Skip to main content
SpringerLink
Log in

Differentiation of progressive disease from pseudoprogression using 3D PCASL and DSC perfusion MRI in patients with glioblastoma

  • Clinical Study
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

Purpose

To use 3D pseudocontinuous arterial spin labeling (3D PCASL) and dynamic susceptibility contrast-enhanced (DSC) perfusion MRI to differentiate progressive disease from pseudoprogression in patients with glioblastoma (GBM).

Methods

Thirty-two patients with GBM who developed progressively enhancing lesions within the radiation field following resection and chemoradiation were included in this retrospective, single-institution study. The updated modified RANO criteria were used to establish progressive disease or pseudoprogression. Following 3D PCASL and DSC MR imaging, perfusion parameter estimates of cerebral blood flow (ASL-nCBF and DSC-nrCBF) and cerebral blood volume (DSC-nrCBV) were calculated. Additionally, contrast enhanced volumes were measured. Mann–Whitney U tests were used to compare groups. Linear discriminant analysis (LDA) and area under receiver operator characteristic curve (AUC) analyses were used to evaluate performance of each perfusion parameter and to determine optimal cut-off points.

Results

All perfusion parameter measurements were higher in patients with progressive disease (mean, 95% CI ASL-nCBF 2.48, [2.03, 2.93]; DSC-nrCBF = 2.27, [1.85, 2.69]; DSC-nrCBV = 3.51, [2.37, 4.66]) compared to pseudoprogression (mean, 95% CI ASL-nCBF 0.99, [0.47, 1.52]; DSC-nrCBF = 1.05, [0.36, 1.74]; DSC-nCBV = 1.19, [0.34, 2.05]), and findings were significant at the p < 0.0125 level (p = 0.001, 0.003, 0.002; effect size: Cohen’s d = 1.48, 1.27, and 0.92). Contrast enhanced volumes were not significantly different between groups (p > 0.447). All perfusion parameters demonstrated high AUC (0.954 for ASL-nCBF, 0.867 for DSC-nrCBF, and 0.891 for DSC-nrCBV), however, ASL-nCBF demonstrated the highest AUC and misclassified the fewest cases (N = 6). Lesions correctly classified by ASL but misclassified by DSC were located along the skull base or adjacent to large resection cavities with residual blood products, at areas of increased susceptibility.

Conclusion

Both 3D PCASL and DSC perfusion MRI techniques have nearly equivalent performance for the differentiation of progressive disease from pseudoprogression in patients with GBM. However, 3D PCASL is less sensitive to susceptibility artifact and may allow for improved classification in select cases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Ostrom QT, Gittleman H, Fulop J, Liu M, Blanda R, Kromer C, Wolinsky Y, Kruchko C, Barnholtz-Sloan JS (2015) CBTRUS Statistical Report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro-oncology 17(Suppl 4):iv1–iv62

    Article  PubMed  PubMed Central  Google Scholar 

  2. Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJB, Janzer RC, Ludwin SK, Allgeier A, Fisher B, Belanger K, Hau P, Brandes AA, Gijtenbeek J, Marosi C, Vecht CJ, Mokhtari K, Wesseling P, Villa S, Eisenhauer E, Gorlia T, Weller M, Lacombe D, Cairncross JG, Mirimanoff R-O (2009) Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10(5):459–466

    Article  CAS  PubMed  Google Scholar 

  3. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJB, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanoff RO (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987–996

    Article  CAS  PubMed  Google Scholar 

  4. Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, Degroot J, Wick W, Gilbert MR, Lassman AB, Tsien C, Mikkelsen T, Wong ET, Chamberlain MC, Stupp R, Lamborn KR, Vogelbaum MA, van den Bent MJ, Chang SM (2010) Updated response assessment criteria for high-grade gliomas: response assessment in Neuro-oncology Working Group. J Clin Oncol 28(11):1963–1972

    Article  PubMed  Google Scholar 

  5. Thust SC, van den Bent MJ, Smits M (2018) Pseudoprogression of brain tumors. J Magn Reson Imaging 48(3):571–589

    Article  PubMed Central  Google Scholar 

  6. Ellingson BM, Wen PY, Cloughesy TF (2017) Modified criteria for radiographic response assessment in glioblastoma clinical trials. Neurother J Am Soc Exp Neurother 14(2):307–320

    Google Scholar 

  7. Weller M, Cloughesy T, Perry JR, Wick W (2013) Standards of care for treatment of recurrent glioblastoma—are we there yet? Neuro-oncology 15(1):4–27

