Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Emerging Techniques in Brain Tumor Imaging: What Radiologists Need to Know

  • Kim, Minjae (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Kim, Ho Sung (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center)
  • Received : 2016.02.29
  • Accepted : 2016.05.03
  • Published : 2016.09.01
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Abstract

Among the currently available brain tumor imaging, advanced MR imaging techniques, such as diffusion-weighted MR imaging and perfusion MR imaging, have been used for solving diagnostic challenges associated with conventional imaging and for monitoring the brain tumor treatment response. Further development of advanced MR imaging techniques and postprocessing methods may contribute to predicting the treatment response to a specific therapeutic regimen, particularly using multi-modality and multiparametric imaging. Over the next few years, new imaging techniques, such as amide proton transfer imaging, will be studied regarding their potential use in quantitative brain tumor imaging. In this review, the pathophysiologic considerations and clinical validations of these promising techniques are discussed in the context of brain tumor characterization and treatment response.

Keywords

Acknowledgement

Grant : 차세대 종양 영상 바이오마커를 이용한 인공지능 진단시스템의 개발

Supported by : 울산대학교

References

  1. Jahng GH, Li KL, Ostergaard L, Calamante F. Perfusion magnetic resonance imaging: a comprehensive update on principles and techniques. Korean J Radiol 2014;15:554-577 https://doi.org/10.3348/kjr.2014.15.5.554
  2. Park SH, Han PK, Choi SH. Physiological and functional magnetic resonance imaging using balanced steady-state free precession. Korean J Radiol 2015;16:550-559 https://doi.org/10.3348/kjr.2015.16.3.550
  3. Iima M, Le Bihan D. Clinical intravoxel incoherent motion and diffusion MR imaging: past, present, and future. Radiology 2016;278:13-32 https://doi.org/10.1148/radiol.2015150244
  4. Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of nongaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 2005;53:1432-1440 https://doi.org/10.1002/mrm.20508
  5. Hu LS, Eschbacher JM, Dueck AC, Heiserman JE, Liu S, Karis JP, et al. Correlations between perfusion MR imaging cerebral blood volume, microvessel quantification, and clinical outcome using stereotactic analysis in recurrent high-grade glioma. AJNR Am J Neuroradiol 2012;33:69-76 https://doi.org/10.3174/ajnr.A2743
  6. Gilad AA, Israely T, Dafni H, Meir G, Cohen B, Neeman M. Functional and molecular mapping of uncoupling between vascular permeability and loss of vascular maturation in ovarian carcinoma xenografts: the role of stroma cells in tumor angiogenesis. Int J Cancer 2005;117:202-211 https://doi.org/10.1002/ijc.21179
  7. Ellingson BM, Malkin MG, Rand SD, Connelly JM, Quinsey C, LaViolette PS, et al. Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity. J Magn Reson Imaging 2010;31:538-548 https://doi.org/10.1002/jmri.22068
  8. Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T, et al. Usefulness of diffusion-weighted MRI with echoplanar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 1999;9:53-60 https://doi.org/10.1002/(SICI)1522-2586(199901)9:1<53::AID-JMRI7>3.0.CO;2-2
  9. Filli L, Wurnig M, Nanz D, Luechinger R, Kenkel D, Boss A. Whole-body diffusion kurtosis imaging: initial experience on non-Gaussian diffusion in various organs. Invest Radiol 2014;49:773-778 https://doi.org/10.1097/RLI.0000000000000082
  10. LaViolette PS, Mickevicius NJ, Cochran EJ, Rand SD, Connelly J, Bovi JA, et al. Precise ex vivo histological validation of heightened cellularity and diffusion-restricted necrosis in regions of dark apparent diffusion coefficient in 7 cases of high-grade glioma. Neuro Oncol 2014;16:1599-1606 https://doi.org/10.1093/neuonc/nou142
