Author:
Steiner Gerald,Galli Roberta,Preusse Grit,Michen Susanne,Meinhardt Matthias,Temme Achim,Sobottka Stephan B.,Juratli Tareq A.,Koch Edmund,Schackert Gabriele,Kirsch Matthias,Uckermann Ortrud
Abstract
Abstract
Purpose
Infrared (IR) spectroscopy has the potential for tumor delineation in neurosurgery. Previous research showed that IR spectra of brain tumors are generally characterized by reduced lipid-related and increased protein-related bands. Therefore, we propose the exploitation of these common spectral changes for brain tumor recognition.
Methods
Attenuated total reflection IR spectroscopy was performed on fresh specimens of 790 patients within minutes after resection. Using principal component analysis and linear discriminant analysis, a classification model was developed on a subset of glioblastoma (n = 135) and non-neoplastic brain (n = 27) specimens, and then applied to classify the IR spectra of several types of brain tumors.
Results
The model correctly classified 82% (517/628) of specimens as “tumor” or “non-tumor”, respectively. While the sensitivity was limited for infiltrative glioma, this approach recognized GBM (86%), other types of primary brain tumors (92%) and brain metastases (92%) with high accuracy and all non-tumor samples were correctly identified.
Conclusion
The concept of differentiation of brain tumors from non-tumor brain based on a common spectroscopic tumor signature will accelerate clinical translation of infrared spectroscopy and related technologies. The surgeon could use a single instrument to detect a variety of brain tumor types intraoperatively in future clinical settings. Our data suggests that this would be associated with some risk of missing infiltrative regions or tumors, but not with the risk of removing non-tumor brain.
Funder
Technische Universität Dresden
Publisher
Springer Science and Business Media LLC
Subject
Cancer Research,Neurology (clinical),Neurology,Oncology
Cited by
3 articles.
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