Journal of Neuro-Oncology

, Volume 125, Issue 2, pp 393–400 | Cite as

Location of brain tumor intersecting white matter tracts predicts patient prognosis

  • Nikolai J. Mickevicius
  • Alexander B. Carle
  • Trevor Bluemel
  • Stephanie Santarriaga
  • Fallon Schloemer
  • Derrick Shumate
  • Jennifer Connelly
  • Kathleen M. Schmainda
  • Peter S. LaViolette
Clinical Study


Brain tumor cells invade adjacent normal brain along white matter (WM) bundles of axons. We therefore hypothesized that the location of tumor intersecting WM tracts would be associated with differing survival. This study introduces a method, voxel-wise survival analysis (VSA), to determine the relationship between the location of brain tumor intersecting WM tracts and patient prognosis. 113 primary glioblastoma (GBM) patients were retrospectively analyzed for this study. Patient specific tumor location, defined by contrast-enhancement, was combined with diffusion tensor imaging derived tractography to determine the location of axons intersecting tumor enhancement (AXITEs). VSA was then used to determine the relationship between the AXITE location and patient survival. Tumors intersecting the right anterior thalamic radiation (ATR), right inferior fronto-occipital fasciculus (IFOF), right and left cortico-spinal tract (CST), and corpus callosum (CC) were associated with decreased overall survival. Tumors intersecting the CST, body of the CC, right ATR, posterior IFOF, and inferior longitudinal fasciculus are associated with decreased progression-free survival (PFS), while tumors intersecting the right genu of the CC and anterior IFOF are associated with increased PFS. Patients with tumors intersecting the ATR, IFOF, CST, or CC had significantly improved survival prognosis if they were additionally treated with bevacizumab. This study demonstrates the usefulness of VSA by locating AXITEs associated with poor prognosis in GBM patients. This information should be included in patient-physician conversations, therapeutic strategy, and clinical trial design.


Brain tumor Glioblastoma Diffusion White matter tracts 



Voxel-wise survival analysis


Axons intersecting tumor enhancement


White matter


Overall survival


Progression free survival


Anterior thalamic radiation


Inferior fronto-occipital fasciculus


Cortico-spinal tract


Corpus callosum



Advancing a Healthier Wisconsin, and the MCW Cancer Center.

Compliance with ethical standards

Conflicts of Interest


Supplementary material

11060_2015_1928_MOESM1_ESM.docx (4.5 mb)
Supplementary material 1 (docx 4604 kb)