  11. Choi H, Paeng JC, Cheon GJ, Park CK, Choi SH, Min HS, et al. Correlation of 11C-methionine PET and diffusion-weighted MRI: is there a complementary diagnostic role for gliomas? Nucl Med Commun 2014;35:720-726 https://doi.org/10.1097/MNM.0000000000000121
  12. Rose S, Fay M, Thomas P, Bourgeat P, Dowson N, Salvado O, et al. Correlation of MRI-derived apparent diffusion coefficients in newly diagnosed gliomas with [18F]-fluoro-L-dopa PET: what are we really measuring with minimum ADC? AJNR Am J Neuroradiol 2013;34:758-764 https://doi.org/10.3174/ajnr.A3315
  13. Hayashida Y, Hirai T, Morishita S, Kitajima M, Murakami R, Korogi Y, et al. Diffusion-weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. AJNR Am J Neuroradiol 2006;27:1419-1425
  14. Shim WH, Kim HS, Choi CG, Kim SJ. Comparison of apparent diffusion coefficient and intravoxel incoherent motion for differentiating among glioblastoma, metastasis, and lymphoma focusing on diffusion-related parameter. PLoS One 2015;10:e0134761 https://doi.org/10.1371/journal.pone.0134761
  15. Federau C, Maeder P, O'Brien K, Browaeys P, Meuli R, Hagmann P. Quantitative measurement of brain perfusion with intravoxel incoherent motion MR imaging. Radiology 2012;265:874-881 https://doi.org/10.1148/radiol.12120584
  16. Notohamiprodjo M, Chandarana H, Mikheev A, Rusinek H, Grinstead J, Feiweier T, et al. Combined intravoxel incoherent motion and diffusion tensor imaging of renal diffusion and flow anisotropy. Magn Reson Med 2015;73:1526-1532 https://doi.org/10.1002/mrm.25245
  17. Raab P, Hattingen E, Franz K, Zanella FE, Lanfermann H. Cerebral gliomas: diffusional kurtosis imaging analysis of microstructural differences. Radiology 2010;254:876-881 https://doi.org/10.1148/radiol.09090819
  18. Dean BL, Drayer BP, Bird CR, Flom RA, Hodak JA, Coons SW, et al. Gliomas: classification with MR imaging. Radiology 1990;174:411-415 https://doi.org/10.1148/radiology.174.2.2153310
  19. Falangola MF, Jensen JH, Babb JS, Hu C, Castellanos FX, Di Martino A, et al. Age-related non-Gaussian diffusion patterns in the prefrontal brain. J Magn Reson Imaging 2008;28:1345-1350 https://doi.org/10.1002/jmri.21604
  20. Kim SJ, Choi CG, Kim JK, Yun SC, Jahng GH, Jeong HK, et al. Effects of MR parameter changes on the quantification of diffusion anisotropy and apparent diffusion coefficient in diffusion tensor imaging: evaluation using a diffusional anisotropic phantom. Korean J Radiol 2015;16:297-303 https://doi.org/10.3348/kjr.2015.16.2.297
  21. Daumas-Duport C, Scheithauer B, O'Fallon J, Kelly P. Grading of astrocytomas. A simple and reproducible method. Cancer 1988;62:2152-2165 https://doi.org/10.1002/1097-0142(19881115)62:10<2152::AID-CNCR2820621015>3.0.CO;2-T
  22. Maier SE, Sun Y, Mulkern RV. Diffusion imaging of brain tumors. NMR Biomed 2010;23:849-864 https://doi.org/10.1002/nbm.1544
  23. Iima M, Yano K, Kataoka M, Umehana M, Murata K, Kanao S, et al. Quantitative non-Gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions. Invest Radiol 2015;50:205-211 https://doi.org/10.1097/RLI.0000000000000094
  24. Birner P, Piribauer M, Fischer I, Gatterbauer B, Marosi C, Ambros PF, et al. Vascular patterns in glioblastoma influence clinical outcome and associate with variable expression of angiogenic proteins: evidence for distinct angiogenic subtypes. Brain Pathol 2003;13:133-143
  25. Korkolopoulou P, Patsouris E, Kavantzas N, Konstantinidou AE, Christodoulou P, Thomas-Tsagli E, et al. Prognostic implications of microvessel morphometry in diffuse astrocytic neoplasms. Neuropathol Appl Neurobiol 2002;28:57-66 https://doi.org/10.1046/j.1365-2990.2002.00367.x
  26. Wesseling P, van der Laak JA, Link M, Teepen HL, Ruiter DJ. Quantitative analysis of microvascular changes in diffuse astrocytic neoplasms with increasing grade of malignancy. Hum Pathol 1998;29:352-358 https://doi.org/10.1016/S0046-8177(98)90115-0
