Skip to main content

Advertisement

SpringerLink
Log in

Multimodal MR imaging model to predict tumor infiltration in patients with gliomas

  • Diagnostic Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Introduction

Gliomas remain difficult to treat, in part, due to our inability to accurately delineate the margins of the tumor. The goal of our study was to evaluate if a combination of advanced MR imaging techniques and a multimodal imaging model could be used to predict tumor infiltration in patients with diffuse gliomas.

Methods

Institutional review board approval and written consent were obtained. This prospective pilot study enrolled patients undergoing stereotactic biopsy for a suspected de novo glioma. Stereotactic biopsy coordinates were coregistered with multiple standard and advanced neuroimaging sequences in 10 patients. Objective imaging values were assigned to the biopsy sites for each of the imaging sequences. A principal component analysis was performed to reduce the dimensionality of the imaging dataset without losing important information. A univariate analysis was performed to identify the statistically relevant principal components. Finally, a multivariate analysis was used to build the final model describing nuclear density.

Results

A univariate analysis identified three principal components as being linearly associated with the observed nuclear density (p values 0.021, 0.016, and 0.046, respectively). These three principal component composite scores are predominantly comprised of DTI (mean diffusivity or average diffusion coefficient and fractional anisotropy) and PWI data (rMTT, Ktrans). The p value of the model was <0.001. The correlation between the predicted and observed nuclear density was 0.75.

Conclusion

A multi-input, single output imaging model may predict the extent of glioma invasion with significant correlation with histopathology.

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

Similar content being viewed by others

References

  1. Deorah S, Lynch CF, Sibenaller ZA, Ryken TC (2006) Trends in brain cancer incidence and survival in the United States: surveillance, epidemiology, and end results program, 1973 to 2001. Neurosurg Focus 20(4):E1. doi:10.3171/foc.2006.20.4.E1

    Article  PubMed  Google Scholar 

  2. Lacroix M, Abi-Said D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, Lang FF, McCutcheon IE, Hassenbusch SJ, Holland E, Hess K, Michael C, Miller D, Sawaya R (2001) A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 95(2):190–198. doi:10.3171/jns.2001.95.2.0190

    Article  CAS  PubMed  Google Scholar 

  3. Sanai N, Berger MS (2008) Glioma extent of resection and its impact on patient outcome. Neurosurgery 62(4):753–764. doi:10.1227/01.neu.0000318159.21731.cf, discussion 264-756

    Article  PubMed  Google Scholar 

  4. Wallner KE, Galicich JH, Krol G, Arbit E, Malkin MG (1989) Patterns of failure following treatment for glioblastoma multiforme and anaplastic astrocytoma. Int J Radiat Oncol Biol Phys 16(6):1405–1409

    Article  CAS  PubMed  Google Scholar 

  5. Law M, Yang S, Babb JS, Knopp EA, Golfinos JG, Zagzag D, Johnson G (2004) Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 25(5):746–755

    PubMed  Google Scholar 

  6. Price SJ, Jena R, Burnet NG, Carpenter TA, Pickard JD, Gillard JH (2007) Predicting patterns of glioma recurrence using diffusion tensor imaging. Eur Radiol 17(7):1675–1684. doi:10.1007/s00330-006-0561-2

    Article  PubMed  Google Scholar 

  7. Provenzale JM, McGraw P, Mhatre P, Guo AC, Delong D (2004) Peritumoral brain regions in gliomas and meningiomas: investigation with isotropic diffusion-weighted MR imaging and diffusion-tensor MR imaging. Radiology 232(2):451–460. doi:10.1148/radiol.2322030959

    Article  PubMed  Google Scholar 

  8. Barajas RF Jr, Phillips JJ, Parvataneni R, Molinaro A, Essock-Burns E, Bourne G, Parsa AT, Aghi MK, McDermott MW, Berger MS, Cha S, Chang SM, Nelson SJ (2012) Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. Neuro Oncol 14(7):942–954. doi:10.1093/neuonc/nos128

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  9. Kidwell CS, Wintermark M, De Silva DA, Schaewe TJ, Jahan R, Starkman S, Jovin T, Hom J, Jumaa M, Schreier J, Gornbein J, Liebeskind DS, Alger JR, Saver JL (2013) Multiparametric MRI and CT models of infarct core and favorable penumbral imaging patterns in acute ischemic stroke. Stroke 44(1):73–79. doi:10.1161/STROKEAHA.112.670034