  1. 1.
    Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987–996CrossRefPubMedGoogle Scholar
  2. 2.
    Ewelt C, Goeppert M, Rapp M, Steiger HJ, Stummer W, Sabel M (2011) Glioblastoma multiforme of the elderly: the prognostic effect of resection on survival. J Neurooncol 103(3):611–618CrossRefPubMedGoogle Scholar
  3. 3.
    Filippini G, Falcone C, Boiardi A et al (2008) Prognostic factors for survival in 676 consecutive patients with newly diagnosed primary glioblastoma. Neuro Oncol 10(1):79–87PubMedCentralCrossRefPubMedGoogle Scholar
  4. 4.
    Jeremic B, Milicic B, Grujicic D, Dagovic A, Aleksandrovic J (2003) Multivariate analysis of clinical prognostic factors in patients with glioblastoma multiforme treated with a combined modality approach. J Cancer Res Clin Oncol 129(8):477–484CrossRefPubMedGoogle Scholar
  5. 5.
    Li SW, Qiu XG, Chen BS et al (2009) Prognostic factors influencing clinical outcomes of glioblastoma multiforme. Chin Med J (Engl) 122(11):1245–1249Google Scholar
  6. 6.
    Fontaine D, Paquis P (2010) Glioblastoma: clinical, radiological and biological prognostic factors. Neurochirurgie 56(6):467–476CrossRefPubMedGoogle Scholar
  7. 7.
    Lacroix M, Abi-Said D, Fourney DR et al (2001) A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 95(2):190–198CrossRefPubMedGoogle Scholar
  8. 8.
    Simpson JR, Horton J, Scott C et al (1993) Influence of location and extent of surgical resection on survival of patients with glioblastoma multiforme: results of three consecutive Radiation Therapy Oncology Group (RTOG) clinical trials. Int J Radiat Oncol Biol Phys 26(2):239–244CrossRefPubMedGoogle Scholar
  9. 9.
    Ellingson BM, Lai A, Harris RJ et al (2013) Probabilistic radiographic atlas of glioblastoma phenotypes. AJNR Am J Neuroradiol 34(3):533–540CrossRefPubMedGoogle Scholar
  10. 10.
    Hammoud MA, Sawaya R, Shi W, Thall PF, Leeds NE (1996) Prognostic significance of preoperative MRI scans in glioblastoma multiforme. J Neurooncol 27(1):65–73CrossRefPubMedGoogle Scholar
  11. 11.
    Ellingson BM, Cloughesy TF, Pope WB et al (2012) Anatomic localization of O6-methylguanine DNA methyltransferase (MGMT) promoter methylated and unmethylated tumors: a radiographic study in 358 de novo human glioblastomas. Neuroimage 59(2):908–916CrossRefPubMedGoogle Scholar
  12. 12.
    Pedersen PH, Edvardsen K, Garcia-Cabrera I et al (1995) Migratory patterns of lac-z transfected human glioma cells in the rat brain. Int J Cancer 62(6):767–771CrossRefPubMedGoogle Scholar
  13. 13.
    Belien AT, Paganetti PA, Schwab ME (1999) Membrane-type 1 matrix metalloprotease (MT1-MMP) enables invasive migration of glioma cells in central nervous system white matter. J Cell Biol 144(2):373–384PubMedCentralCrossRefPubMedGoogle Scholar
  14. 14.
    Lefranc F, Brotchi J, Kiss R (2005) Possible future issues in the treatment of glioblastomas: special emphasis on cell migration and the resistance of migrating glioblastoma cells to apoptosis. J Clin Oncol 23(10):2411–2422CrossRefPubMedGoogle Scholar
  15. 15.
    Baldock AL, Ahn S, Rockne R et al (2014) Patient-specific metrics of invasiveness reveal significant prognostic benefit of resection in a predictable subset of gliomas. PLoS ONE 9(10):e99057PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Ellingson BM, Cloughesy TF, Lai A, Nghiemphu PL, Pope WB (2011) Cell invasion, motility, and proliferation level estimate (CIMPLE) maps derived from serial diffusion MR images in recurrent glioblastoma treated with bevacizumab. J Neurooncol 105(1):91–101CrossRefPubMedGoogle Scholar
  17. 17.
    Basser PJ, Mattiello J, LeBihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophys J 66(1):259–267PubMedCentralCrossRefPubMedGoogle Scholar
  18. 18.
    Ulmer JL, Salvan CV, Mueller WM et al (2004) The role of diffusion tensor imaging in establishing the proximity of tumor borders to functional brain systems: implications for preoperative risk assessments and postoperative outcomes. Technol Cancer Res Treat 3(6):567–576CrossRefPubMedGoogle Scholar
  19. 19.
    Witwer BP, Moftakhar R, Hasan KM et al (2002) Diffusion-tensor imaging of white matter tracts in patients with cerebral neoplasm. J Neurosurg 97(3):568–575CrossRefPubMedGoogle Scholar
  20. 20.
    Le Bihan D (2003) Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci 4(6):469–480CrossRefPubMedGoogle Scholar
  21. 21.
    Yamada K, Kizu O, Mori S et al (2003) Brain fiber tracking with clinically feasible diffusion-tensor MR imaging: initial experience. Radiology 227(1):295–301CrossRefPubMedGoogle Scholar
  22. 22.