    Article  PubMed  Google Scholar 

  8. Rempp KA, Brix G, Wenz F, Becker CR, Guckel F, Lorenz WJ (1994) Quantification of regional cerebral blood flow and volume with dynamic susceptibility contrast-enhanced MR imaging. Radiology 193(3):637–641

    Article  CAS  PubMed  Google Scholar 

  9. Aronen HJ, Gazit IE, Louis DN, Buchbinder BR, Pardo FS, Weisskoff RM, Harsh GR, Cosgrove GR, Halpern EF, Hochberg FH et al (1994) Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 191(1):41–51

    Article  CAS  PubMed  Google Scholar 

  10. Brem S (1976) The role of vascular proliferation in the growth of brain tumors. Clin Neurosurg 23:440–453

    Article  CAS  PubMed  Google Scholar 

  11. Plate KH, Mennel HD (1995) Vascular morphology and angiogenesis in glial tumors. Exp Toxicol Pathol 47(2–3):89–94

    Article  CAS  PubMed  Google Scholar 

  12. Wesseling P, van der Laak JA, de Leeuw H, Ruiter DJ, Burger PC (1994) Quantitative immunohistological analysis of the microvasculature in untreated human glioblastoma multiforme. Computer-assisted image analysis of whole-tumor sections. J Neurosurg 81(6):902–909

    Article  CAS  PubMed  Google Scholar 

  13. Hygino da Cruz LC, Rodriguez I, Domingues RC, Gasparetto EL, Sorensen AG (2011) Pseudoprogression and pseudoresponse: imaging challenges in the assessment of posttreatment glioma. Am J Neuroradiol 32(11):1978

    Article  PubMed  PubMed Central  Google Scholar 

  14. Barajas RF Jr, Chang JS, Segal MR, Parsa AT, McDermott MW, Berger MS, Cha S (2009) Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 253(2):486–496

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hu LS, Baxter LC, Smith KA, Feuerstein BG, Karis JP, Eschbacher JM, Coons SW, Nakaji P, Yeh RF, Debbins J, Heiserman JE (2009) 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. Am J Neuroradiol 30(3):552–558

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Mangla R, Singh G, Ziegelitz D, Milano MT, Korones DN, Zhong J, Ekholm SE (2010) Changes in relative cerebral blood volume 1 month after radiation-temozolomide therapy can help predict overall survival in patients with glioblastoma. Radiology 256(2):575–584

    Article  PubMed  Google Scholar 

  17. Sadowski EA, Bennett LK, Chan MR, Wentland AL, Garrett AL, Garrett RW, Djamali A (2007) Nephrogenic systemic fibrosis: risk factors and incidence estimation. Radiology 243(1):148–157

    Article  PubMed  Google Scholar 

  18. McDonald RJ, McDonald JS, Kallmes DF, Jentoft ME, Murray DL, Thielen KR, Williamson EE, Eckel LJ (2015) Intracranial gadolinium deposition after contrast-enhanced MR imaging. Radiology 275(3):772–782

    Article  PubMed  Google Scholar 

  19. Boxerman JL, Schmainda KM, Weisskoff RM (2006) Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. Am J Neuroradiol 27(4):859–867

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Welker K, Boxerman J, Kalnin A, Kaufmann T, Shiroishi M, Wintermark M (2015) ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain. Am J Neuroradiol 36(6):E41–E51

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Paulson ES, Schmainda KM (2008) Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors. Radiology 249(2):601–613

    Article  PubMed  PubMed Central  Google Scholar 

  22. Jackson A, O’Connor J, Thompson G, Mills S (2008) Magnetic resonance perfusion imaging in neuro-oncology. Cancer Imaging Off Publ Int Cancer Imaging Soc 8(1):186–199

    Google Scholar 

  23. Grade M, Hernandez Tamames JA, Pizzini FB, Achten E, Golay X, Smits M (2015) A neuroradiologist’s guide to arterial spin labeling MRI in clinical practice. Neuroradiology 57(12):1181–1202

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Wong EC (2014) An introduction to ASL labeling techniques. J Magn Reson Imaging 40(1):1–10

    Article  PubMed  Google Scholar 

  25. Alsop DC, Detre JA, Golay X, Günther M, Hendrikse J, Hernandez-Garcia L, Lu H, MacIntosh BJ, Parkes LM, Smits M, van Osch MJP, Wang DJJ, Wong EC, Zaharchuk G (2015) Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM Perfusion Study Group and the European Consortium for ASL in dementia. Magn Reson Med 73(1):102–116

