Patterns of Brain Tumor Recurrence Predicted From DTI Tractography

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1 Patterns of Brain Tumor Recurrence Predicted From DTI Tractography Anitha Priya Krishnan 1, Isaac Asher 2, Dave Fuller 2, Delphine Davis 3, Paul Okunieff 2, Walter O Dell 1,2 Department of Biomedical Engineering 1, Radiation Oncology 2 and Imaging Sciences 3 University of Rochester Medical Center, Rochester, NY USA 14642

2 Stereotactic Radiotherapy (SRT) Radiation oncologists: 4-30mm isotropic margin for gliomas Small margin: recurrences Large margin: damage normal tissue Clinical observation Recurrence often at the boundary of treatment margin Goal: Develop appropriate anisotropic treatment margins using Diffusion Tensor Imaging (DTI)

3 Goal Qualitative correlation of paths of elevated water diffusion along white matter tracts from Tractography with recurrent site. Hypothesis: paths of elevated water diffusion along white matter tracts provide a preferred route for migration of cancer cells.

4 Migration Along WM Tracts Migration of cancer cells along WM tracts in Glioblastoma: 1938 Scherer HJ Matsukado et al Burger et al Johnson et al. Correlate CT with topographic anatomy and postmortem MR imaging with neuropathologic findings Our approach: Correlate White matter tracts from DTI of each patient with recurrent site

5 DTI of Glioblastoma Scalars from DTI (Fractional Anisotropy and Apparent Diffusion Coefficient) 2003 Price et al [1] 2004 Hein et al [2] 2005 Beppu et al [3] Vectors from DTI (principal Eigen vector) 2004 Field et al [4]

6 Patient Selection Category1: distant recurrence Small island of recurrent tumor in the DTI dataset acceptable Category2: local recurrence close to (within 2 cm) or on the boundary of the treatment plan No sign of recurrence in the DTI dataset

7 Image Acquisition GE signa 1.5T MRI scanner EPI sequence, 20 axial slices, voxel dimensions mm 25 diffusion gradient directions, b = 1000, 3 reference (b = 0) scans

8 DTI Analysis Tractography: DTIStudio and FSL ( DTIStudio [5]: Fiber Assignment by Continuous Tracking (FACT)

9 DTI Analysis FSL [6]: Probabilistic fiber tracking Calculates uncertainty in the fiber direction Can track fibers in gray matter Fiber probability maps for seed point in optic tract Yellow high probability, Red moderate relative probability

10 Results: Category 1 (n = 3) Primary GBM Spot on left horn not treated Fiber tracts passing through: Tumor growth Primary tumor Recurrent site

11 Results: Category 1

12 Results: Category 2 (n = 4) Primary Glioma Pretreatment MR T1 Yellow High probability. Red Medium relative probability of connection Fiber Map from FSL SRS treatment plan Recurrence tumor Post contrast Axial MR T1 3 months after SRS

13 Results: Category 2 Primary tumor Pretreatment MR T1 SRS treatment plan Recurrence tumor Fibers from primary Post contrast Axial MR T1 3 months after SRS

14 Results: Category 2

15 Conclusions & Future Work Preliminary results on a small number of patient datasets suggest that the hypothesis is correct Future Work High resolution DTI dataset using 3T MR Improved Fiber analysis Verification with animal models

16 References 1. Price SJ, Burnet NG, Donovan T, et al. Diffusion tensor imaging of brain tumors at 3T: a potential tool for assessing white matter tract invasion? Clin Radiol. 2003; 58: Hein, Patrick A, Eskey, et al. Diffusion-Weighted Imaging in the Follow-up of Treated High-Grade Gliomas: Tumor Recurrence versus Radiation Injury. AJNR Am J Neuroradiol : Beppu T, Inoue T, Shitaba Y, et al. Fractional anisotropy value by diffusion tensor magnetic resonance imaging as a predictor of cell density and proliferation activity of glioblastomas. Surg Neurol. 2005; 63(1): Field Aaron, Alexander Andrew. Diffusion Tensor Imaging in cerebral tumor diagnosis and therapy. Top Magn Reson Imaging oct; 15 (5): Huang H, Zhang J, van Zijl PC, Mori S. Analysis of noise effects on DTI-based tractography using the brute-force and multi-roi approach. Magnetic Resonance in Medicine 2004;52(3): T.E.J. Behrens, M.W. Woolrich, et al. Characterisation and Propagation of Uncertainty in Diffusion Weighted MR imaging. Magn. Reson Med 2003;50: