References
1. Drake LR, Hillmer AT, Cai Z. Approaches to PET imaging of glioblastoma. Molecules 2020; 25: 568. doi: 10.3390/molecules25030568
2. Reza SMS, Samad MD, Shboul ZA, Jones KA, Iftekharuddin KM. Glioma grading using structural magnetic resonance imaging and molecular data. J Med Imaging (Bellingham) 2019, 6: 024501. doi: 10.1117/1.JMI.6.2.024501
3. Chiang GC, Kovanlikaya I, Choi C, Ramakrishna R, Magge R, Shungu DC. Magnetic resonance spectroscopy, positron emission tomography and radiogenomics-relevance to glioma. Front Neurol 2018, 9: 33. doi: 10.3389/fneur.2018.00033
4. Frosina G. Positron emission tomography of high-grade gliomas. J Neurooncol 2016, 127: 415-25. doi: 10.1007/s11060-016-2077-1
5. Holzgreve A, Albert NL, Galldiks N, Suchorska B. Use of PET imaging in neuro-oncological surgery. Cancers (Basel) 2021, 13: 2093. doi: 10.3390/cancers13092093
6. Moreau A, Febvey O, Mognetti T, Frappaz D, Kryza D. Contribution of different positron emission tomography tracers in glioma management: focus on glioblastoma. Front Oncol 2019, 9: 1134. doi: 10.3389/fonc.2019.01134
7. Cook GJ, Maisey MN, Fogelman I. Normal variants, artefacts and interpretative pitfalls in PET imaging with 18-fluoro-2-deoxyglucose and carbon-11 methionine. Eur J Nucl Med 1999, 26: 1363–78. doi: 10.1007/s002590050597
8. Culverwell AD, Scarsbrook AF, Chowdhury FU. False-positive uptake on 2-[18F]-fluoro-2-deoxy-D-glucose (FDG) positron-emission tomography/computed tomography (PET/CT) in oncological imaging. Clin Radiol 2011, 66: 366–82. doi: doi.org/10.1016/j.crad.2010.12.004
9. Nozaki S, Nakatani Y, Mawatari A, Shibata N, Hume WE, Hayashinaka Eet al. 18F-FIMP: a LAT1-specific PET probe for discrimination between tumor tissue and inflammation. Sci Rep 2019, 9: 15718. doi: 10.1038/s41598-019-52270-x
10. Fordham AJ, Hacherl CC, Patel N, Jones K, Myers B, Abraham M et al. Differentiating glioblastomas from solitary brain metastases: an update on the current literature of advanced imaging modalities. Cancers (Basel) 2021, 13: 2960. doi: 10.3390/cancers13122960
11. Brennan CW, Verhaak RG, McKenna A, Campos B, Noushmehr H, Salama SR et al. The somatic genomic landscape of glioblastoma. Cell 2013,155: 462-77. doi: 10.1016/j.cell.2013.09.034
12. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 2016, 131: 803-20. doi: 10.1007/s00401-016-1545-1
13. Roesler R, Brunetto AT, Abujamra AL, de Farias CB, Brunetto AL, Schwartsmann G. Current and emerging molecular targets in glioma. Expert Rev. Anticancer Ther 2010, 10: 1735-51. doi: 10.1586/era.10.167
14. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ,et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005, 352: 987-96. doi: 10.1056/NEJMoa043330
15. Wen PY, Kesari S. Malignant gliomas in adults. N Engl J Med 2008,359: 492-507. doi: 10.1056/NEJMra0708126
16. Alghamri MS, McClellan BL, Hartlage MS, Haase S, Faisal SM, Thalla R, et al. Targeting neuroinflammation in brain cancer: uncovering mechanisms, pharmacological targets, and neuropharmaceutical developments. Front Pharmacol 2021, 12: 680021. doi: 10.3389/fphar.2021.680021
17. Arimappamagan A, Somasundaram K, Thennarasu K, Peddagangannagari S, Srinivasan H, Shailaja BC, et al. A fourteen gene GBM prognostic signature identifies association of immune response pathway and mesenchymal subtype with high risk group. PLoS One 2013, 8:e62042. doi: 10.1371/journal.pone.0062042
18. Carro MS, Lim WK, Alvarez MJ, Bollo RJ, Zhao X, Snyder EY, et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 2010, 463: 318-25. doi: 10.1038/nature08712
19. DeCordova S, Shastri A, Tsolaki AG, Yasmin H, Klein L, Singh SV,et al. Molecular heterogeneity and immunosuppressive microenvironment in glioblastoma. Front Immunol 2020, 11: 1402. doi: 10.3389/fimmu.2020.01402
20. Yeo ECF, Brown MP, Gargett T, Ebert LM. The role of cytokines and chemokines in shaping the immune microenvironment of glioblastoma: implications for immunotherapy. Cells 2021, 10: 607. doi: 10.3390/cells10030607
21. Crane CA, Ahn BJ, Han SJ, Parsa AT. Soluble factors secreted by glioblastoma cell lines facilitate recruitment, survival, and expansion of regulatory T cells: implications for immunotherapy. Neuro Oncol 2012,14: 584-95. doi: 10.1093/neuonc/nos014
22. Groblewska M, Litman-Zawadzka A, Mroczko B. The role of selected chemokines and their receptors in the development of gliomas. Int J Mol Sci 2020, 21: 3704. doi: 10.3390/ijms21103704
23. Huettner C, Paulus W, Roggendorf W. Messenger RNA expression of the immunosuppressive cytokine IL-10 in human gliomas. Am J Pathol 1995,146: 317-22
24. Perng P, Lim M. Immunosuppressive mechanisms of malignant gliomas: parallels at non-CNS sites. Front Oncol 2015, 5: 153. doi: 10.3389/fonc.2015.00153
25. Tafani M, Di Vito M, Frati A, Pellegrini L, De Santis E, Sette G,et al. Pro-inflammatory gene expression in solid glioblastoma microenvironment and in hypoxic stem cells from human glioblastoma. J Neuroinflammation 2011, 8: 32. doi: 10.1186/1742-2094-8-32
26. Urbantat RM, Vajkoczy P, Brandenburg S. Advances in chemokine signaling pathways as therapeutic targets in glioblastoma. Cancers (Basel) 2021, 13: 2983. doi: 10.3390/cancers13122983
27. Van Meir E, Sawamura Y, Diserens AC, Hamou MF, de Tribolet N. Human glioblastoma cells release interleukin 6 in vivo and in vitro. Cancer Res 1990, 50: 6683-8
28. Waters MR, Gupta AS, Mockenhaupt K, Brown LN, Biswas DD, Kordula T. RelB acts as a molecular switch driving chronic inflammation in glioblastoma multiforme. Oncogenesis 2019, 8: 37. doi: 10.1038/s41389-019-0146-y
29. Papale M, Buccarelli M, Mollinari C, Russo MA, Pallini R, Ricci-Vitiani L, et al. Hypoxia, inflammation and necrosis as determinants of glioblastoma cancer Stem cells progression. Int J Mol Sci 2020, 21: 2660. doi: 10.3390/ijms21082660
30. Wang L, Liu Z, Balivada S, Shrestha T, Bossmann S, Pyle M, et al. Interleukin-1β and transforming growth factor-β cooperate to induce neurosphere formation and increase tumorigenicity of adherent LN-229 glioma cells. Stem Cell Res Ther 2012, 3: 5. doi: 10.1186/scrt96
31. Brandes AA, Tosoni A, Spagnolli F, Frezza G, Leonardi M, Calbucci F,et al. Disease progression or pseudoprogression after concomitant radiochemotherapy treatment: pitfalls in neurooncology. Neuro Oncol. 2008, 10: 361-7. doi: 10.1215/15228517-2008-008
32. DeAngelis LM, Delattre JY, Posner JB. Radiation-induced dementia in patients cured of brain metastases. Neurology 1989, 39: 789-96. doi: 10.1212/wnl.39.6.789
33. Sheline GE, Wara WM, Smith V. Therapeutic irradiation and brain injury. Int J Radiat Oncol Biol Phys 1980, 6: 1215-28. doi: 10.1016/0360-3016(80)90175-3
34. Bolcaen J, Descamps B, Deblaere K, Boterberg T, De Vos Pharm F, Kalala JP, et al. (18)F-fluoromethylcholine (FCho), (18)F-fluoroethyltyrosine (FET), and (18)F-fluorodeoxyglucose (FDG) for the discrimination between high-grade glioma and radiation necrosis in rats: a PET study. Nucl Med Biol 2015, 42: 38-45. doi: 10.1016/j.nucmedbio.2014.07.006
35. Verhoeven J, Baguet T, Piron S, Pauwelyn G, Bouckaert C, Descamps B, Raedt R, Vanhove C, et al. 2-[18F]FELP, a novel LAT1-specific PET tracer, for the discrimination between glioblastoma, radiation necrosis and inflammation. Nucl Med Biol 2020, 82-83: 9-16. doi: 10.1016/j.nucmedbio.2019.12.002
36. Sonar SA, Lal G. Blood-brain barrier and its function during inflammation and autoimmunity. J Leukoc Biol 2018, 103: 839-53. doi: 10.1002/JLB.1RU1117-428R
37. Burger PC, Dubois PJ, Schold SC Jr, Smith KR Jr, Odom GL, Crafts DC,et al. Computerized tomographic and pathologic studies of the untreated, quiescent, and recurrent glioblastoma multiforme. J Neurosurg 1983, 58: 159–69. doi: 10.3171/jns.1983.58.2.0159
38. Dooms GC, Hecht S, Brant-Zawadzki M, Berthiaume Y, Norman D, Newton TH. Brain radiation lesions: MR imaging. Radiology 1986, 158:149–55.