    Clark CA, Barrick TR, Murphy MM, Bell BA (2003) White matter fiber tracking in patients with space-occupying lesions of the brain: a new technique for neurosurgical planning? Neuroimage. 20(3):1601–1608CrossRefPubMedGoogle Scholar
  23. 23.
    Yuan J, Liu L, Hu Q (2013) Mathematical modeling of brain glioma growth using modified reaction-diffusion equation on brain MR images. Comput Biol Med 43(12):2007–2013CrossRefPubMedGoogle Scholar
  24. 24.
    Swanson KR, Bridge C, Murray JD, Alvord EC Jr (2003) Virtual and real brain tumors: using mathematical modeling to quantify glioma growth and invasion. J Neurol Sci 216(1):1–10CrossRefPubMedGoogle Scholar
  25. 25.
    Pope WB, Lai A, Mehta R et al (2011) Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. AJNR Am J Neuroradiol 32(5):882–889CrossRefPubMedGoogle Scholar
  26. 26.
    Ellingson BM, Malkin MG, Rand SD et al (2010) Validation of functional diffusion maps (fDMs) as a biomarker for human glioma cellularity. J Magn Reson Imaging 31(3):538–548PubMedCentralCrossRefPubMedGoogle Scholar
  27. 27.
    Hamstra DA, Chenevert TL, Moffat BA et al (2005) Evaluation of the functional diffusion map as an early biomarker of time-to-progression and overall survival in high-grade glioma. Proc Natl Acad Sci USA 102(46):16759–16764PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Ellingson BM, LaViolette PS, Rand SD et al (2011) Spatially quantifying microscopic tumor invasion and proliferation using a voxel-wise solution to a glioma growth model and serial diffusion MRI. Magn Reson Med 65(4):1131–1143PubMedCentralCrossRefPubMedGoogle Scholar
  29. 29.
    Ellingson BM, Cloughesy TF, Lai A, Nghiemphu PL, Pope WB (2011) Cell invasion, motility, and proliferation level estimate (CIMPLE) maps derived from serial diffusion MR images in recurrent glioblastoma treated with bevacizumab. J Neurooncol 105(1):91–101CrossRefPubMedGoogle Scholar
  30. 30.
    Jbabdi S, Mandonnet E, Duffau H et al (2005) Simulation of anisotropic growth of low-grade gliomas using diffusion tensor imaging. Magn Reson Med 54(3):616–624CrossRefPubMedGoogle Scholar
  31. 31.
    Painter KJ, Hillen T (2013) Mathematical modelling of glioma growth: the use of diffusion tensor imaging (DTI) data to predict the anisotropic pathways of cancer invasion. J Theor Biol 323:25–39CrossRefPubMedGoogle Scholar
  32. 32.
    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(11):1963–1972CrossRefPubMedGoogle Scholar
  33. 33.
    Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5(2):143–156CrossRefPubMedGoogle Scholar
  34. 34.
    Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2):825–841CrossRefPubMedGoogle Scholar
  35. 35.
    Taylor PA, Cho KH, Lin CP, Biswal BB (2012) Improving DTI tractography by including diagonal tract propagation. PLoS ONE 7(9):e43415PubMedCentralCrossRefPubMedGoogle Scholar
  36. 36.
    Taylor PA, Saad ZS (2013) FATCAT: (an efficient) functional and tractographic connectivity analysis toolbox. Brain Connect 3(5):523–535PubMedCentralCrossRefPubMedGoogle Scholar
  37. 37.
    Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29(3):162–173CrossRefPubMedGoogle Scholar
  38. 38.
    Varentsova A, Zhang S, Arfanakis K (2014) Development of a high angular resolution diffusion imaging human brain template. Neuroimage 91C:177–186CrossRefGoogle Scholar
  39. 39.
    Bates E, Wilson SM, Saygin AP et al (2003) Voxel-based lesion-symptom mapping. Nat Neurosci 6(5):448–450PubMedGoogle Scholar
  40. 40.
    Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. 3-Dimensional proportional system: an approach to cerebral imaging. Thieme Medical Publishers, New YorkGoogle Scholar
  41. 41.
    Binder JR, Desai RH, Graves WW, Conant LL (2009) Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb Cortex 19(12):2767–2796PubMedCentralCrossRefPubMedGoogle Scholar
  42. 42.
    Paez-Ribes M, Allen E, Hudock J et al (2009) Antiangiogenic therapy elicits malignant progression of tumors to increased local invasion and distant metastasis. Cancer Cell 15(3):220–231PubMedCentralCrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Nikolai J. Mickevicius
    • 1
  • Alexander B. Carle
    • 2
  • Trevor Bluemel
    • 2
  • Stephanie Santarriaga
    • 2
  • Fallon Schloemer
    • 3
  • Derrick Shumate
    • 3
  • Jennifer Connelly
    • 3
  • Kathleen M. Schmainda
    • 1
    • 2
  • Peter S. LaViolette
    • 1
    • 2
  1. 1.Department of BiophysicsMedical College of WisconsinMilwaukeeUSA
  2. 2.Department of RadiologyMedical College of WisconsinMilwaukeeUSA
  3. 3.Department of NeurologyMedical College of WisconsinMilwaukeeUSA

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