Surgical resection is a fixture in the treatment of intracranial tumors, and there is mounting data indicating that overall and progression-free survival improve for gross total resection compared to subtotal resection. The current standard of care for managing intracranial tumors relies heavily on MRI of gadolinium-based contrast agents (Gd-MRI), which plays a central role in diagnosis, surgical planning, intra-surgical guidance, and follow-up monitoring. During surgery, patients are spatially registered to the pre-operative MRI and MRI-derived tumor contours projected over the visual field within the surgical microscope to guide resection. Despite the widespread deployment of these sophisticated tools in surgery, subtotal resection rates remain stubbornly high. The primary culprits include difficulty in identifying tumor visually and the diminishing accuracy of the pre-op registration due to brain deformation as the surgery progresses. In this context, expansive efforts have sought to alleviate these shortcomings, including the use of intra-operative stereovision and/or ultrasound with brain deformation models to update the pre-op MRI and the use of fluorescent agents to label tumor in the visual field. Although promising, both of these approaches have known shortcomings. Specifically, the data sources used for updating pre-op MRI are only surrogate correlates with MRI, and most current fluorescence guided surgery (FGS) efforts focus on targeted agents designed to mark molecular features of tumor cells, which have shown high intra-patient/tumoral heterogeneity. This project aims to solve both of these shortcomings directly by leveraging the existing clinical understanding of Gd-MRI in managing intracranial tumors. Specifically, we will identify and evaluate fluorescent agents that mimic the kinetic behavior of conventional MRI-based contrast agents to guide intracranial tumor surgery. This approach will transfer the well-understood behavior of Gd-MRI directly into the visual field, enable rapid, intra-surgical administration of the agent, and provide an ideal data input for updating of pre-op MRI during surgery. Our approach is premised on compelling preliminary data in small animal glioma models showing highly correlative uptake between Gd-MRI and several untargeted optical agents. To advance this strategy we will, (1) rigorously validate these results and examine additional optical agent candidates using MRI and our custom hyperspectral whole-body imaging cryomacrotome, (2) establish concordance between candidate optical agents and Gd-MRI in a new porcine glioma model using our intra-operative MRI facility and FGS instruments, and (3) assess the capacity to use the optical agent data to update the pre-op MRI. We will also quantitatively compare uptake of the candidate agents, Gd-MRI and ALA-PPIX, the current standard for FGS of glioma. Completing the aims of this project will establish the optical analog strategy as a compelling approach for surgical guidance and lay the groundwork for clinical translation.

Public Health Relevance

The extent to which cancer tissue is completely removed during surgery of intracranial tumors is an important prognostic indicator of local recurrence and overall patient survival; however, current and new approaches designed to guide resection have major shortcomings. To solve this problem, we are advancing a new strategy for surgical guidance that aims to use injected optical contrast agents to reproduce the diagnostic information provided by pre-operative contrast-enhanced MRI images. Contrast-enhanced MRI imaging is the standard reference for diagnosis, surgical navigation and post-treatment monitoring of intracranial tumors; and thus, transferring this important information into the visual surgical field will be a major development to help surgeons maximize tumor clearing.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Imaging Guided Interventions and Surgery Study Section (IGIS)
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Fountain, Jane W
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Dartmouth College
Engineering (All Types)
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United States
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