  27. Wesseling P, van der Laak JA, de Leeuw H, Ruiter DJ, Burger PC. Quantitative immunohistological analysis of the microvasculature in untreated human glioblastoma multiforme. Computer-assisted image analysis of whole-tumor sections. J Neurosurg 1994;81:902-909 https://doi.org/10.3171/jns.1994.81.6.0902
  28. Barajas RF Jr, Phillips JJ, Parvataneni R, Molinaro A, Essock-Burns E, Bourne G, et al. Regional variation in histopathologic features of tumor specimens from treatmentnaive glioblastoma correlates with anatomic and physiologic MR Imaging. Neuro Oncol 2012;14:942-954 https://doi.org/10.1093/neuonc/nos128
  29. Hyodo F, Chandramouli GV, Matsumoto S, Matsumoto K, Mitchell JB, Krishna MC, et al. Estimation of tumor microvessel density by MRI using a blood pool contrast agent. Int J Oncol 2009;35:797-804
  30. Wilmes LJ, Pallavicini MG, Fleming LM, Gibbs J, Wang D, Li KL, et al. AG-013736, a novel inhibitor of VEGF receptor tyrosine kinases, inhibits breast cancer growth and decreases vascular permeability as detected by dynamic contrastenhanced magnetic resonance imaging. Magn Reson Imaging 2007;25:319-327 https://doi.org/10.1016/j.mri.2006.09.041
  31. Boxerman JL, Schmainda KM, Weisskoff RM. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 2006;27:859-867
  32. Donahue KM, Krouwer HG, Rand SD, Pathak AP, Marszalkowski CS, Censky SC, et al. Utility of simultaneously acquired gradient-echo and spin-echo cerebral blood volume and morphology maps in brain tumor patients. Magn Reson Med 2000;43:845-853 https://doi.org/10.1002/1522-2594(200006)43:6<845::AID-MRM10>3.0.CO;2-J
  33. Choi HS, Ahn SS, Shin NY, Kim J, Kim JH, Lee JE, et al. Permeability parameters measured with dynamic contrastenhanced MRI: correlation with the extravasation of evans blue in a rat model of transient cerebral ischemia. Korean J Radiol 2015;16:791-797 https://doi.org/10.3348/kjr.2015.16.4.791
  34. Tofts PS. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J Magn Reson Imaging 1997;7:91-101 https://doi.org/10.1002/jmri.1880070113
  35. Brix G, Griebel J, Kiessling F, Wenz F. Tracer kinetic modelling of tumour angiogenesis based on dynamic contrast-enhanced CT and MRI measurements. Eur J Nucl Med Mol Imaging 2010;37 Suppl 1:S30-S51 https://doi.org/10.1007/s00259-010-1448-7
  36. Patankar TF, Haroon HA, Mills SJ, Baleriaux D, Buckley DL, Parker GJ, et al. Is volume transfer coefficient (K(trans)) related to histologic grade in human gliomas? AJNR Am J Neuroradiol 2005;26:2455-2465
  37. Marzola P, Degrassi A, Calderan L, Farace P, Crescimanno C, Nicolato E, et al. In vivo assessment of antiangiogenic activity of SU6668 in an experimental colon carcinoma model. Clin Cancer Res 2004;10:739-750 https://doi.org/10.1158/1078-0432.CCR-0828-03
  38. Klemm F, Joyce JA. Microenvironmental regulation of therapeutic response in cancer. Trends Cell Biol 2015;25:198-213 https://doi.org/10.1016/j.tcb.2014.11.006
  39. Sorensen AG, Batchelor TT, Zhang WT, Chen PJ, Yeo P, Wang M, et al. A "vascular normalization index" as potential mechanistic biomarker to predict survival after a single dose of cediranib in recurrent glioblastoma patients. Cancer Res 2009;69:5296-5300 https://doi.org/10.1158/0008-5472.CAN-09-0814
  40. Jain RK. Normalizing tumor microenvironment to treat cancer: bench to bedside to biomarkers. J Clin Oncol 2013;31:2205-2218 https://doi.org/10.1200/JCO.2012.46.3653
  41. Sorensen AG, Emblem KE, Polaskova P, Jennings D, Kim H, Ancukiewicz M, et al. Increased survival of glioblastoma patients who respond to antiangiogenic therapy with elevated blood perfusion. Cancer Res 2012;72:402-407 https://doi.org/10.1158/0008-5472.CAN-11-2464
