Korean Journal of Radiology

  • 제21권6호
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  • Pages.707-716
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  • 2020
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  • 1229-6929(pISSN)
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  • 2005-8330(eISSN)
대한영상의학회 (The Korean Society of Radiology)
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Prognostic Value of Dynamic Contrast-Enhanced MRI-Derived Pharmacokinetic Variables in Glioblastoma Patients: Analysis of Contrast-Enhancing Lesions and Non-Enhancing T2 High-Signal Intensity Lesions

  • Yeonah Kang (Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine) ;
  • Eun Kyoung Hong (Department of Radiology, Seoul National University Hospital) ;
  • Jung Hyo Rhim (Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center) ;
  • Roh-Eul Yoo (Department of Radiology, Seoul National University Hospital) ;
  • Koung Mi Kang (Department of Radiology, Seoul National University Hospital) ;
  • Tae Jin Yun (Department of Radiology, Seoul National University Hospital) ;
  • Ji-Hoon Kim (Department of Radiology, Seoul National University Hospital) ;
  • Chul-Ho Sohn (Department of Radiology, Seoul National University Hospital) ;
  • Sun-Won Park (Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center) ;
  • Seung Hong Choi (Department of Radiology, Seoul National University Hospital)
  • 투고 : 2019.08.21
  • 심사 : 2020.02.09
  • 발행 : 2020.06.01
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초록

Objective: To evaluate pharmacokinetic variables from contrast-enhancing lesions (CELs) and non-enhancing T2 high signal intensity lesions (NE-T2HSILs) on dynamic contrast-enhanced (DCE) magnetic resonance (MR) imaging for predicting progression-free survival (PFS) in glioblastoma (GBM) patients. Materials and Methods: Sixty-four GBM patients who had undergone preoperative DCE MR imaging and received standard treatment were retrospectively included. We analyzed the pharmacokinetic variables of the volume transfer constant (Ktrans) and volume fraction of extravascular extracellular space within the CEL and NE-T2HSIL of the entire tumor. Univariate and multivariate Cox regression analyses were performed using preoperative clinical characteristics, pharmacokinetic variables of DCE MR imaging, and postoperative molecular biomarkers to predict PFS. Results: The increased mean Ktrans of the CEL, increased 95th percentile Ktrans of the CELs, and absence of methylated O6-methylguanine-DNA methyltransferase promoter were relevant adverse variables for PFS in the univariate analysis (p = 0.041, p = 0.032, and p = 0.083, respectively). The Kaplan-Meier survival curves demonstrated that PFS was significantly shorter in patients with a mean Ktrans of the CEL > 0.068 and 95th percentile Ktrans of the CEL > 0.223 (log-rank p = 0.038 and p = 0.041, respectively). However, only mean Ktrans of the CEL was significantly associated with PFS (p = 0.024; hazard ratio, 553.08; 95% confidence interval, 2.27-134756.74) in the multivariate Cox proportional hazard analysis. None of the pharmacokinetic variables from NE-T2HSILs were significantly related to PFS. Conclusion: Among the pharmacokinetic variables extracted from CELs and NE-T2HSILs on preoperative DCE MR imaging, the mean Ktrans of CELs exhibits potential as a useful imaging predictor of PFS in GBM patients.

키워드

과제정보

This study was supported by a grant from the Korea Healthcare technology R&D Projects, Ministry for Health, Welfare & Family Affairs (HI16C1111), by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2016M3C7A1914002), by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B2006526), by Creative-Pioneering Researchers Program through Seoul National University (SNU), and by Project Code (IBS-R006-D1).

