, Volume 53, Issue 4, pp 291–302 | Cite as

ADC histograms predict response to anti-angiogenic therapy in patients with recurrent high-grade glioma

  • Martha Nowosielski
  • Wolfgang Recheis
  • Georg Goebel
  • Özgür Güler
  • Gerd Tinkhauser
  • Herwig Kostron
  • Michael Schocke
  • Thaddaeus Gotwald
  • Günther Stockhammer
  • Markus Hutterer
Functional Neuroradiology



The purpose of this study is to evaluate apparent diffusion coefficient (ADC) maps to distinguish anti-vascular and anti-tumor effects in the course of anti-angiogenic treatment of recurrent high-grade gliomas (rHGG) as compared to standard magnetic resonance imaging (MRI).


This retrospective study analyzed ADC maps from diffusion-weighted MRI in 14 rHGG patients during bevacizumab/irinotecan (B/I) therapy. Applying image segmentation, volumes of contrast-enhanced lesions in T1 sequences and of hyperintense T2 lesions (hT2) were calculated. hT2 were defined as regions of interest (ROI) and registered to corresponding ADC maps (hT2-ADC). Histograms were calculated from hT2-ADC ROIs. Thereafter, histogram asymmetry termed “skewness” was calculated and compared to progression-free survival (PFS) as defined by the Response Assessment Neuro-Oncology (RANO) Working Group criteria.


At 8–12 weeks follow-up, seven (50%) patients showed a partial response, three (21.4%) patients were stable, and four (28.6%) patients progressed according to RANO criteria. hT2-ADC histograms demonstrated statistically significant changes in skewness in relation to PFS at 6 months. Patients with increasing skewness (n = 11) following B/I therapy had significantly shorter PFS than did patients with decreasing or stable skewness values (n = 3, median percentage change in skewness 54% versus −3%, p = 0.04).


In rHGG patients, the change in ADC histogram skewness may be predictive for treatment response early in the course of anti-angiogenic therapy and more sensitive than treatment assessment based solely on RANO criteria.


Recurrent high-grade glioma Anti-angiogenic therapy DWI-MRI ADC histograms Skewness 


