · View at Scopus
  97. R. Ceschin, B. F. Kurland, S. R. Abberbock et al., “Parametric response mapping of apparent diffusion coefficient as an imaging biomarker to distinguish pseudoprogression from TrueTumor progression in peptide-based vaccine therapy for pediatric diffuse intrinsic pontine glioma,” American Journal of Neuroradiology, vol. 36, no. 11, pp. 2170–2176, 2015. View at Publisher · View at Google Scholar · View at Scopus
  98. L. Qin, X. Li, A. Stroiney et al., “Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma,” Neuroradiology, vol. 59, no. 2, pp. 1–11, 2017. View at Publisher · View at Google Scholar
  99. O. C. Andronesi, F. Loebel, W. Bogner et al., “Treatment response assessment in IDH-mutant glioma patients by noninvasive 3D functional spectroscopic mapping of 2-hydroxyglutarate,” Clinical Cancer Research, vol. 22, no. 7, pp. 1632–1641, 2016. View at Publisher · View at Google Scholar · View at Scopus
  100. C. Choi, J. M. Raisanen, S. K. Ganji et al., “Prospective longitudinal analysis of 2-hydroxyglutarate magnetic resonance spectroscopy identifies broad clinical utility for the management of patients with IDH-mutant glioma,” Journal of Clinical Oncology, vol. 34, no. 33, pp. 4030–4039, 2016. View at Publisher · View at Google Scholar
  101. D. N. Louis, A. Perry, G. Reifenberger et al., “The 2016 World Health Organization classification of tumors of the central nervous system: a summary,” Acta Neuropathologica, vol. 131, no. 6, pp. 803–820, 2016. View at Publisher · View at Google Scholar · View at Scopus
  102. G. Finocchiaro and S. Pellegatta, “Perspectives for immunotherapy in glioblastoma treatment,” Current Opinion in Oncology, vol. 26, no. 6, pp. 608–614, 2014. View at Publisher · View at Google Scholar · View at Scopus
  103. A. Pajot, M.-L. Michel, N. Fazilleau et al., “A mouse model of human adaptive immune functions: HLA-A2.1-/HLA-DR1-transgenic H-2 class I-/class II-knockout mice HLA class I and II transgenic mice/H-2 class I and II KO mice/CTL/HTL/human immune response,” European Journal of Immunology, vol. 34, no. 11, pp. 3060–3069, 2004. View at Publisher · View at Google Scholar · View at Scopus
  104. J. Kalpathy-Cramer, E. R. Gerstner, K. E. Emblem, O. C. Andronesi, and B. Rosen, “Advanced magnetic resonance imaging of the physical processes in human glioblastoma,” Cancer Research, vol. 74, no. 17, pp. 4622–4637, 2014. View at Publisher · View at Google Scholar · View at Scopus
  105. S. J. Nelson, “Assessment of therapeutic response and treatment planning for brain tumors using metabolic and physiological MRI,” NMR in Biomedicine, vol. 24, no. 6, pp. 734–749, 2011. View at Publisher · View at Google Scholar · View at Scopus
  106. H. Zhang, L. Ma, Q. Wang, X. Zheng, C. Wu, and B. Xu, “Role of magnetic resonance spectroscopy for the differentiation of recurrent glioma from radiation necrosis: a systematic review and meta-analysis,” European Journal of Radiology, vol. 83, no. 12, pp. 2181–2189, 2014. View at Publisher · View at Google Scholar · View at Scopus
  107. L. C. Hygino da Cruz, I. Rodriguez, R. C. Domingues, E. L. Gasparetto, and A. G. Sorensen, “Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma,” American Journal of Neuroradiology, vol. 32, no. 11, pp. 1978–1985, 2011. View at Publisher · View at Google Scholar · View at Scopus
  108. T. Kaminaga and K. Shirai, “Radiation-induced brain metabolic changes in the acute and early delayed phase detected with quantitative proton magnetic resonance spectroscopy,” Journal of Computer Assisted Tomography, vol. 29, no. 3, pp. 293–297, 2005, January 2017, http://www.ncbi.nlm.nih.gov/pubmed/15891493. View at Google Scholar
  109. F. W. Floeth, H. J. Wittsack, V. Engelbrecht, and F. Weber, “Comparative follow-up of enhancement phenomena with MRI and proton MR spectroscopic imaging after intralesional immunotherapy in glioblastoma - report of two exceptional cases,” Zentralblatt für Neurochirurgie, vol. 63, no. 1, pp. 23–28, 2002. View at Publisher · View at Google Scholar · View at Scopus
  110. V. Lawson, “Turned on by danger: activation of CD1d-restricted invariant natural killer T cells,” Immunology, vol. 137, no. 1, pp. 20–27, 2012. View at Publisher · View at Google Scholar · View at Scopus