39. Jain R, Narang J, Sundgren PM, Hearshen D, Saksena S, Rock JP,et al. Treatment induced necrosis versus recurrent/progressing brain tumor: going beyond the boundaries of conventional morphologic imaging. J Neurooncol 2010, 100: 17-29. doi: 10.1007/s11060-010-0139-3
40. Tihan T, Barletta J, Parney I, Lamborn K, Sneed PK, Chang S. Prognostic value of detecting recurrent glioblastoma multiforme in surgical specimens from patients after radiotherapy: should pathology evaluation alter treatment decisions? Hum Pathol 2006, 37:272–82. doi: 10.1016/j.humpath.2005.11.010
41. Martínez-Bisbal MC, Celda B. Proton magnetic resonance spectroscopy imaging in the study of human brain cancer. Q J Nucl Med Mol Imaging. 2009, 53: 618-30
42. Weybright P, Sundgren PC, Maly P, Hassan DG, Nan B, Rohrer S,et al. Differentiation between brain tumor recurrence and radiation injury using MR spectroscopy. AJR Am J Roentgenol 2005,185: 1471-6. doi: 10.2214/AJR.04.0933
43. Chen W. Clinical applications of PET in brain tumors. J Nucl Med 2007, 48: 1468-81. doi: 10.2967/jnumed.106.037689
44. Ricci PE, Karis JP, Heiserman JE, Fram EK, Bice AN, Drayer BP. Differentiating recurrent tumor from radiation necrosis: time for re-evaluation of positron emission tomography? AJNR Am J Neuroradiol 1998, 19: 407-13
45. Menoux I, Noël G, Namer I, Antoni D. [PET scan and NMR spectroscopy for the differential diagnosis between brain radiation necrosis and tumour recurrence after stereotactic irradiation of brain metastases: Place in the decision tree]. Cancer Radiother 2017,21: 389-97. doi: 10.1016/j.canrad.2017.03.003
46. Barajas RF Jr, Chang JS, Segal MR, Parsa AT, McDermott MW, Berger MS, 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 2009, 253: 486-96. doi: 10.1148/radiol.2532090007
47. Nakajima T, Kumabe T, Kanamori M, Saito R, Tashiro M, Watanabe M,et al. Differential diagnosis between radiation necrosis and glioma progression using sequential proton magnetic resonance spectroscopy and methionine positron emission tomography. Neurol Med Chir (Tokyo) 2009, 49: 394-401. doi: 10.2176/nmc.49.394
48. Kamada K, Houkin K, Abe H, Sawamura Y, Kashiwaba T. Differentiation of cerebral radiation necrosis from tumor recurrence by proton magnetic resonance spectroscopy. Neurol Med Chir (Tokyo) 1997, 37:250-6. doi: 10.2176/nmc.37.250
49. Chuang MT, Liu YS, Tsai YS, Chen YC, Wang CK. Differentiating radiation-induced necrosis from recurrent brain tumor using MR perfusion and spectroscopy: a meta-analysis. PLoS One 2016, 11: e0141438. doi: 10.1371/journal.pone.0141438
50. Gao L, Xu W, Li T, Zheng J, Chen G. Accuracy of 11C-choline positron emission tomography in differentiating glioma recurrence from radiation necrosis: A systematic review and meta-analysis. Medicine (Baltimore) 2018, 97: e11556. doi: 10.1097/MD.0000000000011556
51. Tan H, Chen L, Guan Y, Lin X. Comparison of MRI, F-18 FDG, and 11C-choline PET/CT for their potentials in differentiating brain tumor recurrence from brain tumor necrosis following radiotherapy. Clin Nucl Med 2011, 36: 978-81. doi: 10.1097/RLU.0b013e31822f68a6
52. Bolcaen J, Descamps B, Deblaere K, Boterberg T, De Vos Pharm F, Kalala JP, et al. (18)F-fluoromethylcholine (FCho), (18)F-fluoroethyltyrosine (FET), and (18)F-fluorodeoxyglucose (FDG) for the discrimination between high-grade glioma and radiation necrosis in rats: a PET study. Nucl Med Biol 2015, 42: 38-45. doi: 10.1016/j.nucmedbio.2014.07.006
