An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography
- 1Department of Neurology, University of California, San Francisco, United States
- 2University of California, Berkeley, United States
- 3Department of Radiation Oncology, University of California, San Francisco, United States
- 4Samaritan Pastega Regional Cancer Center, Samaritan Health Services, United States
- 5Department of Radiology, University of Washington, United States
- 6Department of Radiology and Biomedical Imaging, University of California, San Francisco, United States
- 7Center for Imaging of Neurodegenerative Disease, San Francisco VA Health Care System, United States
- 8Department of Neurology, University of California, San Francisco, United States
There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system.
Keywords: tractography, Glioma, Code:Python, Neuro Oncology, Radiation therapy (radiotherapy), diffusion MRI (dMRI)
Received: 08 Apr 2019;
Accepted: 08 Aug 2019.
Edited by:Natalie J. Serkova, School of Medicine, University of Colorado Denver, United States
Reviewed by:William I. Rae, University of Sydney, Australia
Guolin Ma, China-Japan Friendship Hospital, China
Copyright: © 2019 Jordan, Morin, Wahl, Amirbekian, Chapman, Owen, Mukherjee, Braunstein and Henry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Kesshi M. Jordan, University of California, San Francisco, Department of Neurology, San Francisco, United States, firstname.lastname@example.org