참고문헌

  1. Eidel O, Burth S, Neumann JO, Kieslich PJ, Sahm F, Jungk C, et al. Tumor infiltration in enhancing and non-enhancing parts of glioblastoma: a correlation with histopathology. PLoS One 2017;12:e0169292 
  2. 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 treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. Neuro Oncol 2012;14:942-954  https://doi.org/10.1093/neuonc/nos128
  3. Oh J, Henry RG, Pirzkall A, Lu Y, Li X, Catalaa I, et al. Survival analysis in patients with glioblastoma multiforme: predictive value of choline-to-N-acetylaspartate index, apparent diffusion coefficient, and relative cerebral blood volume. J Magn Reson Imaging 2004;19:546-554  https://doi.org/10.1002/jmri.20039
  4. 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;352:987-996  https://doi.org/10.1056/NEJMoa043330
  5. Albert FK, Forsting M, Sartor K, Adams HP, Kunze S. Early postoperative magnetic resonance imaging after resection of malignant glioma: objective evaluation of residual tumor and its influence on regrowth and prognosis. Neurosurgery 1994;34:45-60; discussion 60-61  https://doi.org/10.1227/00006123-199401000-00008
  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. Norden AD, Drappatz J, Muzikansky A, David K, Gerard M, McNamara MB, et al. An exploratory survival analysis of anti-angiogenic therapy for recurrent malignant glioma. J Neurooncol 2009;92:149-155  https://doi.org/10.1007/s11060-008-9745-8
  8. Hardee ME, Zagzag D. Mechanisms of glioma-associated neovascularization. Am J Pathol 2012;181:1126-1141  https://doi.org/10.1016/j.ajpath.2012.06.030
  9. Jain R. Measurements of tumor vascular leakiness using DCE in brain tumors: clinical applications. NMR Biomed 2013;26:1042-1049  https://doi.org/10.1002/nbm.2994
  10. Jia Z, Geng D, Xie T, Zhang J, Liu Y. Quantitative analysis of neovascular permeability in glioma by dynamic contrast-enhanced MR imaging. J Clin Neurosci 2012;19:820-823  https://doi.org/10.1016/j.jocn.2011.08.030
  11. Jung SC, Yeom JA, Kim JH, Ryoo I, Kim SC, Shin H, et al. Glioma: application of histogram analysis of pharmacokinetic parameters from T1-weighted dynamic contrast-enhanced MR imaging to tumor grading. AJNR Am J Neuroradiol 2014;35:1103-1110  https://doi.org/10.3174/ajnr.A3825
  12. Cao Y, Nagesh V, Hamstra D, Tsien CI, Ross BD, Chenevert TL, et al. The extent and severity of vascular leakage as evidence of tumor aggressiveness in high-grade gliomas. Cancer Res 2006;66:8912-8917  https://doi.org/10.1158/0008-5472.CAN-05-4328
  13. Ulyte A, Katsaros VK, Liouta E, Stranjalis G, Boskos C, Papanikolaou N, et al. Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients. Neuroradiology 2016;58:1197-1208  https://doi.org/10.1007/s00234-016-1741-7
  14. Kim R, Choi SH, Yun TJ, Lee ST, Park CK, Kim TM, et al. Prognosis prediction of non-enhancing T2 high signal intensity lesions in glioblastoma patients after standard treatment: application of dynamic contrast-enhanced MR imaging. Eur Radiol 2017;27:1176-1185  https://doi.org/10.1007/s00330-016-4464-6
  15. Nam JG, Kang KM, Choi SH, Lim WH, Yoo RE, Kim JH, et al. Comparison between the prebolus T1 measurement and the fixed T1 value in dynamic contrast-enhanced MR imaging for the differentiation of true progression from pseudoprogression in glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy. AJNR Am J Neuroradiol 2017;38:2243-2250  https://doi.org/10.3174/ajnr.A5417
  16. Tietze A, Mouridsen K, Mikkelsen IK. The impact of reliable prebolus T1 measurements or a fixed T1 value in the assessment of glioma patients with dynamic contrast enhancing MRI. Neuroradiology 2015;57:561-572  https://doi.org/10.1007/s00234-015-1502-z
  17. Haacke EM, Filleti CL, Gattu R, Ciulla C, Al-Bashir A, Suryanarayanan K, et al. New algorithm for quantifying vascular changes in dynamic contrast-enhanced MRI independent of absolute T1 values. Magn Reson Med 2007;58:463-472  https://doi.org/10.1002/mrm.21358
  18. Tofts PS, Kermode AG. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med 1991;17:357-367  https://doi.org/10.1002/mrm.1910170208
  19. Sundar H, Shen D, Biros G, Xu C, Davatzikos C. Robust computation of mutual information using spatially adaptive meshes. Med Image Comput Comput Assist Interv 2007;10(Pt 1):950-958  https://doi.org/10.1007/978-3-540-75757-3_115
  20. Pluim JP, Maintz JB, Viergever MA. Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 2003;22:986-1004  https://doi.org/10.1109/TMI.2003.815867
  21. Lacroix M, Abi-Said D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, et al. A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 2001;95:190-198  https://doi.org/10.3171/jns.2001.95.2.0190
  22. Hammoud MA, Sawaya R, Shi W, Thall PF, Leeds NE. Prognostic significance of preoperative MRI scans in glioblastoma multiforme. J Neurooncol 1996;27:65-73  https://doi.org/10.1007/BF00146086
  23. Cha S, Johnson G, Wadghiri YZ, Jin O, Babb J, Zagzag D, et al. Dynamic, contrast-enhanced perfusion MRI in mouse gliomas: correlation with histopathology. Magn Reson Med 2003;49:848-855  https://doi.org/10.1002/mrm.10446
  24. Choi YS, Kim DW, Lee SK, Chang JH, Kang SG, Kim EH, et al. The added prognostic value of preoperative dynamic contrast-enhanced MRI histogram analysis in patients with glioblastoma: analysis of overall and progression-free survival. AJNR Am J Neuroradiol 2015;36:2235-2241  https://doi.org/10.3174/ajnr.A4449
  25. Aref M, Chaudhari AR, Bailey KL, Aref S, Wiener EC. Comparison of tumor histology to dynamic contrast enhanced magnetic resonance imaging-based physiological estimates. Magn Reson Imaging 2008;26:1279-1293  https://doi.org/10.1016/j.mri.2008.02.015
  26. Bisdas S, Smrdel U, Bajrovic FF, Surlan-Popovic K. Assessment of progression-free-survival in glioblastomas by intratreatment dynamic contrast-enhanced MRI. Clin Neuroradiol 2016;26:39-45  https://doi.org/10.1007/s00062-014-0328-0
  27. Nguyen TB, Cron GO, Perdrizet K, Bezzina K, Torres CH, Chakraborty S, et al. Comparison of the diagnostic accuracy of DSC- and dynamic contrast-enhanced MRI in the preoperative grading of astrocytomas. AJNR Am J Neuroradiol 2015;36:2017-2022  https://doi.org/10.3174/ajnr.A4398
  28. Barajas RF Jr, Hodgson JG, Chang JS, Vandenberg SR, Yeh RF, Parsa AT, et al. Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging. Radiology 2010;254:564-576 https://doi.org/10.1148/radiol.09090663