53. Takenaka S, Asano Y, Shinoda J, Nomura Y, Yonezawa S, Miwa K, Yano H, et al. Comparison of (11)C-methionine, (11)C-choline, and (18)F-fluorodeoxyglucose-PET for distinguishing glioma recurrence from radiation necrosis. Neurol Med Chir (Tokyo) 2014, 54: 280-9. doi: 10.2176/nmc.oa2013-0117
54. Lai PH, Weng HH, Chen CY, Hsu SS, Ding S, Ko CW, et al. In vivo differentiation of aerobic brain abscesses and necrotic glioblastomas multiforme using proton MR spectroscopic imaging. AJNR Am J Neuroradiol 2008, 29: 1511-8. doi: 10.3174/ajnr.A1130
55. Aziz K, Nawaz, Atif. 1411. Differentiation of fungal abscess of brain from brain glioblastoma by MRI scan ADC value. Open Forum Infect Dis 2019, 6(Suppl 2): S514. 2019 Oct 23. doi: 10.1093/ofid/ofz360.1275
56. Bink A, Gaa J, Franz K, Weidauer S, Yan B, Lanfermann H, et al. Importance of diffusion-weighted imaging in the diagnosis of cystic brain tumors and intracerebral abscesses. Zentralbl Neurochir 2005,66: 119-25. doi: 10.1055/s-2005-836478
57. Nandy SB, Lakshmanaswamy R. Cancer stem cells and metastasis. Prog Mol Biol Transl Sci 2017, 151: 137-76. doi: 10.1016/bs.pmbts.2017.07.007
58. Doron H, Pukrop T, Erez N. A Blazing landscape: neuroinflammation shapes brain metastasis. Cancer Res 2019, 79: 423-36. doi: 10.1158/0008-5472.CAN-18-1805
59. Gyoneva S, Ransohoff RM. Inflammatory reaction after traumatic brain injury: therapeutic potential of targeting cell-cell communication by chemokines. Trends Pharmacol Sci 2015, 36: 471-80. doi: 10.1016/j.tips.2015.04.003
60. O’Callaghan JP, Sriram K, Miller DB. Defining ”neuroinflammation”. Ann N Y Acad Sci 2008, 1139: 318-30. doi: 10.1196/annals.1432.032
61. Qian, B. Z. & Pollard, J. W. Macrophage diversity enhances tumor progression and metastasis. Cell 141: 39–51, doi: 10.1016/j.cell.2010.03.014
62. Sikpa D, Whittingstall L, Fouquet JP, Radulska A, Tremblay L, Lebel R, et al. Cerebrovascular inflammation promotes the formation of brain metastases. Int J Cancer 2020, 147: 244-55. doi: 10.1002/ijc.32902
63. Burda JE, Sofroniew MV. Reactive gliosis and the multicellular response to CNS damage and disease. Neuron 2014, 81: 229-48. doi: 10.1016/j.neuron.2013.12.034
64. Doron H, Amer M, Ershaid N, Blazquez R, Shani O, Lahav TG, et al. Inflammatory activation of astrocytes facilitates melanoma brain tropism via the CXCL10-CXCR3 signaling axis. Cell Rep 2019, 28:1785-98.e6. doi: 10.1016/j.celrep.2019.07.033
65. Klein A, Schwartz H, Sagi-Assif O, Meshel T, Izraely S, Ben Menachem S, et al. Astrocytes facilitate melanoma brain metastasis via secretion of IL-23. J Pathol 2015, 236: 116-27. doi: 10.1002/path.4509
66. Seike T, Fujita K, Yamakawa Y, Kido MA, Takiguchi S, Teramoto N, et al. Interaction between lung cancer cells and astrocytes via specific inflammatory cytokines in the microenvironment of brain metastasis. Clin Exp Metastasis 2011, 28: 13-25. doi: 10.1007/s10585-010-9354-8
67. Xing F, Kobayashi A, Okuda H, Watabe M, Pai SK, Pandey PR, et al. Reactive astrocytes promote the metastatic growth of breast cancer stem-like cells by activating Notch signalling in brain. EMBO Mol Med 2013, 5: 384-96. doi: 10.1002/emmm.201201623