  42. Detre JA, Zhang W, Roberts DA, Silva AC, Williams DS, Grandis DJ, et al. Tissue specific perfusion imaging using arterial spin labeling. NMR Biomed 1994;7:75-82 https://doi.org/10.1002/nbm.1940070112
  43. Noguchi T, Yoshiura T, Hiwatashi A, Togao O, Yamashita K, Nagao E, et al. Perfusion imaging of brain tumors using arterial spin-labeling: correlation with histopathologic vascular density. AJNR Am J Neuroradiol 2008;29:688-693 https://doi.org/10.3174/ajnr.A0903
  44. Warmuth C, Gunther M, Zimmer C. Quantification of blood flow in brain tumors: comparison of arterial spin labeling and dynamic susceptibility-weighted contrast-enhanced MR imaging. Radiology 2003;228:523-532 https://doi.org/10.1148/radiol.2282020409
  45. Haacke EM, Xu Y, Cheng YC, Reichenbach JR. Susceptibility weighted imaging (SWI). Magn Reson Med 2004;52:612-618 https://doi.org/10.1002/mrm.20198
  46. Mittal S, Wu Z, Neelavalli J, Haacke EM. Susceptibilityweighted imaging: technical aspects and clinical applications, part 2. AJNR Am J Neuroradiol 2009;30:232-252 https://doi.org/10.3174/ajnr.A1461
  47. Sehgal V, Delproposto Z, Haacke EM, Tong KA, Wycliffe N, Kido DK, et al. Clinical applications of neuroimaging with susceptibility-weighted imaging. J Magn Reson Imaging 2005;22:439-450 https://doi.org/10.1002/jmri.20404
  48. Barth M, Nobauer-Huhmann IM, Reichenbach JR, Mlynarik V, Schoggl A, Matula C, et al. High-resolution three-dimensional contrast-enhanced blood oxygenation level-dependent magnetic resonance venography of brain tumors at 3 Tesla: first clinical experience and comparison with 1.5 Tesla. Invest Radiol 2003;38:409-414
  49. Fahrendorf D, Schwindt W, Wolfer J, Jeibmann A, Kooijman H, Kugel H, et al. Benefits of contrast-enhanced SWI in patients with glioblastoma multiforme. Eur Radiol 2013;23:2868-2879 https://doi.org/10.1007/s00330-013-2895-x
  50. Sampetrean O, Saga I, Nakanishi M, Sugihara E, Fukaya R, Onishi N, et al. Invasion precedes tumor mass formation in a malignant brain tumor model of genetically modified neural stem cells. Neoplasia 2011;13:784-791 https://doi.org/10.1593/neo.11624
  51. Jones CK, Schlosser MJ, van Zijl PC, Pomper MG, Golay X, Zhou J. Amide proton transfer imaging of human brain tumors at 3T. Magn Reson Med 2006;56:585-592 https://doi.org/10.1002/mrm.20989
  52. Henkelman RM, Stanisz GJ, Graham SJ. Magnetization transfer in MRI: a review. NMR Biomed 2001;14:57-64 https://doi.org/10.1002/nbm.683
  53. van Zijl PC, Yadav NN. Chemical exchange saturation transfer (CEST): what is in a name and what isn't? Magn Reson Med 2011;65:927-948 https://doi.org/10.1002/mrm.22761
  54. Zhou J, Zhu H, Lim M, Blair L, Quinones-Hinojosa A, Messina SA, et al. Three-dimensional amide proton transfer MR imaging of gliomas: initial experience and comparison with gadolinium enhancement. J Magn Reson Imaging 2013;38:1119-1128 https://doi.org/10.1002/jmri.24067
  55. Togao O, Yoshiura T, Keupp J, Hiwatashi A, Yamashita K, Kikuchi K, et al. Amide proton transfer imaging of adult diffuse gliomas: correlation with histopathological grades. Neuro Oncol 2014;16:441-448 https://doi.org/10.1093/neuonc/not158
  56. Sagiyama K, Mashimo T, Togao O, Vemireddy V, Hatanpaa KJ, Maher EA, et al. In vivo chemical exchange saturation transfer imaging allows early detection of a therapeutic response in glioblastoma. Proc Natl Acad Sci U S A 2014;111:4542-4547 https://doi.org/10.1073/pnas.1323855111
  57. Park JE, Kim HS, Park KJ, Kim SJ, Kim JH, Smith SA. Pre- and posttreatment glioma: comparison of amide proton transfer imaging with MR spectroscopy for biomarkers of tumor proliferation. Radiology 2016;278:514-523 https://doi.org/10.1148/radiol.2015142979
  58. Chenevert TL, McKeever PE, Ross BD. Monitoring early response of experimental brain tumors to therapy using diffusion magnetic resonance imaging. Clin Cancer Res 1997;3:1457-1466
