Surgical resection is one of the mainstay treatments for human gliomas and a growing number of studies have demonstrated the benefits of maximal safe resection for patient survival. The goal of modern treatment planning is to aid the surgeon in determining the optimal resection trajectory and margin that avoids eloquent (language, visual and motor) tracts, maximizing tumor removal while preserving eloquent function. Diffusion imaging provides critical insight into the WM fiber pathways of the brain. Identifying eloquent tracts in the vicinity of a tumor requires fiber tracking through regions affected by edema and mass effect. Current tractography tools frequently fail to reconstruct fiber pathways that have crossing fibers or are edematous, shifted and/or infiltrated by a surrounding tumor, limiting their clinical utility. Furthermore, these tools require manual placement of seed regions to segment tracts, which time consuming, and subject to inter-user and inter-software variability. Finally, there is no comprehensive validation undertaken in the presence of a tumor. This calls for the development of innovative technical solutions, translated to the clinic, that alleviate these issue with current surgical planning tools.
In Aim 1, we will develop an edema invariant fiber tractography paradigm that incorporates a multi-compartment model into an anatomically and functionally constrained fiber tracking. The multi-compartment model will consist of a free water compartment that characterizes the edema and a high angular diffusion compartment to characterize underlying fibers.
In Aim 2, we will develop an automated tract extraction paradigm, based on structural and functional connectivity information, robust to mass effect and edema. Finally, comprehensive validation will be undertaken in the form of voxel-wise validation of the tractography on a software phantom and replication dataset (Aim 1). The reconstructed fiber tracts (bundles) will be validated by comparing with manual drawings by experts. Additionally, in Aim 3, we propose a further validation of tracts (fiber bundles) using direct electrical stimulatio during awake surgery, task fMRI and comparison of tracts before and after surgery on the same patient, at two different stages of edema. These tracts will then be incorporated into a map of eloquent function comprising of automatically extracted fiber tracts along with tract proximity measures. This map will enable the surgeon to perform a sophisticated pre-surgical analysis when determining the surgical trajectory. These novel techniques will then be combined into a platform independent web-accessible tool, in Aim 4, that can be used by any clinician. The tool will be evaluated against current planning tools by surgeons and radiologists. Due to these methodologically advanced and clinically relevant features, we expect the tool to provide treatment planning capabilities beyond the currently available clinical software, leading to enhanced safety during surgery for patients with brain cancer, resulting in reduced post-surgical deficits with improved quality of life. The tool can also be used for radiation planning.

Public Health Relevance

This project aims to develop a new paradigm for surgical and treatment planning that consists of an anatomically and functionally constrained tractography that can track through edema and shape changes caused by the tumor, a method for automated tract extraction and validation, that will be incorporated into a map of eloquent function. These methods will be integrated into a validated tool for use by clinicians. Ultimately, this software wll improve the quality of life of patients undergoing treatment for brain cancer by enabling preservation of eloquent function and safer therapy planning.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS096606-05
Application #
9894856
Study Section
Medical Imaging Study Section (MEDI)
Program Officer
Babcock, Debra J
Project Start
2016-03-01
Project End
2021-02-28
Budget Start
2020-03-01
Budget End
2021-02-28
Support Year
5
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
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