Skip to main content

Advertisement

SpringerLink
Log in

Differentiation between treatment-related changes and progressive disease in patients with high grade brain tumors using support vector machine classification based on DCE MRI

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

Abstract

Differentiation between treatment-related changes and progressive disease (PD) remains a major clinical challenge in the follow-up of patients with high grade brain tumors. The aim of this study was to differentiate between treatment-related changes and PD using dynamic contrast enhanced (DCE) MRI. Twenty patients were scanned using conventional, DCE-MRI and MR spectroscopy (total of 44 MR scans). The enhanced lesion area was extracted using independent components analysis of the DCE data. Pharmacokinetic parameters were estimated from the DCE data based on the Extended-Tofts-Model. Voxel based classification for treatment-related changes versus PD was performed in a patient-wise leave-one-out manner, using a support vector machine classifier. DCE parameters, K trans, v e, k ep and v p, significantly differentiated between the tissue types. Classification results were validated using spectroscopy data showing significantly higher choline/creatine values in the extracted PD component compared to areas with treatment-related changes and normal appearing white matter, and high correlation between choline/creatine values and the percentage of the identified PD component within the lesion area (r = 0.77, p < 0.001). On the training data the sensitivity and specificity were 98 and 97 %, respectively, for the treatment-related changes component and 97 and 98 % for the PD component. This study proposes a methodology based on DCE-MRI to differentiate lesion areas into treatment-related changes versus PD, prospectively in each scan. Results may have major clinical importance for pre-operative planning, guidance for targeting biopsy, and early prediction of radiological outcomes in patients with high grade brain tumors.

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. 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 28:1963–1972. doi:10.1200/JCO.2009.26.3541

    Article  PubMed  Google Scholar 

  2. Ruben JD, Dally M, Bailey M, Smith R, McLean CA, Fedele P (2006) Cerebral radiation necrosis: incidence, outcomes, and risk factors with emphasis on radiation parameters and chemotherapy. Int J Radiat Oncol Biol Phys 65:499–508. doi:10.1016/j.ijrobp.2005.12.002

    Article  PubMed  Google Scholar 

  3. Kumar AJ, Leeds NE, Fuller GN, Van Tassel P, Maor MH, Sawaya RE, Levin VA (2000) Malignant gliomas: MR imaging spectrum of radiation therapy- and chemotherapy-induced necrosis of the brain after treatment. Radiology 217:377–384. doi:10.1148/radiology.217.2.r00nv36377

    Article  CAS  PubMed  Google Scholar 

  4. Ulmer S, Braga TA, Barker FG 2nd, Lev MH, Gonzalez RG, Henson JW (2006) Clinical and radiographic features of peritumoral infarction following resection of glioblastoma. Neurology 67:1668–1670. doi:10.1212/01.wnl.0000242894.21705.3c

    Article  CAS  PubMed  Google Scholar 

  5. Wong CS, Van der Kogel AJ (2004) Mechanisms of radiation injury to the central nervous system: implications for neuroprotection. Mol Interv 4:273–284. doi:10.1124/mi.4.5.7

    Article  CAS  PubMed  Google Scholar 

  6. Lyubimova N, Hopewell JW (2004) Experimental evidence to support the hypothesis that damage to vascular endothelium plays the primary role in the development of late radiation-induced CNS injury. Br J Radiol 77:488–492

    Article  CAS  PubMed  Google Scholar 

  7. Verma N, Cowperthwaite MC, Burnett MG, Markey MK (2013) Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. Neuro Oncol 15:515–534. doi:10.1093/neuonc/nos307

    Article  PubMed  PubMed Central  Google Scholar 

  8. Brandes AA, Tosoni A, Spagnolli F, Frezza G, Leonardi M, Calbucci F, Franceschi E (2008) Disease progression or pseudoprogression after concomitant radiochemotherapy treatment: pitfalls in neurooncology. Neuro Oncol 10:361–367. doi:10.1215/15228517-2008-008

    Article  PubMed  PubMed Central  Google Scholar 

  9. Pope WB, Young JR, Ellingson BM (2011) Advances in MRI assessment of gliomas and response to anti-VEGF therapy. Curr Neurol Neurosci Rep 11:336–344. doi:10.1007/s11910-011-0179-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. 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 253:486–496. doi:10.1148/radiol.2532090007