  59. Lee EK, Choi SH, Yun TJ, Kang KM, Kim TM, Lee SH, et al. Prediction of response to concurrent chemoradiotherapy with temozolomide in glioblastoma: application ofimmediate postoperative dynamic susceptibility contrast and diffusion-weighted MR imaging. Korean J Radiol 2015;16:1341-1348 https://doi.org/10.3348/kjr.2015.16.6.1341
  60. Ellingson BM, Sahebjam S, Kim HJ, Pope WB, Harris RJ, Woodworth DC, et al. Pretreatment ADC histogram analysis is a predictive imaging biomarker for bevacizumab treatment but not chemotherapy in recurrent glioblastoma. AJNR Am J Neuroradiol 2014;35:673-679 https://doi.org/10.3174/ajnr.A3748
  61. High WA, Ayers RA, Cowper SE. Gadolinium is quantifiable within the tissue of patients with nephrogenic systemic fibrosis. J Am Acad Dermatol 2007;56:710-712 https://doi.org/10.1016/j.jaad.2007.01.022
  62. Kanda T, Ishii K, Kawaguchi H, Kitajima K, Takenaka D. High signal intensity in the dentate nucleus and globus pallidus on unenhanced T1-weighted MR images: relationship with increasing cumulative dose of a gadolinium-based contrast material. Radiology 2014;270:834-841 https://doi.org/10.1148/radiol.13131669
  63. Kim DY, Kim HS, Goh MJ, Choi CG, Kim SJ. Utility of intravoxel incoherent motion MR imaging for distinguishing recurrent metastatic tumor from treatment effect following gamma knife radiosurgery: initial experience. AJNR Am J Neuroradiol 2014;35:2082-2090 https://doi.org/10.3174/ajnr.A3995
  64. Kim HS, Suh CH, Kim N, Choi CG, Kim SJ. Histogram analysis of intravoxel incoherent motion for differentiating recurrent tumor from treatment effect in patients with glioblastoma: initial clinical experience. AJNR Am J Neuroradiol 2014;35:490-497 https://doi.org/10.3174/ajnr.A3719
  65. Bai Y, Lin Y, Tian J, Shi D, Cheng J, Haacke EM, et al. Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology 2016;278:496-504 https://doi.org/10.1148/radiol.2015142173
  66. Van Cauter S, Veraart J, Sijbers J, Peeters RR, Himmelreich U, De Keyzer F, et al. Gliomas: diffusion kurtosis MR imaging in grading. Radiology 2012;263:492-501 https://doi.org/10.1148/radiol.12110927
  67. 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;9:453-461 https://doi.org/10.1016/S1470-2045(08)70125-6
  68. Sanghera P, Perry J, Sahgal A, Symons S, Aviv R, Morrison M, et al. Pseudoprogression following chemoradiotherapy for glioblastoma multiforme. Can J Neurol Sci 2010;37:36-42 https://doi.org/10.1017/S0317167100009628
  69. Kong DS, Kim ST, Kim EH, Lim DH, Kim WS, Suh YL, 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. AJNR Am J Neuroradiol 2011;32:382-387 https://doi.org/10.3174/ajnr.A2286
  70. Chamberlain MC. Pseudoprogression in glioblastoma. J Clin Oncol 2008;26:4359; author reply 4359-4360
  71. Baek HJ, Kim HS, Kim N, Choi YJ, Kim YJ. Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. Radiology 2012;264:834-843 https://doi.org/10.1148/radiol.12112120
  72. Wang S, Martinez-Lage M, Sakai Y, Chawla S, Kim SG, Alonso-Basanta M, et al. Differentiating tumor progression from pseudoprogression in patients with glioblastomas using diffusion tensor imaging and dynamic susceptibility contrast MRI. AJNR Am J Neuroradiol 2016;37:28-36 https://doi.org/10.3174/ajnr.A4474
  73. 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;256:575-584 https://doi.org/10.1148/radiol.10091440
  74. Radbruch A, Bendszus M, Wick W, Heiland S. Comment to: parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma: pitfalls in perfusion MRI in brain tumors: Tsien C, Galban CJ, Chenevert TL, Johnson TD, Hamstra DA, Sundgren PC, Junck L, Meyer CR, Rehemtulla A, Lawrence T, Ross BD. J Clin Oncol. 2010;28:2293-9. Clin Neuroradiol 2010;20:183-184 https://doi.org/10.1007/s00062-010-0024-7