    Article  PubMed Central  PubMed  Google Scholar 

  10. Law M, Young R, Babb J, Rad M, Sasaki T, Zagzag D, Johnson G (2006) Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 27(9):1975–1982

    CAS  PubMed  Google Scholar 

  11. Cook P, Bai Y, Nedjati-Gilani S, Seunarine K, Hall M, Parker G, Alexander D Camino (2006) Open-Source Diffusion-MRI Reconstruction and Processing. In: 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine, Seattle, WA, USA. p 2759

  12. Barboriak DP (2006) Dynamic susceptibility contrast MR Analysis (DSCoMAn), 1st edn. Duke University, Durham

    Google Scholar 

  13. 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. AJNR Am J Neuroradiol 27(4):859–867

    CAS  PubMed  Google Scholar 

  14. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114(2):97–109. doi:10.1007/s00401-007-0243-4

    Article  PubMed Central  PubMed  Google Scholar 

  15. Boruah D, Deb P (2013) Utility of nuclear morphometry in predicting grades of diffusely infiltrating gliomas. ISRN Oncol 2013:760653. doi:10.1155/2013/760653

    PubMed Central  PubMed  Google Scholar 

  16. Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54(3):2033–2044. doi:10.1016/j.neuroimage.2010.09.025

    Article  PubMed Central  PubMed  Google Scholar 

  17. Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W (2003) PET-CT image registration in the chest using free-form deformations. IEEE Trans Med Imaging 22(1):120–128. doi:10.1109/TMI.2003.809072

    Article  PubMed  Google Scholar 

  18. Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303–304

    Article  PubMed  Google Scholar 

  19. Johnson RA, Wichern DW (2007) Applied Multivariate Statistical Analysis. 6th edn. Pearson

  20. Hardin JW, Hilbe JM (2002) Generalized estimating equations, 1st edn. Chapman and Hall, Boca Raton

    Book  Google Scholar 

  21. Huber PJ (1967) The Behavior of Maximum Likelihood Estimates Under Nonstandard Conditions. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. pp 221–233

  22. White H (1980) A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48(4):817–838

    Article  Google Scholar 

  23. Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, Gerig G (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116–1128. doi:10.1016/j.neuroimage.2006.01.015

    Article  PubMed  Google Scholar 

  24. Chaskis C, Stadnik T, Michotte A, Van Rompaey K, D’Haens J (2006) Prognostic value of perfusion-weighted imaging in brain glioma: a prospective study. Acta Neurochir (Wien) 148(3):277–285. doi:10.1007/s00701-005-0718-9, discussion 285

    Article  CAS  Google Scholar 

  25. Stecco A, Pisani C, Quarta R, Brambilla M, Masini L, Beldi D, Zizzari S, Fossaceca R, Krengli M, Carriero A (2011) DTI and PWI analysis of peri-enhancing tumoral brain tissue in patients treated for glioblastoma. J Neurooncol 102(2):261–271. doi:10.1007/s11060-010-0310-x

    Article  PubMed  Google Scholar 

  26. Law M, Oh S, Babb JS, Wang E, Inglese M, Zagzag D, Knopp EA, Johnson G (2006) Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging–prediction of patient clinical response. Radiology 238(2):658–667. doi:10.1148/radiol.2382042180

    Article  PubMed  Google Scholar 

  27. Hakyemez B, Erdogan C, Ercan I, Ergin N, Uysal S, Atahan S (2005) High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol 60(4):493–502. doi:10.1016/j.crad.2004.09.009

    Article  CAS  PubMed  Google Scholar 

  28. Sorensen AG, Batchelor TT, Zhang WT, Chen PJ, Yeo P, Wang M, Jennings D, Wen PY, Lahdenranta J, Ancukiewicz M, di Tomaso E, Duda DG, Jain RK (2009) A “vascular normalization index” as potential mechanistic biomarker to predict survival after a single dose of cediranib in recurrent glioblastoma patients. Cancer Res 69(13):5296–5300. doi:10.1158/0008-5472.CAN-09-0814

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  29. Mills SJ, Patankar TA, Haroon HA, Baleriaux D, Swindell R, Jackson A (2006) Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? AJNR Am J Neuroradiol 27(4):853–858

    CAS  PubMed  Google Scholar 

  30. Price SJ, Jena R, Burnet NG, Hutchinson PJ, Dean AF, Pena A, Pickard JD, Carpenter TA, Gillard JH (2006) Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. AJNR Am J Neuroradiol 27(9):1969–1974