    Article  PubMed  PubMed Central  Google Scholar 

  11. 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. AJNR Am J Neuroradiol 30:552–558. doi:10.3174/ajnr.A1377

    Article  CAS  PubMed  Google Scholar 

  12. Bobek-Billewicz B, Stasik-Pres G, Majchrzak H, Zarudzki L (2010) Differentiation between brain tumor recurrence and radiation injury using perfusion, diffusion-weighted imaging and MR spectroscopy. Folia Neuropathol 48:81–92

    PubMed  Google Scholar 

  13. Kim J, Leira EC, Callison RC, Ludwig B, Moritani T, Magnotta VA, Madsen MT (2010) Toward fully automated processing of dynamic susceptibility contrast perfusion MRI for acute ischemic cerebral stroke. Comput Methods Programs Biomed 98:204–213

    Article  PubMed  Google Scholar 

  14. Barajas RF, Chang JS, Sneed PK, Segal MR, McDermott MW, Cha S (2009) Distinguishing recurrent intra-axial metastatic tumor from radiation necrosis following gamma knife radiosurgery using dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. AJNR Am J Neuroradiol 30:367–372. doi:10.3174/ajnr.A1362

    Article  CAS  PubMed  Google Scholar 

  15. Sourbron SP, Buckley DL (2013) Classic models for dynamic contrast-enhanced MRI. NMR Biomed 26:1004–1027. doi:10.1002/nbm.2940

    Article  PubMed  Google Scholar 

  16. Tofts PS, Kermode AG (1991) Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn Reson Med 17:357–367

    Article  CAS  PubMed  Google Scholar 

  17. Liberman G, Nadav G, Louzoun Y, Artzi M, Ben Bashat D (2014) Bolus arrival time extraction using super temporal resolution analysis of DCE. International Society for Magnetic Resonance in Medicine, Milan

    Google Scholar 

  18. Bisdas S, Naegele T, Ritz R, Dimostheni A, Pfannenberg C, Reimold M, Koh TS, Ernemann U (2011) Distinguishing recurrent high-grade gliomas from radiation injury: a pilot study using dynamic contrast-enhanced MR imaging. Acad Radiol 18:575–583. doi:10.1016/j.acra.2011.01.018

    Article  PubMed  Google Scholar 

  19. Larsen VA, Simonsen HJ, Law I, Larsson HB, Hansen AE (2013) Evaluation of dynamic contrast-enhanced T1-weighted perfusion MRI in the differentiation of tumor recurrence from radiation necrosis. Neuroradiology 55:361–369. doi:10.1007/s00234-012-1127-4

    Article  PubMed  Google Scholar 

  20. Narang J, Jain R, Arbab AS, Mikkelsen T, Scarpace L, Rosenblum ML, Hearshen D, Babajani-Feremi A (2011) Differentiating treatment-induced necrosis from recurrent/progressive brain tumor using nonmodel-based semiquantitative indices derived from dynamic contrast-enhanced T1-weighted MR perfusion. Neuro Oncol 13:1037–1046. doi:10.1093/neuonc/nor075

    Article  PubMed  PubMed Central  Google Scholar 

  21. Deoni SC, Peters TM, Rutt BK (2004) High-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magn Reson Med 53:237–241

    Article  Google Scholar 

  22. Liberman G, Louzoun Y, Ben Bashat D (2013) T1 mapping using variable flip angle SPGR data with flip angle correction. J Magn Reson Imaging 40:171–180

    Article  PubMed  Google Scholar 

  23. Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5:143–156

    Article  CAS  PubMed  Google Scholar 

  24. Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155. doi:10.1002/hbm.10062

    Article  PubMed  Google Scholar 

  25. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. Med Imaging IEEE Trans 17:87–97

    Article  CAS  Google Scholar 

  26. Deoni SC, Peters TM, Rutt BK (2005) High-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magn Reson Med 53:237–241

    Article  PubMed  Google Scholar 

  27. Murase K (2004) Efficient method for calculating kinetic parameters using T1-weighted dynamic contrast-enhanced magnetic resonance imaging. Magn Reson Med 51:858–862. doi:10.1002/mrm.20022

    Article  PubMed  Google Scholar 

  28. Bagher-Ebadian H, Jain R, Nejad-Davarani SP, Mikkelsen T, Lu M, Jiang Q, Scarpace L, Arbab AS, Narang J, Soltanian-Zadeh H, Paudyal R, Ewing JR (2012) Model selection for DCE-T1 studies in glioblastoma. Magn Reson Med 68:241–251. doi:10.1002/mrm.23211

