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뇌종양 영상의 현재와 미래

Current Applications and Future Perspectives of Brain Tumor Imaging

  • 박지은 (울산대학교 의과대학 서울아산병원 영상의학과) ;
  • 김호성 (울산대학교 의과대학 서울아산병원 영상의학과)
  • Ji Eun Park (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Ho Sung Kim (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center)
  • 투고 : 2020.04.14
  • 심사 : 2020.05.07
  • 발행 : 2020.05.01

초록

뇌종양의 진단 및 치료 반응 평가의 기본이 되는 영상기법은 해부학적 영상이다. 현재 임상에서 사용 가능한 영상기법들 중 확산 강조 영상 및 관류 영상이 추가적인 정보를 제공하고 있다. 최근에는 종양의 유전체 변이와 이질성 평가가 중요해지면서 라디오믹스와 딥러닝을 이용한 영상분석기법의 임상 응용이 기대되고 있다. 본 종설에서는 뇌종양 영상 임상 적용에서 여전히 중요한 해부학적 영상을 중심으로 한 자기공명영상 촬영 권고안, 최신 영상기법 중 확산 강조 영상 및 관류 영상의 기본 원리, 병태생리학적 배경 및 임상응용, 마지막으로 최근 컴퓨터 기술의 발전으로 많이 연구되고 있는 라디오믹스와 딥러닝의 뇌종양에서의 향후 활용가치에 대해 기술하고자 한다.

Anatomical imaging is the basis of the diagnosis and treatment response assessment of brain tumors. Among the existing imaging techniques currently available in clinical practice, diffusion-weighted imaging and perfusion imaging provide additional information. Recently, with the increasing importance of evaluation of the genomic variation and heterogeneity of tumors, clinical application of imaging techniques using radiomics and deep learning is expected. In this review, we will describe recommendations for magnetic resonance imaging protocols focusing on anatomical images that are still important in the clinical application of brain tumor imaging, and the basic principles of diffusion-weighted imaging and perfusion imaging among the advanced imaging techniques, as well as their pathophysiological background and clinical application. Finally, we will review the future perspectives of radiomics and deep learning applications in brain tumor imaging, which have been studied to a great extent due to the development of computer technology.

키워드

과제정보

This research was supported by a grant of the Basic Science Research Program through the National Research Foundation of Korea (2020R1A2B5B01001707).

