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Determining Malignancy of Brain Tumors by Analysis of Vessel Shape

  • Elizabeth Bullitt
  • Inkyung Jung
  • Keith Muller
  • Guido Gerig
  • Stephen Aylward
  • Sarang Joshi
  • Keith Smith
  • Weili Lin
  • Matthew Ewend
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3217)

Abstract

Vessels supplying malignant tumors are abnormally shaped. This paper describes a blinded study that assessed tumor malignancy by analyzing vessel shape within MR images of 21 brain tumors prior to surgical resection. The program’s assessment of malignancy was then compared to the final histological diagnosis. All tumors were classified correctly as benign or malignant. Of importance, malignancy-associated vessel abnormalities extend outside apparent tumor margins, thus allowing classification of even small or hemorrhagic tumors.

Keywords

Brain Tumor Total Path Length Vessel Analysis Magnetic Resonance Unit Vessel Tortuosity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Benard, F., Romsa, J., Hustinx, R.: Imaging gliomas with positron emission tomography and single-photon emission computed tomography. Seminars Nuc. Med. 23, 148–162 (2003)CrossRefGoogle Scholar
  2. 2.
    Burtscher, L.M., Holtas, S.: Proton magnetic resonance spectroscopy in brain tumors: clinical applications. Neuroradiology 43, 345–352 (2001)CrossRefGoogle Scholar
  3. 3.
    Tosi, M.R., Fini, G., Tinti, A., Reggiani, A., Tugnoli, V.: Molecular characterization of human healthy and neoplastic cerebral and renal tissues by in vitro 1H NMR spectroscopy (Review). International Journal of Molecular Medicine 9, 299–310 (2002)Google Scholar
  4. 4.
    Law, M., Yang, S., Wang, H., Babb, J.S., Johnson, G., Cha, S., Knopp, E.A., Zagzag, D.: Glioma Grading:Sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR 24, 1989–1998 (2003)Google Scholar
  5. 5.
    Baish, J.S., Jain, R.K.: Fractals and cancer. Cancer Research 60, 3683–3688 (2000)Google Scholar
  6. 6.
    Folkman, J.: Incipient Angiogenesis. Journal of the National Cancer Institute 92, 94–95 (2000)CrossRefGoogle Scholar
  7. 7.
    Lau, D.H., Xue, L., Young, L.J., Burke, P.A., Cheung, A.T.: Paclitaxel (Taxol): an inhibitor of angiogenesis in a highly vascularized transgenic breast cancer. Cancer Biother. Radiopharm 14, 31–36 (1999)CrossRefGoogle Scholar
  8. 8.
    Burger, P.C., Scheithauer, B.W., Vogel, F.S.: Surgical Pathology of the Nervous System and its Coverings, 3rd edn. Churchill Livingstone, New York (1991)Google Scholar
  9. 9.
    Siemann, D.: Vascular Targeting Agents. Horizons in Cancer Therapeutics 3, 4–15 (2002)Google Scholar
  10. 10.
    Helmlinger, G., Sckell, A., Dellian, M., Forbes, N.S., Jain, R.K.: Acid production in glycolysisimpaired tumors provides new insights into tumor metabolism. Clinical Cancer Research 8, 1284–1291 (2002)Google Scholar
  11. 11.
    Jain, R.K.: Normalizing tumor vasculature with anti-angiogenic therapy: a new paradigm for combination therapy. Nature Medicine 7, 987–998 (2001)CrossRefGoogle Scholar
  12. 12.
    Jackson, A., Kassner, A., Annesley-Williams, D., Reid, H., Zhu, X., Li, K.: Abnormalities in the recirculation phase of contrast agent bolus passage in cerebral gliomas: Comparison with relative blood volume and tumor grade. AJNR 23, 7–14 (2002)Google Scholar
  13. 13.
    Bullitt, E., Gerig, G., Pizer, S., Aylward, S.R.: Measuring tortuosity of the intracerebral vasculature from MRA images. IEEE-TMI 22, 1163–1171 (2003)Google Scholar
  14. 14.
    Bullitt, E., Gerig, G., Aylward, S., Joshi, S., Smith, K., Ewend, M., Lin, W.: Vascular Attributes and Malignant Brain Tumors. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 671–679. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  15. 15.
    Brey, E.M., King, T.W., Johnston, C., McIntire, L.V., Reece, G.P., Patrick, C.W.: A technique for quantitative three-dimensional analysis of microvascular structure. Microvascular Research 63, 279–294 (2002)CrossRefGoogle Scholar
  16. 16.
    Sabo, E., Boltenko, A., Sova, Y., Stein, A., Kleinhaus, S., Resnick, M.B.: Microscopic Analysis and Significance of Vascular Architectural Complexity in Renal Cell Carcinoma. Clinical Cancer Research 7, 533–537 (2001)Google Scholar
  17. 17.
    Neufeld, V.: Webster’s New World Dictionary, p. 623. Warner Books, New York (1990)Google Scholar
  18. 18.
    Aylward, S., Bullitt, E.: Initialization, noise, singularities and scale in height ridge traversal for tubular object centerline extraction. IEEE-TMI 21, 61–75 (2002)Google Scholar
  19. 19.
    Bullitt, E., Aylward, S., Smith, K., Mukherji, S., Jiroutek, M., Muller, K.: Symbolic Description of Intracerebral Vessels Segmented from MRA and Evaluation by Comparison with XRay Angiograms. Medical Image Analysis 5, 157–169 (2001)CrossRefGoogle Scholar
  20. 20.
    Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., Gerig, G.: Automatic brain tumor segmentation by subject specific modification of atlas priors. Academic. Radiology 10, 1341–1348 (2003)CrossRefGoogle Scholar
  21. 21.
    Schnabel, J.A., Rueckert, D., Quist, M., Blackall, J.M., Castellano Smith, A.D., Hartkens, T., Penney, G.P., Hall, W.A., Liu, H., Truwit, C.L., Gerritsen, F.A., Hill, D.L.G., Hawkes, J.D.: A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 573–581. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  22. 22.
    Rueckert, D.: Rview (2002), Available: www.doc.ic.ac.uk/~dr/software
  23. 23.
    ICBM Atlas, McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, CanadaGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Elizabeth Bullitt
    • 1
  • Inkyung Jung
    • 2
  • Keith Muller
    • 2
  • Guido Gerig
    • 3
  • Stephen Aylward
    • 4
  • Sarang Joshi
    • 5
  • Keith Smith
    • 4
  • Weili Lin
    • 4
  • Matthew Ewend
    • 1
  1. 1.Department of SurgeryUniversity of North CarolinaChapel HillUSA
  2. 2.Department of BiostatiscsUniversity of North CarolinaChapel HillUSA
  3. 3.Department of Computer ScienceUniversity of North CarolinaChapel HillUSA
  4. 4.Department of RadiologyUniversity of North CarolinaChapel HillUSA
  5. 5.Department of Radiation OncologyUniversity of North CarolinaChapel HillUSA

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