Introduction: The main goal of brain tumour surgery is to maximize tumour resection while avoiding neurological deficits. Accurate characterization of tissue and delineation of resection margins are, therefore, essential to achieve optimal surgical results.
Objectives: The primary objective of this study was to develop and validate a mass spectrometry- based technique for the molecular characterization of high- and low-grade glioma tissue during surgery.
Methods: An electrosurgical knife is connected to a mass spectrometer (iKnife). Using this system, an aerosol created during electrosurgical resection is aspirated to a mass spectrometer to determine the molecular profile of the tissue within seconds. This rapid evaporative ionization mass spectrometry (REIMS) technique is used to create a chemical profile database and develop a real-time tissue recognition system based on machine learning.
Results: Classification models were built by analysing biopsies from 36 patients who underwent brain tumour resection. Our multivariate statistical model could differentiate between astrocytoma grade II and III, glioblastoma, oligodendroglioma grade II and III, and normal brain tissue with an 88% overall accuracy. Astrocytoma and oligodendroglioma grade II were separated from normal brain with a 96% correct classification rate. REIMS could differentiate between different percentages of GBM with 99.2% sensitivity and different percentages of astrocytoma grade II with 97.5% sensitivity.
Conclusion: Real-time information during electrosurgical dissection can improve intra-operative decision-making, leading to a more accurate tumour removal for different glioma subtypes.
|Number of pages||10|
|Journal||Journal of Mass Spectrometry and Advances in the Clinical Lab|
|Publication status||Published - Apr 2022|
- 5-AMINOLEVULINIC ACID
- Brain tumors
- Real-time characterisation
- Tumor margin delineation