Towards real-time intraoperative tissue interrogation for REIMS-guided glioma surgery

Laura Van Hese; Steven De Vleeschouwer; Tom Theys; Emma Larivière; Lien Solie; Raf Sciot; Tiffany Porta Siegel; Steffen Rex; Ron M A Heeren; Eva Cuypers

doi:10.1016/j.jmsacl.2022.04.004

Towards real-time intraoperative tissue interrogation for REIMS-guided glioma surgery

Laura Van Hese, Steven De Vleeschouwer, Tom Theys, Emma Larivière, Lien Solie, Raf Sciot, Tiffany Porta Siegel, Steffen Rex, Ron M A Heeren, Eva Cuypers^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

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.

Original language	English
Pages (from-to)	80-89
Number of pages	10
Journal	Journal of Mass Spectrometry and Advances in the Clinical Lab
Volume	24
DOIs	https://doi.org/10.1016/j.jmsacl.2022.04.004
Publication status	Published - Apr 2022

Keywords

5-AMINOLEVULINIC ACID
Brain tumors
FLUORESCENCE
IDENTIFICATION
IN-VIVO
MASS-SPECTROMETRY
PHASE
REIMS
RESECTION
Real-time characterisation
TUMOR
Tumor margin delineation

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10.1016/j.jmsacl.2022.04.004Licence: CC BY-NC-ND

Cite this

@article{5a1cc9fe0ff64b6fa0aeb5f24f434b4a,

title = "Towards real-time intraoperative tissue interrogation for REIMS-guided glioma surgery",

abstract = "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.",

keywords = "5-AMINOLEVULINIC ACID, Brain tumors, FLUORESCENCE, IDENTIFICATION, IN-VIVO, MASS-SPECTROMETRY, PHASE, REIMS, RESECTION, Real-time characterisation, TUMOR, Tumor margin delineation",

author = "{Van Hese}, Laura and {De Vleeschouwer}, Steven and Tom Theys and Emma Larivi{\`e}re and Lien Solie and Raf Sciot and Siegel, {Tiffany Porta} and Steffen Rex and Heeren, {Ron M A} and Eva Cuypers",

note = "{\textcopyright} 2022 THE AUTHORS. Publishing services by ELSEVIER B.V. on behalf of MSACL.",

year = "2022",

month = apr,

doi = "10.1016/j.jmsacl.2022.04.004",

language = "English",

volume = "24",

pages = "80--89",

journal = "Journal of Mass Spectrometry and Advances in the Clinical Lab",

issn = "2667-145X",

publisher = "Elsevier",

}

TY - JOUR

T1 - Towards real-time intraoperative tissue interrogation for REIMS-guided glioma surgery

AU - Van Hese, Laura

AU - De Vleeschouwer, Steven

AU - Theys, Tom

AU - Larivière, Emma

AU - Solie, Lien

AU - Sciot, Raf

AU - Siegel, Tiffany Porta

AU - Rex, Steffen

AU - Heeren, Ron M A

AU - Cuypers, Eva

PY - 2022/4

Y1 - 2022/4

N2 - 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.

AB - 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.

KW - 5-AMINOLEVULINIC ACID

KW - Brain tumors

KW - FLUORESCENCE

KW - IDENTIFICATION

KW - IN-VIVO

KW - MASS-SPECTROMETRY

KW - PHASE

KW - REIMS

KW - RESECTION

KW - Real-time characterisation

KW - TUMOR

KW - Tumor margin delineation

U2 - 10.1016/j.jmsacl.2022.04.004

DO - 10.1016/j.jmsacl.2022.04.004

M3 - Article

C2 - 35572786

SN - 2667-145X

VL - 24

SP - 80

EP - 89

JO - Journal of Mass Spectrometry and Advances in the Clinical Lab

JF - Journal of Mass Spectrometry and Advances in the Clinical Lab

ER -