    Article  PubMed  Google Scholar 

  26. Järnum H, Steffensen EG, Knutsson L, Fründ E-T, Simonsen CW, Lundbye-Christensen S, Shankaranarayanan A, Alsop DC, Jensen FT, Larsson E-M (2010) Perfusion MRI of brain tumours: a comparative study of pseudo-continuous arterial spin labelling and dynamic susceptibility contrast imaging. Neuroradiology 52(4):307–317

    Article  PubMed  Google Scholar 

  27. Lehmann P, Monet P, de Marco G, Saliou G, Perrin M, Stoquart-Elsankari S, Bruniau A, Vallee JN (2010) A comparative study of perfusion measurement in brain tumours at 3 Tesla MR: arterial spin labeling versus dynamic susceptibility contrast-enhanced MRI. Eur Neurol 64(1):21–26

    Article  CAS  PubMed  Google Scholar 

  28. Warmuth C, Gunther M, Zimmer C (2003) Quantification of blood flow in brain tumors: comparison of arterial spin labeling and dynamic susceptibility-weighted contrast-enhanced MR imaging. Radiology 228(2):523–532

    Article  PubMed  Google Scholar 

  29. Noguchi T, Yoshiura T, Hiwatashi A, Togao O, Yamashita K, Nagao E, Shono T, Mizoguchi M, Nagata S, Sasaki T, Suzuki SO, Iwaki T, Kobayashi K, Mihara F, Honda H (2008) Perfusion imaging of brain tumors using arterial spin-labeling: correlation with histopathologic vascular density. Am J Neuroradiol 29(4):688–693

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cebeci H, Aydin O, Ozturk-Isik E, Gumus C, Inecikli F, Bekar A, Kocaeli H, Hakyemez B (2014) Assessment of perfusion in glial tumors with arterial spin labeling; comparison with dynamic susceptibility contrast method. Eur J Radiol 83(10):1914–1919

    Article  CAS  PubMed  Google Scholar 

  31. Ozsunar Y, Mullins ME, Kwong K, Hochberg FH, Ament C, Schaefer PW, Gonzalez RG, Lev MH (2010) Glioma recurrence versus radiation necrosis? A pilot comparison of arterial spin-labeled, dynamic susceptibility contrast enhanced MRI, and FDG-PET imaging. Acad Radiol 17(3):282–290

    Article  PubMed  Google Scholar 

  32. Wang Y-L, Chen S, Xiao H-F, Li Y, Wang Y, Liu G, Lou X, Ma L (2018) Differentiation between radiation-induced brain injury and glioma recurrence using 3D PCASL and dynamic susceptibility contrast-enhanced perfusion-weighted imaging. Radiother Oncol 129(1):68–74

    Article  PubMed  Google Scholar 

  33. Xu Q, Liu Q, Ge H, Ge X, Wu J, Qu J, Xu K (2017) Tumor recurrence versus treatment effects in glioma: a comparative study of three dimensional pseudo-continuous arterial spin labeling and dynamic susceptibility contrast imaging. Medicine 96(50):e9332–e9332

    Article  PubMed  PubMed Central  Google Scholar 

  34. Ye J, Bhagat SK, Li H, Luo X, Wang B, Liu L, Yang G (2016) Differentiation between recurrent gliomas and radiation necrosis using arterial spin labeling perfusion imaging. Exp Ther Med 11(6):2432–2436

    Article  PubMed  PubMed Central  Google Scholar 

  35. Jovanovic M, Radenkovic S, Stosic-Opincal T, Lavrnic S, Gavrilovic S, Lazovic-Popovic B, Soldatovic I, Maksimovic R (2017) Differentiation between progression and pseudoprogression by arterial spin labeling MRI in patients with glioblastoma multiforme. J BUON 22(4):1061–1067

    PubMed  Google Scholar 

  36. Choi YJ, Kim HS, Jahng G-H, Kim SJ, Suh DC (2013) Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging. Acta Radiol 54(4):448–454

    Article  PubMed  Google Scholar 

  37. Buckner JC, Shaw EG, Pugh SL, Chakravarti A, Gilbert MR, Barger GR, Coons S, Ricci P, Bullard D, Brown PD, Stelzer K, Brachman D, Suh JH, Schultz CJ, Bahary JP, Fisher BJ, Kim H, Murtha AD, Bell EH, Won M, Mehta MP, Curran WJ Jr (2016) Radiation plus procarbazine, CCNU, and vincristine in low-grade glioma. N Engl J Med 374(14):1344–1355

