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Added Value of Contrast Leakage Information over the CBV Value of DSC Perfusion MRI to Differentiate between Pseudoprogression and True Progression after Concurrent Chemoradiotherapy in Glioblastoma Patients

  • Pak, Elena (Department of Radiology, Seoul National University Hospital) ;
  • Choi, Seung Hong (Department of Radiology, Seoul National University Hospital) ;
  • Park, Chul-Kee (Department of Neurosurgery and Biomedical Research Institute, Seoul National University Hospital) ;
  • Kim, Tae Min (Department of Internal Medicine and Cancer Research Institute, Seoul National University Hospital) ;
  • Park, Sung-Hye (Department of Pathology, Seoul National University Hospital) ;
  • Won, Jae-Kyung (Department of Pathology, Seoul National University Hospital) ;
  • Lee, Joo Ho (Department of Radiation Oncology and Cancer Research Institute, Seoul National University Hospital) ;
  • Lee, Soon-Tae (Department of Neurology, Seoul National University Hospital) ;
  • Hwang, Inpyeong (Department of Radiology, Seoul National University Hospital) ;
  • Yoo, Roh-Eul (Department of Radiology, Seoul National University Hospital) ;
  • Kang, Koung Mi (Department of Radiology, Seoul National University Hospital) ;
  • Yun, Tae Jin (Department of Radiology, Seoul National University Hospital)
  • Received : 2021.10.04
  • Published : 2022.03.30

Abstract

Purpose: To evaluate whether the added value of contrast leakage information from dynamic susceptibility contrast magnetic resonance imaging (DSC MRI) is a better prognostic imaging biomarker than the cerebral blood volume (CBV) value in distinguishing true progression from pseudoprogression in glioblastoma patients. Materials and Methods: Forty-nine glioblastoma patients who had undergone MRI after concurrent chemoradiotherapy with temozolomide were enrolled in this retrospective study. Twenty features were extracted from the normalized relative CBV (nCBV) and extraction fraction (EF) map of the contrast-enhancing region in each patient. After univariable analysis, we used multivariable stepwise logistic regression analysis to identify significant predictors for differentiating between pseudoprogression and true progression. Receiver operating characteristic (ROC) analysis was employed to determine the best cutoff values for the nCBV and EF features. Finally, leave-one-out cross-validation was used to validate the best predictor in differentiating between true progression and pseudoprogression. Results: Multivariable stepwise logistic regression analysis showed that MGMT (O6-methylguanine-DNA methyltransferase) and EF max were independent differentiating variables (P = 0.004 and P = 0.02, respectively). ROC analysis yielded the best cutoff value of 95.75 for the EF max value for differentiating the two groups (sensitivity, 61%; specificity, 84.6%; AUC, 0.681 ± 0.08; 95% CI, 0.524-0.837; P = 0.03). In the leave-one-out cross-validation of the EF max value, the cross-validated values for predicting true progression and pseudoprogression accuracies were 69.4% and 71.4%, respectively. Conclusion: We demonstrated that contrast leakage information parameter from DSC MRI showed significance in differentiating true progression from pseudoprogression in glioblastoma patients.

Keywords

Acknowledgement

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 (NRF-2016M3C7A1914002), by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2020R1A2C2008949 and NRF-2020R1A4A1018714), by Creative-Pioneering Researchers Program through Seoul National University (SNU), and by the Institute for Basic Science (IBS-R006-A1).