참고문헌

  1. Upadhyay N, Waldman AD. Conventional MRI evaluation of gliomas. Br J Radiol 2011;84 Spec No 2:S107-S111  https://doi.org/10.1259/bjr/65711810
  2. Pope WB, Brandal G. Conventional and advanced magnetic resonance imaging in patients with high-grade glioma. Q J Nucl Med Mol Imaging 2018;62:239-253  https://doi.org/10.23736/S1824-4785.18.03086-8
  3. Jain R, Johnson DR, Patel SH, Castillo M, Smits M, Bent MJVD, et al. 'Real world' use of a highly reliable imaging sign: 'T2-FLAIR mismatch' for identification of IDH mutant astrocytomas. Neuro Oncol. 2020 [In press] doi: https://doi.org/10.1093/neuonc/noaa041 
  4. Broen MPG, Smits M, Wijnenga MMJ, Dubbink HJ, Anten MHME, Schijns OEMG, et al. The T2-FLAIR mismatch sign as an imaging marker for non-enhancing IDH-mutant, 1p/19q-intact lower-grade glioma: a validation study. Neuro Oncol 2018;20:1393-1399  https://doi.org/10.1093/neuonc/noy048
  5. Patel SH, Poisson LM, Brat DJ, Zhou Y, Cooper L, Snuderl M, et al. T2-FLAIR mismatch, an imaging biomarker for IDH and 1p/19q status in lower-grade gliomas: a TCGA/TCIA project. Clin Cancer Res 2017;23:6078-6085  https://doi.org/10.1158/1078-0432.CCR-17-0560
  6. 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;28:1963-1972  https://doi.org/10.1200/JCO.2009.26.3541
  7. Ellingson BM, Bendszus M, Boxerman J, Barboriak D, Erickson BJ, Smits M, et al. Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. Neuro Oncol 2015;17:1188-1198 
  8. Ostrom QT, Cioffi G, Gittleman H, Patil N, Waite K, Kruchko C, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012-2016. Neuro Oncol 2019;21:v1-v100  https://doi.org/10.1093/neuonc/noz150
  9. Kaufmann TJ, Smits M, Boxerman J, Huang R, Barboriak DP, Weller M, et al. Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases (BTIP-BM). Neuro Oncol 2020 [In press] doi: https://doi.org/10.1093/neuonc/noaa030 
  10. Davis FG, Dolecek TA, McCarthy BJ, Villano JL. Toward determining the lifetime occurrence of metastatic brain tumors estimated from 2007 United States cancer incidence data. Neuro Oncol 2012;14:1171-1177  https://doi.org/10.1093/neuonc/nos152
  11. Lin NU, Lee EQ, Aoyama H, Barani IJ, Barboriak DP, Baumert BG, et al. Response assessment criteria for brain metastases: proposal from the RANO group. Lancet Oncol 2015;16:e270-e278  https://doi.org/10.1016/S1470-2045(15)70057-4
  12. 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
  13. Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T, et al. Usefulness of diffusion-weighted MRI with echo-planar 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
  14. Chen L, Liu M, Bao J, Xia Y, Zhang J, Zhang L, et al. The correlation between apparent diffusion coefficient and tumor cellularity in patients: a meta-analysis. PLoS One 2013;8:e79008 
  15. 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
  16. 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
  17. Kim M, Kim HS. Emerging techniques in brain tumor imaging: what radiologists need to know. Korean J Radiol 2016;17:598-619  https://doi.org/10.3348/kjr.2016.17.5.598
  18. Cha S, Knopp EA, Johnson G, Wetzel SG, Litt AW, Zagzag D. Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. Radiology 2002;223:11-29  https://doi.org/10.1148/radiol.2231010594
  19. Puig J, Biarnes C, Daunis-I-Estadella P, Blasco G, Gimeno A, Essig M, et al. Macrovascular networks on contrast-enhanced magnetic resonance imaging improves survival prediction in newly diagnosed glioblastoma. Cancers (Basel) 2019;11:E84 
  20. Hakyemez B, Erdogan C, Bolca N, Yildirim N, Gokalp G, Parlak M. Evaluation of different cerebral mass lesions by perfusion-weighted MR imaging. J Magn Reson Imaging 2006;24:817-824  https://doi.org/10.1002/jmri.20707
  21. Floriano VH, Torres US, Spotti AR, Ferraz-Filho JR, Tognola WA. The role of dynamic susceptibility contrast-enhanced perfusion MR imaging in differentiating between infectious and neoplastic focal brain lesions: results from a cohort of 100 consecutive patients. PLoS One 2013;8:e81509 