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Larsen J, Wharton SB, McKevitt F, Romanowski C, Bridgewater C, Zaki H, Hoggard N (2017) ‘Low grade glioma’: an update for radiologists. Br J Radiol 90(1070):20160600–20160600

    Article  PubMed  PubMed Central  Google Scholar 

  39. Farid N, Almeida-Freitas DB, White NS, McDonald CR, Kuperman JM, Almutairi AA, Muller KA, VandenBerg SR, Kesari S, Dale AM (2014) Combining diffusion and perfusion differentiates tumor from bevacizumab-related imaging abnormality (BRIA). J Neurooncol 120(3):539–546

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Perme M, Blas M, Turk S (2004) Comparison of logistic regression and linear discriminant analysis: a simulation study. Metod Zv 1:143–161

    Google Scholar 

  41. van West SE, de Bruin HG, van de Langerijt B, Swaak-Kragten AT, van den Bent MJ, Taal W (2016) Incidence of pseudoprogression in low-grade gliomas treated with radiotherapy. Neurooncology 19(5):719–725

    Google Scholar 

  42. Melguizo-Gavilanes I, Bruner JM, Guha-Thakurta N, Hess KR, Puduvalli VK (2015) Characterization of pseudoprogression in patients with glioblastoma: is histology the gold standard? J Neurooncol 123(1):141–150

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ (2008) Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol 9(5):453–461

    Article  PubMed  Google Scholar 

  44. Chamberlain MC, Glantz MJ, Chalmers L, Van Horn A, Sloan AE (2007) Early necrosis following concurrent Temodar and radiotherapy in patients with glioblastoma. J Neurooncol 82(1):81–83

    Article  PubMed  Google Scholar 

  45. de Wit MCY, de Bruin HG, Eijkenboom W, Sillevis Smitt PAE, van den Bent MJ (2004) Immediate post-radiotherapy changes in malignant glioma can mimic tumor progression. Neurology 63(3):535–537

    Article  PubMed  Google Scholar 

  46. Taal W, Brandsma D, de Bruin HG, Bromberg JE, Swaak-Kragten AT, Sillevis Smitt PAE, van Es CA, van den Bent MJ (2008) Incidence of early pseudo-progression in a cohort of malignant glioma patients treated with chemoirradiation with temozolomide. Cancer 113(2):405–410

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

This study was supported by GE Healthcare GE14347180341 (N.F. and C.R.M.) and American Cancer Society RSG-15-229-01-CCE (C.R.M).

Author information

Authors and Affiliations

  1. Department of Radiology, University of California, San Diego, La Jolla, CA, 92037, USA

    Paul Manning, Shadi Daghighi, Anders M. Dale, Divya Bolar & Nikdokht Farid

  2. Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, CA, 92037, USA

    Paul Manning, Shadi Daghighi, Matthew K. Rajaratnam, Sowmya Parthiban, Naeim Bahrami, Anders M. Dale, Carrie R. McDonald & Nikdokht Farid

  3. Department of Neurosciences, University of California, San Diego, La Jolla, CA, 92037, USA

    Anders M. Dale, David E. Piccioni & Carrie R. McDonald

  4. Center for Functional Magnetic Resonance Imaging, University of California, San Diego, La Jolla, CA, 92037, USA

    Divya Bolar

  5. Department of Psychiatry, University of California, San Diego, La Jolla, CA, 92037, USA

    Carrie R. McDonald

  6. Department of Radiology, University of California, San Diego, 200 West Arbor Drive, Mailbox # 8756, San Diego, CA, 92103, USA

    Paul Manning

Authors
  1. Paul Manning
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shadi Daghighi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew K. Rajaratnam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sowmya Parthiban
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Naeim Bahrami
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anders M. Dale
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Divya Bolar
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. David E. Piccioni
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Carrie R. McDonald
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nikdokht Farid
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paul Manning.

Ethics declarations

Conflict of interest

Carrie R. McDonald and Nikdokht Farid have received funding from GE Healthcare. David E. Piccioni is on the advisory board for Tocagen. Other authors report no conflicts of interest related to this study.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional and/or National Research Committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manning, P., Daghighi, S., Rajaratnam, M.K. et al. Differentiation of progressive disease from pseudoprogression using 3D PCASL and DSC perfusion MRI in patients with glioblastoma. J Neurooncol 147, 681–690 (2020). https://doi.org/10.1007/s11060-020-03475-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11060-020-03475-y

Keywords

Search

Navigation