  75. Cheng HL, Wallis C, Shou Z, Farhat WA. Quantifying angiogenesis in VEGF-enhanced tissue-engineered bladder constructs by dynamic contrast-enhanced MRI using contrast agents of different molecular weights. J Magn Reson Imaging 2007;25:137-145 https://doi.org/10.1002/jmri.20787
  76. Leach MO, Brindle KM, Evelhoch JL, Griffiths JR, Horsman MR, Jackson A, et al. Assessment of antiangiogenic and antivascular therapeutics using MRI: recommendations for appropriate methodology for clinical trials. Br J Radiol 2003;76 Spec No 1:S87-S91 https://doi.org/10.1259/bjr/15917261
  77. Kim HS, Goh MJ, Kim N, Choi CG, Kim SJ, Kim JH. Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility. Radiology 2014;273:831-843 https://doi.org/10.1148/radiol.14132868
  78. Narang J, Jain R, Arbab AS, Mikkelsen T, Scarpace L, Rosenblum ML, et al. Differentiating treatment-induced necrosis from recurrent/progressive brain tumor using nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion. Neuro Oncol 2011;13:1037-1046 https://doi.org/10.1093/neuonc/nor075
  79. Li C, Ai B, Li Y, Qi H, Wu L. Susceptibility-weighted imaging in grading brain astrocytomas. Eur J Radiol 2010;75:e81-e85 https://doi.org/10.1016/j.ejrad.2009.08.003
  80. Park MJ, Kim HS, Jahng GH, Ryu CW, Park SM, Kim SY. Semiquantitative assessment of intratumoral susceptibility signals using non-contrast-enhanced high-field highresolution susceptibility-weighted imaging in patients with gliomas: comparison with MR perfusion imaging. AJNR Am J Neuroradiol 2009;30:1402-1408 https://doi.org/10.3174/ajnr.A1593
  81. Pinker K, Noebauer-Huhmann IM, Stavrou I, Hoeftberger R, Szomolanyi P, Karanikas G, et al. High-resolution contrastenhanced, susceptibility-weighted MR imaging at 3T in patients with brain tumors: correlation with positronemission tomography and histopathologic findings. AJNR Am J Neuroradiol 2007;28:1280-1286 https://doi.org/10.3174/ajnr.A0540
  82. Hori M, Ishigame K, Kabasawa H, Kumagai H, Ikenaga S, Shiraga N, et al. Precontrast and postcontrast susceptibilityweighted imaging in the assessment of intracranial brain neoplasms at 1.5 T. Jpn J Radiol 2010;28:299-304 https://doi.org/10.1007/s11604-010-0427-z
  83. Park JE, Kim HS, Park KJ, Choi CG, Kim SJ. Histogram analysis of amide proton transfer imaging to identify contrastenhancing low-grade brain tumor that mimics high-grade tumor: increased accuracy of MR perfusion. Radiology 2015;277:151-161 https://doi.org/10.1148/radiol.2015142347
  84. Park KJ, Kim HS, Park JE, Shim WH, Kim SJ, Smith SA. Added value of amide proton transfer imaging to conventional and perfusion MR imaging for evaluating the treatment response of newly diagnosed glioblastoma. Eur Radiol 2016 Feb 16 [Epub]. http://dx.doi.org/10.1007/s00330-016-4261-2
  85. Jung V, Romeike BF, Henn W, Feiden W, Moringlane JR, Zang KD, et al. Evidence of focal genetic microheterogeneity in glioblastoma multiforme by area-specific CGH on microdissected tumor cells. J Neuropathol Exp Neurol 1999;58:993-999 https://doi.org/10.1097/00005072-199909000-00009
  86. Park JE, Kim HS, Goh MJ, Kim SJ, Kim JH. Pseudoprogression in patients with glioblastoma: assessment by using volume-weighted voxel-based multiparametric clustering of MR imaging data in an independent test set. Radiology 2015;275:792-802 https://doi.org/10.1148/radiol.14141414

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  13. Emerging MRI Techniques to Redefine Treatment Response in Patients With Glioblastoma vol.52, pp.4, 2016, https://doi.org/10.1002/jmri.27105
  14. Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging vol.45, pp.4, 2021, https://doi.org/10.1097/rct.0000000000001180
  15. Artificial Intelligence Algorithm-Based Analysis of Ultrasonic Imaging Features for Diagnosis of Pregnancy Complicated with Brain Tumor vol.2021, pp.None, 2016, https://doi.org/10.1155/2021/4022312