  22. Blasel S, Pfeilschifter W, Jansen V, Mueller K, Zanella F, Hattingen E. Metabolism and regional cerebral blood volume in autoimmune inflammatory demyelinating lesions mimicking malignant gliomas. J Neurol 2011;258:113-122  https://doi.org/10.1007/s00415-010-5703-4
  23. Bhagavathi S, Wilson JD. Primary central nervous system lymphoma. Arch Pathol Lab Med 2008;132:1830-1834  https://doi.org/10.5858/132.11.1830
  24. Xu W, Wang Q, Shao A, Xu B, Zhang J. The performance of MR perfusion-weighted imaging for the differentiation of high-grade glioma from primary central nervous system lymphoma: a systematic review and metaanalysis. PLoS One 2017;12:e0173430 
  25. Kickingereder P, Wiestler B, Sahm F, Heiland S, Roethke M, Schlemmer HP, et al. Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging. Radiology 2014;272:843-850  https://doi.org/10.1148/radiol.14132740
  26. Kruser TJ, Mehta MP, Robins HI. Pseudoprogression after glioma therapy: a comprehensive review. Expert Rev Neurother 2013;13:389-403  https://doi.org/10.1586/ern.13.7
  27. Brandes AA, Franceschi E, Tosoni A, Blatt V, Pession A, Tallini G, et al. MGMT promoter methylation status can predict the incidence and outcome of pseudoprogression after concomitant radiochemotherapy in newly diagnosed glioblastoma patients. J Clin Oncol 2008;26:2192-2197  https://doi.org/10.1200/JCO.2007.14.8163
  28. 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
  29. Chu HH, Choi SH, Ryoo I, Kim SC, Yeom JA, Shin H, 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 2013;269:831-840  https://doi.org/10.1148/radiol.13122024
  30. 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
  31. Blasel S, Zagorcic A, Jurcoane A, Bahr O, Wagner M, Harter PN, et al. Perfusion MRI in the evaluation of suspected glioblastoma recurrence. J Neuroimaging 2016;26:116-123  https://doi.org/10.1111/jon.12247
  32. Patel P, Baradaran H, Delgado D, Askin G, Christos P, John Tsiouris A, et al. MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro Oncol 2017;19:118-127  https://doi.org/10.1093/neuonc/now148
  33. Hamstra DA, Chenevert TL, Moffat BA, Johnson TD, Meyer CR, Mukherji SK, et al. Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci U S A 2005;102:16759-16764  https://doi.org/10.1073/pnas.0508347102
  34. Tsien C, Galban CJ, Chenevert TL, Johnson TD, Hamstra DA, Sundgren PC, et al. Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. J Clin Oncol 2010;28:2293-2299  https://doi.org/10.1200/JCO.2009.25.3971
  35. Chinot OL, Wick W, Mason W, Henriksson R, Saran F, Nishikawa R, et al. Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. N Engl J Med 2014;370:709-722  https://doi.org/10.1056/NEJMoa1308345
  36. Gilbert MR, Dignam JJ, Armstrong TS, Wefel JS, Blumenthal DT, Vogelbaum MA, et al. A randomized trial of bevacizumab for newly diagnosed glioblastoma. N Engl J Med 2014;370:699-708  https://doi.org/10.1056/NEJMoa1308573
  37. Wick W, Brandes A, Gorlia T, Bendszus M, Sahm F, Taal W, et al. LB-05 phase III trial exploring the combination of bevacizumab and lomustine in patients with first recurrence of a glioblastoma: the EORTC 26101 trial. Neuro-Oncology 2015;17:v1 
  38. Pope WB, Kim HJ, Huo J, Alger J, Brown MS, Gjertson D, et al. Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 2009;252:182-189  https://doi.org/10.1148/radiol.2521081534
  39. Ellingson BM, Gerstner ER, Smits M, Huang RY, Colen R, Abrey LE, et al. Diffusion MRI phenotypes predict overall survival benefit from anti-VEGF monotherapy in recurrent glioblastoma: converging evidence from phase II trials. Clin Cancer Res 2017;23:5745-5756  https://doi.org/10.1158/1078-0432.CCR-16-2844
  40. Ellingson BM, Gerstner ER, Smits M, Huang RY, Colen R, Abrey LE, et al. Diffusion MRI phenotypes predict overall survival benefit from anti-VEGF monotherapy in recurrent glioblastoma: converging evidence from phase II trials. Clin Cancer Res 2017;23:5745-5756  https://doi.org/10.1158/1078-0432.CCR-16-2844