  1. 1.
    Wong ET, Hess KR, Gleason MJ et al (1999) Outcomes and prognostic factors in recurrent glioma patients enrolled onto phase II clinical trials. J Clin Oncol 17:2572–2578PubMedGoogle Scholar
  2. 2.
    Lamborn KR, Yung WK, Chang SM et al (2008) Progression-free survival: an important end point in evaluating therapy for recurrent high-grade gliomas. Neuro Oncol 10:162–170PubMedCrossRefGoogle Scholar
  3. 3.
    Vredenburgh JJ, Desjardins A, Herndon JE 2nd et al (2007) Bevacizumab plus irinotecan in recurrent glioblastoma multiforme. J Clin Oncol 25:4722–4729PubMedCrossRefGoogle Scholar
  4. 4.
    Macdonald DR, Cascino TL, Schold SC Jr, Cairncross JG (1990) Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 8:1277–1280PubMedGoogle Scholar
  5. 5.
    Wen PY, Macdonald DR, Reardon DA et al (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28:1963–1972PubMedCrossRefGoogle Scholar
  6. 6.
    Jain R, Scarpace LM, Ellika S et al (2009) Imaging response criteria for recurrent gliomas treated with bevacizumab: role of diffusion weighted imaging as an imaging biomarker. J Neurooncol 96:423–431PubMedCrossRefGoogle Scholar
  7. 7.
    Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168:497–505PubMedGoogle Scholar
  8. 8.
    Padhani AR, Liu G, Koh DM et al (2009) Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11:102–125PubMedGoogle Scholar
  9. 9.
    Patterson DM, Padhani AR, Collins DJ (2008) Technology insight: water diffusion MRI—a potential new biomarker of response to cancer therapy. Nat Clin Pract Oncol 5:220–233PubMedCrossRefGoogle Scholar
  10. 10.
    Thoeny HC, De Keyzer F, Vandecaveye V et al (2005) Effect of vascular targeting agent in rat tumor model: dynamic contrast-enhanced versus diffusion-weighted MR imaging. Radiology 237:492–499PubMedCrossRefGoogle Scholar
  11. 11.
    Hamstra DA, Rehemtulla A, Ross BD (2007) Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. J Clin Oncol 25:4104–4109PubMedCrossRefGoogle Scholar
  12. 12.
    Paldino MJ, Barboriak D, Desjardins A, Friedman HS, Vredenburgh JJ (2009) Repeatability of quantitative parameters derived from diffusion tensor imaging in patients with glioblastoma multiforme. J Magn Reson Imaging 29:1199–1205PubMedCrossRefGoogle Scholar
  13. 13.
    Pope WB, Kim HJ, Huo J et al (2009) Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 252:182–189PubMedCrossRefGoogle Scholar
  14. 14.
    Jain R, Scarpace LM, Ellika S et al (2009) Imaging response criteria for recurrent gliomas treated with bevacizumab: role of diffusion weighted imaging as an imaging biomarker. J Neurooncol 96(3):423–431PubMedCrossRefGoogle Scholar
  15. 15.
    Guzman R, Altrichter S, El-Koussy M et al (2008) Contribution of the apparent diffusion coefficient in perilesional edema for the assessment of brain tumors. J Neuroradiol 35:224–229PubMedCrossRefGoogle Scholar
  16. 16.
    Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996PubMedCrossRefGoogle Scholar
  17. 17.
    Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128PubMedCrossRefGoogle Scholar
  18. 18.
    Dempsey MF, Condon BR, Hadley DM (2005) Measurement of tumor "size" in recurrent malignant glioma: 1D, 2D, or 3D? AJNR Am J Neuroradiol 26:770–776PubMedGoogle Scholar
  19. 19.
    de Groot JF, Fuller G, Kumar AJ et al (2010) Tumor invasion after treatment of glioblastoma with bevacizumab: radiographic and pathologic correlation in humans and mice. Neuro Oncol 12:233–242PubMedGoogle Scholar
  20. 20.
    Pieper S LB, Schroeder W, Kikinis R (2006) The NA-MIC kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community. Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro 1:698–701Google Scholar
  21. 21.
    Pieper S LB, Schroeder W, Kikinis R (2010)
  22. 22.
    Ballman KV, Buckner JC, Brown PD et al (2007) The relationship between six-month progression-free survival and 12-month overall survival end points for phase II trials in patients with glioblastoma multiforme. Neuro Oncol 9:29–38PubMedCrossRefGoogle Scholar
  23. 23.
    Sinha G (2008) Expensive cancer drugs with modest benefit ignite debate over solutions. J Natl Cancer Inst 100:1347–1349PubMedCrossRefGoogle Scholar
  24. 24.
    Karapetis CS, Khambata-Ford S, Jonker DJ et al (2008) K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 359:1757–1765PubMedCrossRefGoogle Scholar
  25. 25.
    Oldenhuis CN, Oosting SF, Gietema JA, de Vries EG (2008) Prognostic versus predictive value of biomarkers in oncology. Eur J Cancer 44:946–953PubMedCrossRefGoogle Scholar
  26. 26.
    Miller JC, Pien HH, Sahani D, Sorensen AG, Thrall JH (2005) Imaging angiogenesis: applications and potential for drug development. J Natl Cancer Inst 97:172–187PubMedCrossRefGoogle Scholar
  27. 27.
    Pope WB, Lai A, Nghiemphu P, Mischel P, Cloughesy TF (2006) MRI in patients with high-grade gliomas treated with bevacizumab and chemotherapy. Neurology 66:1258–1260PubMedCrossRefGoogle Scholar
  28. 28.
    Vredenburgh JJ, Desjardins A, Herndon JE 2nd et al (2007) Phase II trial of bevacizumab and irinotecan in recurrent malignant glioma. Clin Cancer Res 13:1253–1259PubMedCrossRefGoogle Scholar