References

  1. de Groot JF. High-grade gliomas. Continuum (Minneap Minn) 2015;21:332-344 https://doi.org/10.1212/01.CON.0000464173.58262.d9
  2. Ellingson BM, Chung C, Pope WB, Boxerman JL, Kaufmann TJ. Pseudoprogression, radionecrosis, inflammation or true tumor progression? challenges associated with glioblastoma response assessment in an evolving therapeutic landscape. J Neurooncol 2017;134:495-504 https://doi.org/10.1007/s11060-017-2375-2
  3. Fajardo LF, Berthrong M, Anderson RE. Radiation pathology. New York: Oxford University Press, 2001
  4. Hygino da Cruz LC Jr, Rodriguez I, Domingues RC, Gasparetto EL, Sorensen AG. Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. AJNR Am J Neuroradiol 2011;32:1978-1985 https://doi.org/10.3174/ajnr.A2397
  5. Linhares P, Carvalho B, Figueiredo R, Reis RM, Vaz R. Early pseudoprogression following chemoradiotherapy in glioblastoma patients: the value of RANO evaluation. J Oncol 2013;2013:690585 https://doi.org/10.1155/2013/690585
  6. Barajas RF Jr, Chang JS, Segal MR, 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;253:486-496 https://doi.org/10.1148/radiol.2532090007
  7. Wurdinger T, Tannous BA. Glioma angiogenesis: towards novel RNA therapeutics. Cell Adh Migr 2009;3:230-235 https://doi.org/10.4161/cam.3.2.7910
  8. Abbasi AW, Westerlaan HE, Holtman GA, Aden KM, van Laar PJ, van der Hoorn A. Incidence of tumour progression and pseudoprogression in high-grade gliomas: a systematic review and meta-analysis. Clin Neuroradiol 2018;28:401-411 https://doi.org/10.1007/s00062-017-0584-x
  9. Bjornerud A, Sorensen AG, Mouridsen K, Emblem KE. T1- and T2*-dominant extravasation correction in DSC-MRI: part I--theoretical considerations and implications for assessment of tumor hemodynamic properties. J Cereb Blood Flow Metab 2011;31:2041-2053 https://doi.org/10.1038/jcbfm.2011.52
  10. Kim SH, Cho KH, Choi SH, et al. Prognostic predictions for patients with glioblastoma after standard treatment: application of contrast leakage information from DSC-MRI within nonenhancing FLAIR high-signal-intensity lesions. AJNR Am J Neuroradiol 2019;40:2052-2058
  11. Lee B, Park JE, Bjornerud A, Kim JH, Lee JY, Kim HS. Clinical value of vascular permeability estimates using dynamic susceptibility contrast MRI: improved diagnostic performance in distinguishing hypervascular primary CNS lymphoma from glioblastoma. AJNR Am J Neuroradiol 2018;39:1415-1422
  12. Emblem KE, Bjornerud A, Mouridsen K, et al. T(1)- and T(2) (*)-dominant extravasation correction in DSC-MRI: part II-predicting patient outcome after a single dose of cediranib in recurrent glioblastoma patients. J Cereb Blood Flow Metab 2011;31:2054-2064 https://doi.org/10.1038/jcbfm.2011.39
  13. Wang S, Martinez-Lage M, Sakai Y, 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
  14. Young RJ, Gupta A, Shah AD, et al. MRI perfusion in determining pseudoprogression in patients with glioblastoma. Clin Imaging 2013;37:41-49 https://doi.org/10.1016/j.clinimag.2012.02.016
  15. Wen PY, Macdonald DR, Reardon DA, 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
  16. Yin J, Sun H, Yang J, Guo Q. Comparison of K-means and fuzzy c-means algorithm performance for automated determination of the arterial input function. PLoS One 2014;9:e85884 https://doi.org/10.1371/journal.pone.0085884
  17. Yan LF, Sun YZ, Zhao SS, et al. Perfusion, diffusion, or brain tumor barrier integrity: which represents the glioma features best? Cancer Manag Res 2019;11:9989-10000 https://doi.org/10.2147/CMAR.S197839
  18. Wong AM, Yan FX, Liu HL. Comparison of three-dimensional pseudo-continuous arterial spin labeling perfusion imaging with gradient-echo and spin-echo dynamic susceptibility contrast MRI. J Magn Reson Imaging 2014;39:427-433 https://doi.org/10.1002/jmri.24178
  19. Sanz-Requena R, Prats-Montalban JM, Marti-Bonmati L, et al. Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images. J Magn Reson Imaging 2015;42:477-487 https://doi.org/10.1002/jmri.24805
  20. Rosen BR, Belliveau JW, Vevea JM, Brady TJ. Perfusion imaging with NMR contrast agents. Magn Reson Med 1990;14:249-265 https://doi.org/10.1002/mrm.1910140211
  21. Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 1996;36:715-725 https://doi.org/10.1002/mrm.1910360510