  41. Rahman R, Hamdan A, Zweifler R, Jiang H, Norden AD, Reardon DA, et al. Histogram analysis of apparent diffusion coefficient within enhancing and nonenhancing tumor volumes in recurrent glioblastoma patients treated with bevacizumab. J Neurooncol 2014;119:149-158  https://doi.org/10.1007/s11060-014-1464-8
  42. 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;37:2201-2208  https://doi.org/10.3174/ajnr.A4898
  43. Thomas A, Rosenblum M, Karimi S, DeAngelis LM, Omuro A, Kaley TJ. Radiographic patterns of recurrence and pathologic correlation in malignant gliomas treated with bevacizumab. CNS Oncol 2018;7:7-13  https://doi.org/10.2217/cns-2017-0025
  44. Schmainda KM, Prah M, Connelly J, Rand SD, Hoffman RG, Mueller W, et al. Dynamic-susceptibility contrast agent MRI measures of relative cerebral blood volume predict response to bevacizumab in recurrent high-grade glioma. Neuro Oncol 2014;16:880-888  https://doi.org/10.1093/neuonc/not216
  45. Kickingereder P, Wiestler B, Burth S, Wick A, Nowosielski M, Heiland S, et al. Relative cerebral blood volume is a potential predictive imaging biomarker of bevacizumab efficacy in recurrent glioblastoma. Neuro Oncol 2015;17:1139-1147  https://doi.org/10.1093/neuonc/nov028
  46. Verhoeff JJ, Lavini C, Van Linde ME, Stalpers LJ, Majoie CB, Reijneveld JC, et al. Bevacizumab and dose-intense temozolomide in recurrent high-grade glioma. Ann Oncol 2010;21:1723-1727  https://doi.org/10.1093/annonc/mdp591
  47. Schmainda KM, Zhang Z, Prah M, Snyder BS, Gilbert MR, Sorensen AG, et al. Dynamic susceptibility contrast MRI measures of relative cerebral blood volume as a prognostic marker for overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 multicenter trial. Neuro Oncol 2015;17:1148-1156  https://doi.org/10.1093/neuonc/nou364
  48. Park JE, Kim HS, Park SY, Jung SC, Kim JH, Heo HY. Identification of early response to anti-angiogenic therapy in recurrent glioblastoma: amide proton transfer-weighted and perfusion-weighted MRI compared with diffusion-weighted MRI. Radiology 2020;295:397-406  https://doi.org/10.1148/radiol.2020191376
  49. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563-577  https://doi.org/10.1148/radiol.2015151169
  50. Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 2019;20:1124-1137  https://doi.org/10.3348/kjr.2018.0070
  51. Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006 
  52. Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 2009;62:1609-1618  https://doi.org/10.1002/mrm.22147
  53. Kang D, Park JE, Kim YH, Kim JH, Oh JY, Kim J, et al. Diffusion radiomics as a diagnostic model for atypical manifestation of primary central nervous system lymphoma: development and multicenter external validation. Neuro Oncol 2018;20:1251-1261  https://doi.org/10.1093/neuonc/noy021
  54. Kniep HC, Madesta F, Schneider T, Hanning U, Schonfeld MH, Schon G, et al. Radiomics of brain MRI: utility in prediction of metastatic tumor type. Radiology 2019;290:479-487  https://doi.org/10.1148/radiol.2018180946
  55. Zhang X, Tian Q, Wang L, Liu Y, Li B, Liang Z, et al. Radiomics strategy for molecular subtype stratification of lower-grade glioma: detecting IDH and TP53 mutations based on multimodal MRI. J Magn Reson Imaging 2018;48:916-926  https://doi.org/10.1002/jmri.25960
  56. Yu J, Shi Z, Lian Y, Li Z, Liu T, Gao Y, et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol 2017;27:3509-3522  https://doi.org/10.1007/s00330-016-4653-3
  57. Kim M, Jung SY, Park JE, Jo Y, Park SY, Nam SJ, et al. Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma. Eur Radiol 2020;30:2142-2151  https://doi.org/10.1007/s00330-019-06548-3
  58. Arita H, Kinoshita M, Kawaguchi A, Takahashi M, Narita Y, Terakawa Y, et al. Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas. Sci Rep 2018;8:11773 
  59. Kickingereder P, Bonekamp D, Nowosielski M, Kratz A, Sill M, Burth S, et al. Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiology 2016;281:907-918  https://doi.org/10.1148/radiol.2016161382