  29. 29.
    Batchelor TT, Sorensen AG, di Tomaso E et al (2007) AZD2171, a pan-VEGF receptor tyrosine kinase inhibitor, normalizes tumor vasculature and alleviates edema in glioblastoma patients. Cancer Cell 11:83–95PubMedCrossRefGoogle Scholar
  30. 30.
    Jain RK, Duda DG, Clark JW, Loeffler JS (2006) Lessons from phase III clinical trials on anti-VEGF therapy for cancer. Nat Clin Pract Oncol 3:24–40PubMedCrossRefGoogle Scholar
  31. 31.
    Park JW, Kerbel RS, Kelloff GJ et al (2004) Rationale for biomarkers and surrogate end points in mechanism-driven oncology drug development. Clin Cancer Res 10:3885–3896PubMedCrossRefGoogle Scholar
  32. 32.
    Thoeny HC, Ross BD (2010) Predicting and monitoring cancer treatment response with diffusion-weighted MRI. J Magn Reson Imaging 32:2–16PubMedCrossRefGoogle Scholar
  33. 33.
    Lee KC, Hall DE, Hoff BA et al (2006) Dynamic imaging of emerging resistance during cancer therapy. Cancer Res 66:4687–4692PubMedCrossRefGoogle Scholar
  34. 34.
    Kono K, Inoue Y, Nakayama K et al (2001) The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol 22:1081–1088PubMedGoogle Scholar
  35. 35.
    Chenevert TL, Stegman LD, Taylor JM et al (2000) Diffusion magnetic resonance imaging: an early surrogate marker of therapeutic efficacy in brain tumors. J Natl Cancer Inst 92:2029–2036PubMedCrossRefGoogle Scholar
  36. 36.
    Yoshikawa MI, Ohsumi S, Sugata S et al (2008) Relation between cancer cellularity and apparent diffusion coefficient values using diffusion-weighted magnetic resonance imaging in breast cancer. Radiat Med 26:222–226PubMedCrossRefGoogle Scholar
  37. 37.
    Guo Y, Cai YQ, Cai ZL et al (2002) Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging 16:172–178PubMedCrossRefGoogle Scholar
  38. 38.
    Squillaci E, Manenti G, Cova M et al (2004) Correlation of diffusion-weighted MR imaging with cellularity of renal tumours. Anticancer Res 24:4175–4179PubMedGoogle Scholar
  39. 39.
    Manenti G, Di Roma M, Mancino S et al (2008) Malignant renal neoplasms: correlation between ADC values and cellularity in diffusion weighted magnetic resonance imaging at 3 T. Radiol Med 113:199–213PubMedCrossRefGoogle Scholar
  40. 40.
    Hayashida Y, Yakushiji T, Awai K et al (2006) Monitoring therapeutic responses of primary bone tumors by diffusion-weighted image: initial results. Eur Radiol 16:2637–2643PubMedCrossRefGoogle Scholar
  41. 41.
    Higano S, Yun X, Kumabe T et al (2006) Malignant astrocytic tumors: clinical importance of apparent diffusion coefficient in prediction of grade and prognosis. Radiology 241:839–846PubMedCrossRefGoogle Scholar
  42. 42.
    Murakami R, Sugahara T, Nakamura H et al (2007) Malignant supratentorial astrocytoma treated with postoperative radiation therapy: prognostic value of pretreatment quantitative diffusion-weighted MR imaging. Radiology 243:493–499PubMedCrossRefGoogle Scholar
  43. 43.
    Reddy JS, Mishra AM, Behari S et al (2006) The role of diffusion-weighted imaging in the differential diagnosis of intracranial cystic mass lesions: a report of 147 lesions. Surg Neurol 66:246–250, discussion 250–241PubMedCrossRefGoogle Scholar
  44. 44.
    Gruber SK ML, Medabalmi P, Gruber DB, Golfinos J, Parker E, Narayana A (2010) Change in pattern of relapse in newly diagnosed high-grade glioma following bevacizumab therapy. J Clin Oncol 28:15s (suppl; abstr 2020)Google Scholar
  45. 45.
    Norden AD, Young GS, Setayesh K et al (2008) Bevacizumab for recurrent malignant gliomas: efficacy, toxicity, and patterns of recurrence. Neurology 70:779–787PubMedCrossRefGoogle Scholar
  46. 46.
    Rubenstein JL, Kim J, Ozawa T et al (2000) Anti-VEGF antibody treatment of glioblastoma prolongs survival but results in increased vascular cooption. Neoplasia 2:306–314PubMedCrossRefGoogle Scholar
  47. 47.
    Sorensen AG, Batchelor TT, Wen PY, Zhang WT, Jain RK (2008) Response criteria for glioma. Nat Clin Pract Oncol 5:634–644PubMedCrossRefGoogle Scholar
  48. 48.
    Chen W, Silverman DH (2008) Advances in evaluation of primary brain tumors. Semin Nucl Med 38:240–250PubMedCrossRefGoogle Scholar
  49. 49.

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Martha Nowosielski
    • 1
  • Wolfgang Recheis
    • 2
  • Georg Goebel
    • 3
  • Özgür Güler
    • 4
  • Gerd Tinkhauser
    • 1
  • Herwig Kostron
    • 5
  • Michael Schocke
    • 2
  • Thaddaeus Gotwald
    • 2
  • Günther Stockhammer
    • 1
  • Markus Hutterer
    • 1
    • 6
  1. 1.Department of NeurologyInnsbruck Medical UniversityInnsbruckAustria
  2. 2.Department of RadiologyInnsbruck Medical UniversityInnsbruckAustria
  3. 3.Department of Medical Statistics, Informatics and Health EconomicsInnsbruck Medical UniversityInnsbruckAustria
  4. 4.4D Visualization Laboratory, University Clinic of Oto-, Rhino- and LaryngologyInnsbruck Medical UniversityInnsbruckAustria
  5. 5.Department of NeurosurgeryInnsbruck Medical UniversityInnsbruckAustria
  6. 6.Department of NeurologyParacelsus Medical University Salzburg–Christian Doppler HospitalSalzburgAustria

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