68. Fordham AJ, Hacherl CC, Patel N, Jones K, Myers B, Abraham M,et al. Differentiating glioblastomas from solitary brain metastases: an update on the current literature of advanced imaging modalities. Cancers (Basel) 2021, 13: 2960. doi: 10.3390/cancers13122960
69. Ishimaru H, Morikawa M, Iwanaga S, Kaminogo M, Ochi M, Hayashi K. Differentiation between high-grade glioma and metastatic brain tumor using single-voxel proton MR spectroscopy. Eur Radiol 2001, 11:1784-91. doi: 10.1007/s003300000814
70. Beig Zali S, Alinezhad F, Ranjkesh M, Daghighi MH, Poureisa M. Accuracy of apparent diffusion coefficient in differentiation of glioblastoma from metastasis. Neuroradiol J 2021, 34: 205-12. doi: 10.1177/1971400920983678
71. Zhang G, Chen X, Zhang S, Ruan X, Gao C, Liu Z, et al.Discrimination between solitary brain metastasis and glioblastoma multiforme by using ADC-based texture analysis: a comparison of two different ROI placements. Acad Radiol 2019, 26: 1466-72. doi: 10.1016/j.acra.2019.01.010
72. Fordham AJ, Hacherl CC, Patel N, Jones K, Myers B, Abraham M,et al. Differentiating glioblastomas from solitary brain metastases: an update on the current literature of advanced imaging modalities. Cancers (Basel) 2021, 13: 2960. doi: 10.3390/cancers13122960
73. Skogen K, Schulz A, Helseth E, Ganeshan B, Dormagen JB, Server A. Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis. Acta Radiol 2019,60: 356-66. doi: 10.1177/0284185118780889
74. Abdel Razek AAK, Talaat M, El-Serougy L, Abdelsalam M, Gaballa G. Differentiating glioblastomas from solitary brain metastases using arterial spin labeling perfusion- and diffusion tensor imaging-derived metrics. World Neurosurg 2019, 127, e593-e598. doi: 10.1016/j.wneu.2019.03.213
75. Ortiz-Ramón R, Ruiz-España S, Mollá-Olmos E, Moratal D. Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach. Phys Med 2020, 76:44-54. doi: 10.1016/j.ejmp.2020.06.016
76. Petrujkić K, Milošević N, Rajković N, Stanisavljević D, Gavrilović S, Dželebdžić D, et al. Computational quantitative MR image features - a potential useful tool in differentiating glioblastoma from solitary brain metastasis. Eur J Radiol 2019, 119: 108634. doi: 10.1016/j.ejrad.2019.08.003
77. de Causans A, Carré A, Roux A, Tauziède-Espariat A, Ammari S, Dezamis E, et al. Development of a machine learning classifier based on radiomic features extracted from post-contrast 3D T1-weighted MR images to distinguish glioblastoma from solitary brain metastasis. Front Oncol 2021, 11: 638262. doi: 10.3389/fonc.2021.638262
78. Shin I, Kim H, Ahn SS, Sohn B, Bae S, Park JE, et al.Development and validation of a deep learning-based model to distinguish glioblastoma from solitary brain metastasis using conventional MR images. AJNR Am J Neuroradiol 2021, 42: 838-44. doi: 10.3174/ajnr.A7003
79. Swinburne NC, Schefflein J, Sakai Y, Oermann EK, Titano JJ, Chen I,et al. Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging. Ann Transl Med 2019, 7:232. doi: 10.21037/atm.2018.08.05
80. Tateishi M, Nakaura T, Kitajima M, Uetani H, Nakagawa M, Inoue T, et al. An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases. J Neurol Sci 2020, 410:116514. doi: 10.1016/j.jns.2019.116514
81. Ziebart A, Stadniczuk D, Roos V, Ratliff M, von Deimling A, Hänggi D, et al. Deep neural network for differentiation of brain tumor tissue displayed by confocal laser endomicroscopy. Front Oncol 2021,11: 668273. doi: 10.3389/fonc.2021.668273
82. She D, Xing Z, Cao D. Differentiation of glioblastoma and solitary brain metastasis by gradient of relative cerebral blood volume in the peritumoral brain zone derived from dynamic susceptibility contrast perfusion magnetic resonance imaging. J Comput Assist Tomogr 2019,43: 13-7. doi: 10.1097/RCT.0000000000000771
83. Doishita S, Sakamoto S, Yoneda T, Uda T, Tsukamoto T, Yamada E, et al. Differentiation of brain metastases and gliomas based on color map of phase difference enhanced imaging. Front Neurol 2018, 9:788. doi: 10.3389/fneur.2018.00788
84. Tran TT, Gallezot JD, Jilaveanu LB, Zito C, Turcu G, Lim K, et al. [11C]methionine and [11C]PBR28 as PET imaging tracers to differentiate metastatic tumor recurrence or radiation necrosis. Mol Imaging 2020, 19: 1536012120968669. doi: 10.1177/1536012120968669
85. Asao C, Korogi Y, Kitajima M, Hirai T, Baba Y, Makino K, et al. Diffusion-weighted imaging of radiation-induced brain injury for differentiation from tumor recurrence. AJNR Am J Neuroradiol 2005,26: 1455-60
86. Hein PA, Eskey CJ, Dunn JF, Hug EB. Diffusion-weighted imaging in the follow-up of treated high-grade gliomas: tumor recurrence versus radiation injury. AJNR Am J Neuroradiol 2004, 25: 201-9
87. Prager AJ, Martinez N, Beal K, Omuro A, Zhang Z, Young RJ. Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. AJNR Am J Neuroradiol 2015, 36: 877-85. doi: 10.3174/ajnr.A4218
88. Sugahara T, Korogi Y, Tomiguchi S, Shigematsu Y, Ikushima I, Kira T,et al. Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from nonneoplastic contrast-enhancing tissue. AJNR Am J Neuroradiol 2000, 21: 901-9.
89. Xu JL, Li YL, Lian JM, Dou SW, Yan FS, Wu H, et al.Distinction between postoperative recurrent glioma and radiation injury using MR diffusion tensor imaging. Neuroradiology 2010, 52:1193-9. doi: 10.1007/s00234-010-0731-4
90. Young RJ, Gupta A, Shah AD, Graber JJ, Chan TA, Zhang Z, et al. MRI perfusion in determining pseudoprogression in patients with glioblastoma. Clin Imaging 2013, 37: 41-9. doi: 10.1016/j.clinimag.2012.02.016
91. Yun TJ, Park CK, Kim TM, Lee SH, Kim JH, Sohn CH, et al. Glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy: differentiation of true progression from pseudoprogression with quantitative dynamic contrast-enhanced MR imaging. Radiology 2015, 274: 830-40. doi: 10.1148/radiol.14132632
92. Wesseling P, Ruiter DJ, Burger PC. Angiogenesis in brain tumors; pathobiological and clinical aspects. J Neurooncol 1997, 32: 253-65. doi: 10.1023/a:1005746320099
93. Nael K, Bauer AH, Hormigo A, Lemole M, Germano IM, Puig J, et al. Multiparametric MRI for differentiation of radiation necrosis from recurrent tumor in patients with treated glioblastoma. AJR Am J Roentgenol 2018, 210: 18-23. doi: 10.2214/AJR.17.18003
94. Soni N, Ora M, Mohindra N, Menda Y, Bathla G. Diagnostic performance of PET and perfusion-weighted imaging in differentiating tumor recurrence or progression from radiation necrosis in posttreatment gliomas: a review of literature. AJNR Am J Neuroradiol 2020, 41: 1550-7. doi: 10.3174/ajnr.A6685