    Article  PubMed  PubMed Central  Google Scholar 

  29. Beckmann CF, Smith SM (2004) Probabilistic independent component analysis for functional magnetic resonance imaging. Med Imaging IEEE Trans 23:137–152

    Article  Google Scholar 

  30. Provencher SW (1993) Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 30:672–679

    Article  CAS  PubMed  Google Scholar 

  31. Hopewell JW, Calvo W, Jaenke R, Reinhold HS, Robbins ME, Whitehouse EM (1993) Microvasculature and radiation damage. Recent Results Cancer Res 130:1–16

    Article  CAS  PubMed  Google Scholar 

  32. Mehrabian H, Chopra R, Martel AL (2013) Calculation of intravascular signal in dynamic contrast enhanced-MRI using adaptive complex independent component analysis. IEEE Trans Med Imaging 32:699–710. doi:10.1109/TMI.2012.2233747

    Article  PubMed  Google Scholar 

  33. Koh TS, Thng CH, Ho JT, Tan PH, Rumpel H, Khoo JB (2008) Independent component analysis of dynamic contrast-enhanced magnetic resonance images of breast carcinoma: a feasibility study. J Magn Reson Imaging 28:271–277. doi:10.1002/jmri.21391

    Article  PubMed  Google Scholar 

  34. Preul MC, Leblanc R, Caramanos Z, Kasrai R, Narayanan S, Arnold DL (1998) Magnetic resonance spectroscopy guided brain tumor resection: differentiation between recurrent glioma and radiation change in two diagnostically difficult cases. Can J Neurol Sci 25:13–22

    Article  CAS  PubMed  Google Scholar 

  35. Chernov MF, Hayashi M, Izawa M, Usukura M, Yoshida S, Ono Y, Muragaki Y, Kubo O, Hori T, Takakura K (2006) Multivoxel proton MRS for differentiation of radiation-induced necrosis and tumor recurrence after gamma knife radiosurgery for brain metastases. Brain Tumor Pathol 23:19–27. doi:10.1007/s10014-006-0194-9

    Article  CAS  PubMed  Google Scholar 

  36. Weber MA, Giesel FL, Stieltjes B (2008) MRI for identification of progression in brain tumors: from morphology to function. Expert Rev Neurother 8:1507–1525. doi:10.1586/14737175.8.10.1507

    Article  PubMed  Google Scholar 

  37. Sundgren PC (2009) MR spectroscopy in radiation injury. AJNR Am J Neuroradiol 30:1469–1476. doi:10.3174/ajnr.A1580

    Article  CAS  PubMed  Google Scholar 

  38. Delrue LJ, Casneuf V, Van Damme N, Blanckaert P, Peeters M, Ceelen WP, Duyck PC (2011) Assessment of neovascular permeability in a pancreatic tumor model using dynamic contrast-enhanced (DCE) MRI with contrast agents of different molecular weights. Magma 24:225–232. doi:10.1007/s10334-011-0256-9

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

To Vicki Myers for editorial assistance and Faina Vitinshtein for assistance in patient recruitment and MRI scans.

Author information

Authors and Affiliations

  1. Functional Brain Center, The Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, 6 Weizman St., 64239, Tel Aviv, Israel

    Moran Artzi, Gilad Liberman, Guy Nadav, Orna Aizenstein & Dafna Ben Bashat

  2. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel

    Moran Artzi & Dafna Ben Bashat

  3. Department of Chemical Physics, Weizmann Institute, Rehovot, Israel

    Gilad Liberman

  4. Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel

    Guy Nadav

  5. Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

    Deborah T. Blumenthal & Felix Bokstein

  6. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel

    Dafna Ben Bashat

Authors
  1. Moran Artzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gilad Liberman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guy Nadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Deborah T. Blumenthal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Felix Bokstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Orna Aizenstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dafna Ben Bashat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dafna Ben Bashat.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Artzi, M., Liberman, G., Nadav, G. et al. Differentiation between treatment-related changes and progressive disease in patients with high grade brain tumors using support vector machine classification based on DCE MRI. J Neurooncol 127, 515–524 (2016). https://doi.org/10.1007/s11060-016-2055-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11060-016-2055-7

Keywords

Search

Navigation