  22. Kim JH, Choi SH, Ryoo I, et al. Prognosis prediction of measurable enhancing lesion after completion of standard concomitant chemoradiotherapy and adjuvant temozolomide in glioblastoma patients: application of dynamic susceptibility contrast perfusion and diffusion-weighted imaging. PLoS One 2014;9:e113587 https://doi.org/10.1371/journal.pone.0113587
  23. Sourbron S, Ingrisch M, Siefert A, Reiser M, Herrmann K. Quantification of cerebral blood flow, cerebral blood volume, and blood-brain-barrier leakage with DCE-MRI. Magn Reson Med 2009;62:205-217 https://doi.org/10.1002/mrm.22005
  24. 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
  25. Kim JY, Yoon MJ, Park JE, Choi EJ, Lee J, Kim HS. Radiomics in peritumoral non-enhancing regions: fractional anisotropy and cerebral blood volume improve prediction of local progression and overall survival in patients with glioblastoma. Neuroradiology 2019;61:1261-1272 https://doi.org/10.1007/s00234-019-02255-4
  26. Hauck WW, Miike R. A proposal for examining and reporting stepwise regressions. Stat Med 1991;10:711-715 https://doi.org/10.1002/sim.4780100505
  27. Thust SC, van den Bent MJ, Smits M. Pseudoprogression of brain tumors. J Magn Reson Imaging 2018;48:571-589 https://doi.org/10.1002/jmri.26171
  28. Cha J, Kim ST, Kim HJ, et al. Differentiation of tumor progression from pseudoprogression in patients with posttreatment glioblastoma using multiparametric histogram analysis. AJNR Am J Neuroradiol 2014;35:1309-1317 https://doi.org/10.3174/ajnr.A3876
  29. Park HH, Roh TH, Kang SG, et al. Pseudoprogression in glioblastoma patients: the impact of extent of resection. J Neurooncol 2016;126:559-566 https://doi.org/10.1007/s11060-015-2001-0
  30. Boxerman JL, Ellingson BM, Jeyapalan S, et al. Longitudinal DSC-MRI for distinguishing tumor recurrence from pseudoprogression in patients with a high-grade glioma. Am J Clin Oncol 2017;40:228-234 https://doi.org/10.1097/COC.0000000000000156
  31. Neska-Matuszewska M, Bladowska J, Sasiadek M, Zimny A. Differentiation of glioblastoma multiforme, metastases and primary central nervous system lymphomas using multiparametric perfusion and diffusion MR imaging of a tumor core and a peritumoral zone-Searching for a practical approach. PLoS One 2018;13:e0191341 https://doi.org/10.1371/journal.pone.0191341
  32. Essig M, Shiroishi MS, Nguyen TB, et al. Perfusion MRI: the five most frequently asked technical questions. AJR Am J Roentgenol 2013;200:24-34 https://doi.org/10.2214/AJR.12.9543
  33. Soliman HM, ElBeheiry AA, Abdel-Kerim AA, Farhoud AH, Reda MI. Recurrent brain tumor versus radiation necrosis; can dynamic susceptibility contrast (DSC) perfusion magnetic resonance imaging differentiate? Egypt J Radiol Nucl Med 2018;49:719-726 https://doi.org/10.1016/j.ejrnm.2018.03.013
  34. Song YS, Choi SH, Park CK, 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 J Radiol 2013;14:662-672 https://doi.org/10.3348/kjr.2013.14.4.662
  35. Hu LS, Baxter LC, Smith KA, 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. AJNR Am J Neuroradiol 2009;30:552-558 https://doi.org/10.3174/ajnr.A1377
  36. Jain R, Narang J, Schultz L, et al. Permeability estimates in histopathology-proved treatment-induced necrosis using perfusion CT: can these add to other perfusion parameters in differentiating from recurrent/progressive tumors? AJNR Am J Neuroradiol 2011;32:658-663 https://doi.org/10.3174/ajnr.A2378
  37. Jain R. Perfusion CT imaging of brain tumors: an overview. AJNR Am J Neuroradiol 2011;32:1570-1577 https://doi.org/10.3174/ajnr.A2263
  38. Kwon YW, Moon WJ, Park M, et al. Dynamic susceptibility contrast (DSC) perfusion MR in the prediction of long-term survival of glioblastomas (GBM): correlation with MGMT promoter methylation and 1p/19q deletions. Investig Magn Reson Imaging 2018;22:158-167 https://doi.org/10.13104/imri.2018.22.3.158
  39. Binabaj MM, Bahrami A, ShahidSales S, et al. The prognostic value of MGMT promoter methylation in glioblastoma: a meta-analysis of clinical trials. J Cell Physiol 2018;233:378-386 https://doi.org/10.1002/jcp.25896
  40. Brandes AA, Franceschi E, Tosoni A, 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