    CAS  PubMed  Google Scholar 

  31. Stadlbauer A, Nimsky C, Buslei R, Salomonowitz E, Hammen T, Buchfelder M, Moser E, Ernst-Stecken A, Ganslandt O (2007) Diffusion tensor imaging and optimized fiber tracking in glioma patients: histopathologic evaluation of tumor-invaded white matter structures. Neuroimage 34(3):949–956. doi:10.1016/j.neuroimage.2006.08.051

    Article  PubMed  Google Scholar 

  32. Lee HY, Na DG, Song IC, Lee DH, Seo HS, Kim JH, Chang KH (2008) Diffusion-tensor imaging for glioma grading at 3-T magnetic resonance imaging: analysis of fractional anisotropy and mean diffusivity. J Comput Assist Tomogr 32(2):298–303. doi:10.1097/RCT.0b013e318076b44d

    Article  PubMed  Google Scholar 

  33. Sanai N, Alvarez-Buylla A, Berger MS (2005) Neural stem cells and the origin of gliomas. N Engl J Med 353(8):811–822. doi:10.1056/NEJMra043666

    Article  CAS  PubMed  Google Scholar 

  34. Coons SW, Johnson PC, Shapiro JR (1995) Cytogenetic and flow cytometry DNA analysis of regional heterogeneity in a low grade human glioma. Cancer Res 55(7):1569–1577

    CAS  PubMed  Google Scholar 

  35. Cha S, Knopp EA, Johnson G, Wetzel SG, Litt AW, Zagzag D (2002) Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. Radiology 223(1):11–29

    Article  PubMed  Google Scholar 

  36. Lupo JM, Cha S, Chang SM, Nelson SJ (2005) Dynamic susceptibility-weighted perfusion imaging of high-grade gliomas: characterization of spatial heterogeneity. AJNR Am J Neuroradiol 26(6):1446–1454

    PubMed  Google Scholar 

  37. Arvinda HR, Kesavadas C, Sarma PS, Thomas B, Radhakrishnan VV, Gupta AK, Kapilamoorthy TR, Nair S (2009) Glioma grading: sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging. J Neurooncol 94(1):87–96. doi:10.1007/s11060-009-9807-6

    Article  CAS  PubMed  Google Scholar 

  38. Emblem KE, Nedregaard B, Nome T, Due-Tonnessen P, Hald JK, Scheie D, Borota OC, Cvancarova M, Bjornerud A (2008) Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. Radiology 247(3):808–817. doi:10.1148/radiol.2473070571

    Article  PubMed  Google Scholar 

  39. Lev MH, Ozsunar Y, Henson JW, Rasheed AA, Barest GD, Harsh GR, Fitzek MM, Chiocca EA, Rabinov JD, Csavoy AN, Rosen BR, Hochberg FH, Schaefer PW, Gonzalez RG (2004) Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol 25(2):214–221

    PubMed  Google Scholar 

  40. Roberts HC, Roberts TP, Ley S, Dillon WP, Brasch RC (2002) Quantitative estimation of microvascular permeability in human brain tumors: correlation of dynamic Gd-DTPA-enhanced MR imaging with histopathologic grading. Acad Radiol 9(Suppl 1):S151–S155

    Article  PubMed  Google Scholar 

  41. Uematsu H, Maeda M, Sadato N, Matsuda T, Ishimori Y, Koshimoto Y, Yamada H, Kimura H, Kawamura Y, Hayashi N, Yonekura Y, Ishii Y (2000) Vascular permeability: quantitative measurement with double-echo dynamic MR imaging—theory and clinical application. Radiology 214(3):912–917

    Article  CAS  PubMed  Google Scholar 

  42. Patankar TF, Haroon HA, Mills SJ, Baleriaux D, Buckley DL, Parker GJ, Jackson A (2005) Is volume transfer coefficient (K(trans)) related to histologic grade in human gliomas? AJNR Am J Neuroradiol 26(10):2455–2465

    PubMed  Google Scholar 

  43. Zhang N, Zhang L, Qiu B, Meng L, Wang X, Hou BL (2012) Correlation of volume transfer coefficient Ktrans with histopathologic grades of gliomas. J Magn Reson Imaging 36(2):355–363. doi:10.1002/jmri.23675

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  44. Bulakbasi N, Guvenc I, Onguru O, Erdogan E, Tayfun C, Ucoz T (2004) The added value of the apparent diffusion coefficient calculation to magnetic resonance imaging in the differentiation and grading of malignant brain tumors. J Comput Assist Tomogr 28(6):735–746

