Automated online safety margin (GLIOVIS) for glioma surgery model

Marianne Mazevet; Christian Oberli; Sebastiano Marinelli; Ismail Zaed; Stefanie Bauer; Alain Kaelin-Lang; Francesco Marchi; Roberto Gardenghi; Michael Reinert; Andrea Cardia

doi:10.3389/fonc.2024.1361022

Automated online safety margin (GLIOVIS) for glioma surgery model

Front Oncol. 2024 Apr 29:14:1361022. doi: 10.3389/fonc.2024.1361022. eCollection 2024.

Authors

Affiliations

¹ Department of Innovative Technologies, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
² Department of Neurosurgery, Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale, Lugano, Switzerland.
³ Faculty of Biomedical Sciences, Università della Svizzera Italiana, Lugano, Switzerland.
⁴ Department of Neurology, Neurocenter of Southern Switzerland, Ente Ospedaliero Cantonale, Lugano, Switzerland.
⁵ Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
⁶ Department of Neurosurgery, Hirslanden Neurological and Spinal Surgery Center, St. Anna Clinic, Lucerne, Switzerland.
⁷ Department of Neurosurgery, Inselspital University Hospital, University of Bern, Bern, Switzerland.

Abstract

Purpose: Glioblastoma is the most common type of primary brain malignancy and has a poor prognosis. The standard treatment strategy is based on maximal safe surgical resection followed by radiotherapy and chemotherapy. Surgical resection can be optimized by using 5-delta-aminolevulinic acid (5-ALA)-induced fluorescence, which is the current mainstay. Although 5-ALA-induced fluorescence has gained general acceptance, it is also limited by inter-observer variability and non-standardized fluorescence parameters. We present a new software for processing images analysis to better recognize the tumor infiltration margins using an intraoperative immediate safety map of 5-ALA-induced fluorescence. We tested this in a brain model using a commercial surgical exoscope.

Methods: A dedicated software GLIOVIS (ACQuF-II, Advanced Colorimetry-based Quantification of Fluorescence) was designed for processing analysis of images taken on the Intraoperative Orbital Camera Olympus Orbeye (IOC) to determine the relative quantification of Protoporphyrin IX (5-ALA metabolite) fluorescence. The software allows to superpose the new fluorescence intensity map and the safety margins over the original images. The software was tested on gel-based brain models.

Results: Two surrogate models were developed: PpIX agarose gel-integrated in gelatin-based brain model at different scales (1:25 and 1:1). The images taken with the IOC were then processed using GLIOVIS. The intensity map and safety margins could be obtained for all available models.

Conclusions: GLIOVIS for 5-ALA-guided surgery image processing was validated on various gelatin-based brain models. Different levels of fluorescence could be qualitatively digitalized using this technique. These results need to be further confirmed and corroborated in vivo and validated clinically in order to define a new standard of care for glioblastoma resection.

Keywords: 5-ALA–guided surgery; fluorescence quantification; glioblastoma; gliolan; safety margins.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was supported by the Fondazione Leonardo.