Automated online safety margin (GLIOVIS) for glioma surgery model

Area of Science:

• Neurosurgery
• Medical Imaging
• Oncology

Background:

• Glioblastoma is an aggressive brain tumor with poor prognosis.
• Current surgical resection relies on 5-aminolevulinic acid (5-ALA) fluorescence, but faces limitations like variability.
• Accurate identification of tumor infiltration margins is crucial for effective glioblastoma treatment.

Purpose of the Study:

• To introduce GLIOVIS, a novel software for analyzing 5-ALA-induced fluorescence images.
• To improve the recognition of tumor infiltration margins during glioblastoma surgery.
• To create an intraoperative safety map for enhanced surgical guidance.

Main Methods:

• Development of GLIOVIS software for quantitative analysis of Protoporphyrin IX fluorescence.
• Utilizing the Intraoperative Orbital Camera Olympus Orbeye (IOC) for image acquisition.
• Testing the software on gelatin-based brain models with varying fluorescence levels.

Main Results:

• GLIOVIS successfully processed images from brain models, generating fluorescence intensity maps.
• The software enabled the visualization of safety margins, aiding in tumor delineation.
• Qualitative digitalization of different fluorescence levels was achieved using this technique.

Conclusions:

• GLIOVIS demonstrates potential for image processing in 5-ALA-guided glioblastoma surgery.
• Validation on brain models shows promise for digitalizing fluorescence data.
• Further in vivo and clinical validation is required to establish GLIOVIS as a new standard of care.