Intraoperative discrimination of native meningioma and dura mater by Raman spectroscopy

Abstract

Meningiomas are among the most frequent tumors of the central nervous system. For a total resection, shown to decrease recurrences, it is paramount to reliably discriminate tumor tissue from normal dura mater intraoperatively. Raman spectroscopy (RS) is a non-destructive, label-free method for vibrational analysis of biochemical molecules. On the microscopic level, RS was already used to differentiate meningioma from dura mater. In this study we test its suitability for intraoperative macroscopic meningioma diagnostics. RS is applied to surgical specimen of intracranial meningiomas. The main purpose is the differentiation of tumor from normal dura mater, in order to potentially accelerate the diagnostic workflow. The collected meningioma and dura mater samples (n = 223 tissue samples from a total of 59 patients) are analyzed under untreated conditions using a new partially robotized RS acquisition system. Spectra (n = 1273) are combined with the according histopathological analysis for each sample. Based on this, a classifier is trained via machine learning. Our trained classifier separates meningioma and dura mater with a sensitivity of 96.06 $$\pm $$ ± 0.03% and a specificity of 95.44 $$\pm $$ ± 0.02% for internal fivefold cross validation and 100% and 93.97% if validated with an external test set. RS is an efficient method to discriminate meningioma from healthy dura mater in fresh tissue samples without additional processing or histopathological imaging. It is a quick and reliable complementary diagnostic tool to the neuropathological workflow and has potential for guided surgery. RS offers a safe way to examine unfixed surgical specimens in a perioperative setting.

Raman Spectroscopy: A Tool for Molecular Fingerprinting of Brain Cancer

“…Though the sensitivity is obtained to be 100% from the classifier, single spot measurements cannot be used for diagnosis as in areas of infiltration the tumor margin in the tissue varies from point-topoint necessitating the need for more spectra. 31 The feature engineering approach opted for by Leblond et al analyses a data set of 547 spectra based on about 30 parameters through a Bayesian framework. This comprehensive molecular profiling infers plausible changes noticed in glioma like a nucleic acid increase, collagen IV overexpression, and a shift in the spectra of peaks involved in primary metabolism.…”

“…The study used the tissue samples obtained intraoperatively without any preprocessing and were measured using Raman spectroscopy within 20 min of excision to match the in vivo sample condition as much as possible. Though the sensitivity is obtained to be 100% from the classifier, single spot measurements cannot be used for diagnosis as in areas of infiltration the tumor margin in the tissue varies from point-to-point necessitating the need for more spectra …”

Chondrogenic Cancer Grading by combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues

“…In this sense, the employment of Machine Learning (ML) algorithms to interpret Raman-derived experimental data could represent a powerful solution to retrieve the information of interest in times compatible with the final use requirements [11][12][13]. In particular, Several works [14][15][16][17] showed how RS in combination with ML allows to detect malignant tissues in timescales of minutes or even seconds, being a potential help in the intraoperative estimation of the tumor margins.…”

Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms

“…The applications of this vibrational spectroscopic technique, which is physically based on inelastically scattered photons and subsequent generation of a molecular ngerprint, range from intra-/perioperative use in neurosurgery to employment of this method in diagnostic pathology 5,6 . Based on spectral tissue properties, fast and label-free discrimination between healthy and tumoral tissue 7 as well as differentiation between different tumor types 8 and grades 9 is feasible. For intraoperative use, fast measurements of single spots would need to provide su cient information about the nature of the underlying tissue in order to be useful for resection control.…”