Abstract | OBJECTIVE: Accurate identification of surgical margins in brain tumors is of significant prognostic importance. Despite the availability of methods such as 5-ALA and image guidance, recognizing tumor boundary is highly subjective, dependant on recognizing subtle changes in tissue characteristics including texture and color to aid distinction. METHOD: Design and development of a semi-automated system integrated with MEMS-based electromechanical sensors to enable an objective and reliable method of distinguishing tumors from normal brain tissue. Simultaneous electrical impedance and viscoelastic characterization of three types of freshly excised gliomas ( glioblastoma (GBM), astrocytoma (AST), and oligodendroglioma (OLI)) (N = 8 each) and seventeen different normal brain regions (N = 6 each) obtained postmortem. RESULTS: The electrical impedance of gliomas (462±56Ω) was found to be significantly lower than corresponding normal (1267±515Ω) regions at 100kHz (p = 7.46e-11). The difference in the impedance between individual tumor types and corresponding normal regions was also statistically significant (p = 1e-8), suggesting accurate tumor delineation. There were distinct differences in the viscoelastic relaxation responses of high-grade and low-grade gliomas. White matter regions demonstrated higher impedance and faster stress relaxation compared to grey matter regions as a characteristic of their structural composition. CONCLUSION: We demonstrate that simultaneous electromechanical characterization of brain tumors and normal brain tissues can be an effective biomarker for tumor delineation, grading, and studying heterogeneity between the brain regions. SIGNIFICANCE: The observations suggest the potential use of the technology in a clinical setting to achieve gross total resection and improve treatment outcomes by helping surgeons perform real-time risk evaluation during surgery.
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Authors | Arjun Bs, Alekya B, Hari Rs, Vikas V, Anita Mahadevan, Hardik J Pandya |
Journal | IEEE transactions on bio-medical engineering
(IEEE Trans Biomed Eng)
Vol. 69
Issue 11
Pg. 3484-3493
(11 2022)
ISSN: 1558-2531 [Electronic] United States |
PMID | 35486560
(Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
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Chemical References |
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Topics |
- Humans
- Glioma
(diagnostic imaging)
- Brain Neoplasms
(pathology)
- Glioblastoma
(diagnostic imaging, pathology)
- Brain
(diagnostic imaging, pathology)
- Biomarkers
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