  60. Hu LS, Ning S, Eschbacher JM, Baxter LC, Gaw N, Ranjbar S, et al. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 2017;19:128-137  https://doi.org/10.1093/neuonc/now135
  61. Park JE, Kim HS, Park SY, Nam SJ, Chun SM, Jo Y, et al. Prediction of core signaling pathway by using diffusion- and perfusion-based MRI radiomics and next-generation sequencing in isocitrate dehydrogenase wild-type glioblastoma. Radiology 2020;294:388-397  https://doi.org/10.1148/radiol.2019190913
  62. Kickingereder P, Burth S, Wick A, Gotz M, Eidel O, Schlemmer HP, et al. Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016;280:880-889  https://doi.org/10.1148/radiol.2016160845
  63. Bae S, Choi YS, Ahn SS, Chang JH, Kang SG, Kim EH, et al. Radiomic MRI phenotyping of glioblastoma: improving survival prediction. Radiology 2018;289:797-806  https://doi.org/10.1148/radiol.2018180200
  64. Kickingereder P, Neuberger U, Bonekamp D, Piechotta PL, Gotz M, Wick A, et al. Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro Oncol 2018;20:848-857  https://doi.org/10.1093/neuonc/nox188
  65. 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 
  66. Kim JY, Park JE, Jo Y, Shim WH, Nam SJ, Kim JH, et al. Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol 2019;21:404-414  https://doi.org/10.1093/neuonc/noy133
  67. Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, et al. Deep learning in medical imaging: general overview. Korean J Radiol 2017;18:570-584  https://doi.org/10.3348/kjr.2017.18.4.570
  68. Park SH. Artificial intelligence in medicine: beginner's guide. J Korean Soc Radiol 2018;78:301-308  https://doi.org/10.3348/jksr.2018.78.5.301
  69. Rudie JD, Rauschecker AM, Bryan RN, Davatzikos C, Mohan S. Emerging applications of artificial intelligence in neuro-oncology. Radiology 2019;290:607-618  https://doi.org/10.1148/radiol.2018181928
  70. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 2018;286:800-809  https://doi.org/10.1148/radiol.2017171920
  71. Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, et al. Automated abdominal segmentation of CT scans for body composition analysis using deep learning. Radiology 2019;290:669-679  https://doi.org/10.1148/radiol.2018181432
  72. Lin L, Dou Q, Jin YM, Zhou GQ, Tang YQ, Chen WL, et al. Deep learning for automated contouring of primary tumor volumes by MRI for nasopharyngeal carcinoma. Radiology 2019;291:677-686  https://doi.org/10.1148/radiol.2019182012
  73. Norman B, Pedoia V, Majumdar S. Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry. Radiology 2018;288:177-185  https://doi.org/10.1148/radiol.2018172322
  74. Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 2019;20:728-740  https://doi.org/10.1016/S1470-2045(19)30098-1
  75. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging 2012;30:1234-1248  https://doi.org/10.1016/j.mri.2012.06.010
  76. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, De Jong EEC, Van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017;14:749-762  https://doi.org/10.1038/nrclinonc.2017.141
  77. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48:441-446  https://doi.org/10.1016/j.ejca.2011.11.036
  78. Park JE, Kim HS. Radiomics as a quantitative imaging biomarker: practical considerations and the current standpoint in neuro-oncologic studies. Nucl Med Mol Imaging 2018;52:99-108  https://doi.org/10.1007/s13139-017-0512-7
  79. Ambrosini RD, Wang P, O'Dell WG. Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching. J Magn Reson Imaging 2010;31:85-93  https://doi.org/10.1002/jmri.22009
  80. Jun YH, Eo T, Kim T, Shin H, Hwang D, Bae SH, et al. Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors. Sci Rep 2018;8:9450 
  81. Perez-Ramirez U, Arana E, Moratal D. Brain metastases detection on MR by means of three-dimensional tumor-appearance template matching. J Magn Reson Imaging 2016;44:642-652  https://doi.org/10.1002/jmri.25207
  82. Iqbal T, Ali H. Generative adversarial network for medical images (MI-GAN). J Med Syst 2018;42:231