    Article  PubMed  Google Scholar 

  45. Kono K, Inoue Y, Nakayama K, Shakudo M, Morino M, Ohata K, Wakasa K, Yamada R (2001) The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol 22(6):1081–1088

    CAS  PubMed  Google Scholar 

  46. Guo AC, Cummings TJ, Dash RC, Provenzale JM (2002) Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology 224(1):177–183

    Article  PubMed  Google Scholar 

  47. Sadeghi N, D’Haene N, Decaestecker C, Levivier M, Metens T, Maris C, Wikler D, Baleriaux D, Salmon I, Goldman S (2008) Apparent diffusion coefficient and cerebral blood volume in brain gliomas: relation to tumor cell density and tumor microvessel density based on stereotactic biopsies. AJNR Am J Neuroradiol 29(3):476–482. doi:10.3174/ajnr.A0851

    Article  CAS  PubMed  Google Scholar 

  48. Tien RD, Felsberg GJ, Friedman H, Brown M, MacFall J (1994) MR imaging of high-grade cerebral gliomas: value of diffusion-weighted echoplanar pulse sequences. AJR Am J Roentgenol 162(3):671–677

    Article  CAS  PubMed  Google Scholar 

  49. Fan GG, Deng QL, Wu ZH, Guo QY (2006) Usefulness of diffusion/perfusion-weighted MRI in patients with non-enhancing supratentorial brain gliomas: a valuable tool to predict tumour grading? Br J Radiol 79(944):652–658. doi:10.1259/bjr/25349497

    Article  CAS  PubMed  Google Scholar 

  50. Stadlbauer A, Ganslandt O, Buslei R, Hammen T, Gruber S, Moser E, Buchfelder M, Salomonowitz E, Nimsky C (2006) Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. Radiology 240(3):803–810. doi:10.1148/radiol.2403050937

    Article  PubMed  Google Scholar 

  51. Cruz LC Jr, Sorensen AG (2006) Diffusion tensor magnetic resonance imaging of brain tumors. Magn Reson Imaging Clin N Am 14(2):183–202. doi:10.1016/j.mric.2006.06.003

    Article  PubMed  Google Scholar 

  52. Yoshikawa K, Kajiwara K, Morioka J, Fujii M, Tanaka N, Fujisawa H, Kato S, Nomura SM (2006) Improvement of functional outcome after radical surgery in glioblastoma patients: the efficacy of a navigation-guided fence-post procedure and neurophysiological monitoring. J Neurooncol 78(1):91–97. doi:10.1007/s11060-005-9064-2

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

We would like to acknowledge our MRI technicians for their hard work, including Jaime Weathersbee, Brian Burkholder, Dean Baugher, and Thomas Huerta. Additionally, we would like to acknowledge David Beech from the OR for providing all surgical coordinates.

Conflict of interest

We declare that we have no conflict of interest.

Author information

Authors and Affiliations

  1. University of Virginia Health Systems, Division of Neuroradiology, Department of Radiology and Medical Imaging, PO Box 800170, FedEx:1215 Lee Street-New Hospital, Charlottesville, VA, 22908, USA

    Christopher R. Durst, Nicholas J. Tustison & Max Wintermark

  2. Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, USA

    Prashant Raghavan

  3. Department of Neurosurgery, University of Virginia, Charlottesville, VA, USA

    Mark E. Shaffrey, M. Beatriz Lopes, Jason P. Sheehan, W. Jeff Elias, Kenneth C. Liu & Greg A. Helm

  4. Department of Neurology, University of Virginia, Charlottesville, VA, USA

    David Schiff

  5. Department of Pathology (Neuropathology), University of Virginia, Charlottesville, VA, USA

    M. Beatriz Lopes

  6. Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA

    James T. Patrie & Wenjun Xin

  7. School of Public Health, Loma Linda University, Loma Linda, CA, USA

    A. Cupino

Authors
  1. Christopher R. Durst
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prashant Raghavan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark E. Shaffrey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. David Schiff
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M. Beatriz Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jason P. Sheehan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nicholas J. Tustison
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. James T. Patrie
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wenjun Xin
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. W. Jeff Elias
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Kenneth C. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Greg A. Helm
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. A. Cupino
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Max Wintermark
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher R. Durst.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 149 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Durst, C.R., Raghavan, P., Shaffrey, M.E. et al. Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology 56, 107–115 (2014). https://doi.org/10.1007/s00234-013-1308-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00234-013-1308-9

Keywords

Search

Navigation