  111. S. Pellegatta, M. Eoli, S. Frigerio et al., “The natural killer cell response and tumor debulking are associated with prolonged survival in recurrent glioblastoma patients receiving dendritic cells loaded with autologous tumor lysates,” Oncoimmunology, vol. 2, no. 3, article e23401, 2013. View at Publisher · View at Google Scholar · View at Scopus
  112. E. M. Haacke, S. Mittal, Z. Wu, J. Neelavalli, and Y.-C. N. Cheng, “Susceptibility-weighted imaging: technical aspects and clinical applications, part 1,” AJNR. American Journal of Neuroradiology, vol. 30, no. 1, pp. 19–30, 2009. View at Publisher · View at Google Scholar · View at Scopus
  113. W. Mohammed, H. Xunning, S. Haibin, and M. Jingzhi, “Clinical applications of susceptibility-weighted imaging in detecting and grading intracranial gliomas: a review,” Cancer Imaging, vol. 13, no. 2, pp. 186–195, 2013. View at Publisher · View at Google Scholar · View at Scopus
  114. C. Li, B. Ai, Y. Li, H. Qi, and L. Wu, “Susceptibility-weighted imaging in grading brain astrocytomas,” European Journal of Radiology, vol. 75, no. 1, pp. e81–e85, 2010. View at Publisher · View at Google Scholar · View at Scopus
  115. X. Li, Y. Zhu, H. Kang et al., “Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging,” Cancer Imaging, vol. 15, no. 1, p. 4, 2015. View at Publisher · View at Google Scholar · View at Scopus
  116. Y. Ding, Z. Xing, B. Liu, X. Lin, and D. Cao, “Differentiation of primary central nervous system lymphoma from high-grade glioma and brain metastases using susceptibility-weighted imaging,” Brain and Behavior: A Cognitive Neuroscience Perspective, vol. 4, no. 6, pp. 841–849, 2014. View at Publisher · View at Google Scholar · View at Scopus
  117. C. C. T. Hsu, T. W. Watkins, G. N. C. Kwan, and E. M. Haacke, “Susceptibility-weighted imaging of glioma: update on current imaging status and future directions,” Journal of Neuroimaging, vol. 26, no. 4, pp. 383–390, 2016. View at Publisher · View at Google Scholar · View at Scopus
  118. M. J. Park, H. S. Kim, G. H. Jahng, C. W. Ryu, S. M. Park, and S. Y. Kim, “Semiquantitative assessment of intratumoral susceptibility signals using non-contrast-enhanced high-field high-resolution susceptibility-weighted imaging in patients with gliomas: comparison with MR perfusion imaging,” American Journal of Neuroradiology, vol. 30, no. 7, pp. 1402–1408, 2009. View at Publisher · View at Google Scholar · View at Scopus
  119. W. Bian, C. P. Hess, S. M. Chang, S. J. Nelson, and J. M. Lupo, “Susceptibility-weighted MR imaging of radiation therapy-induced cerebral microbleeds in patients with glioma: a comparison between 3T and 7T,” Neuroradiology, vol. 56, no. 2, pp. 91–96, 2014. View at Publisher · View at Google Scholar · View at Scopus
  120. J. M. Lupo, E. Essock-Burns, A. M. Molinaro et al., “Using susceptibility-weighted imaging to determine response to combined anti-angiogenic, cytotoxic, and radiation therapy in patients with glioblastoma multiforme,” Neuro-Oncology, vol. 15, no. 4, pp. 480–489, 2013. View at Publisher · View at Google Scholar · View at Scopus
  121. S. Peters, R. Pahl, A. Claviez, and O. Jansen, “Detection of irreversible changes in susceptibility-weighted images after whole-brain irradiation of children,” Neuroradiology, vol. 55, no. 7, pp. 853–859, 2013. View at Publisher · View at Google Scholar · View at Scopus
  122. H. J. Baek, H. S. Kim, N. Kim, Y. J. Choi, and Y. J. Kim, “(Histographic pattern) percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas,” Radiology, vol. 264, no. 3, pp. 834–843, 2012. View at Publisher · View at Google Scholar · View at Scopus
  123. H. H. Chu, S. H. Choi, I. Ryoo et al., “Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted imaging,” Radiology, vol. 269, no. 3, pp. 831–840, 2013. View at Publisher · View at Google Scholar · View at Scopus
  124. W. B. Pope, H. J. Kim, J. Huo et al., “Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment,” Radiology, vol. 252, no. 1, pp. 182–189, 2009. View at Publisher · View at Google Scholar · View at Scopus
  125. M. Nowosielski, W. Recheis, G. Goebel et al., “ADC histograms predict response to anti-angiogenic therapy in patients with recurrent high-grade glioma,” Neuroradiology, vol. 53, no. 4, pp. 291–302, 2011. View at Publisher · View at Google Scholar · View at Scopus
  126. C. Tsien, C. J. Galbán, T. L. Chenevert et al., “Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma,” Journal of Clinical Oncology, vol. 28, no. 13, pp. 2293–2299, 2010. View at Publisher · View at Google Scholar · View at Scopus