A Handheld Visible Resonance Raman Analyzer Used in Intraoperative Detection of Human Glioma

Abstract

There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.

Accurate and rapid molecular subgrouping of high-grade glioma via deep learning-assisted label-free fiber-optic Raman spectroscopy

“…For instance, Michael et al ( 54 ) developed a handheld contact Raman spectroscopy probe technique for live, local detection of cancer cells in the human brain with a sensitivity of 93% and a specificity of 91%. Zhang et al ( 23 ) investigated human glioma using a portable visible resonance Raman analyzer under quasiclinical conditions with over 80% accuracy. Kevin et al ( 55 ) for the first time developed a Raman spectroscopy guidance system in human in vivo integrated with a brain biopsy needle.…”

“…AI algorithms have been shown to improve the robustness of Raman spectroscopy-based precise cancer diagnosis ( 22 ). Particularly, previous studies have demonstrated the capability of Raman spectroscopy in intraoperative brain cancer detection ( 23–27 ). Regarding molecular subgrouping, most Raman spectroscopy studies focused on detection of IDH mutation, the accuracy of which ranged from 80 to 90% ( 28–30 ).…”

Human glioma tumor diagnosis using visible resonance Raman spectroscopy and deep learning

“…It can help a surgical doctor to better remove the tumor and achieve total resection. In our previous studies, a Raman technique called visible resonance Raman (VRR) spectroscopy was shown to be promising as an optical molecular diagnostic technique for rapid noninvasive intraoperative diagnosis of glioma tumors among other human tumors [4][5][6][7][8]. Machine learning analysis along with decomposition methods has been used to analyze and classify spectral data and yielded high accuracy in distinguishing cancerous from healthy tissue or cells [4,5,7,[9][10][11][12][13][14].…”

“…In our previous studies, a Raman technique called visible resonance Raman (VRR) spectroscopy was shown to be promising as an optical molecular diagnostic technique for rapid noninvasive intraoperative diagnosis of glioma tumors among other human tumors [4][5][6][7][8]. Machine learning analysis along with decomposition methods has been used to analyze and classify spectral data and yielded high accuracy in distinguishing cancerous from healthy tissue or cells [4,5,7,[9][10][11][12][13][14]. However, when Raman spectra are classified, preprocessing such as baseline subtraction has been part of the standard procedure which usually involves tuning fitting parameters.…”

Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy

“…So far, the practical potential of this method in combination with supervised and unsupervised machine learning algorithms has been shown not only for the classification of tumor types and distinct histomorphological tumor areas but also for the differentiation of the tumor grade or the detection of tumor margins/infiltration zone [4][5][6][7]. Most recently, Zhang et al yielded >80% accuracy with support vector machinebased intraoperative differentiation of glioma tissue and healthy control, and Romanishkin et al reported a support vector machine-based accuracy in detecting glioblastoma tissue (regardless of the histologically proven sampling area, namely, central tumor core or tumor edge) of 83% [8,9]; by using unprocessed samples of pediatric brain tumors, even an accuracy of 86.2% was achieved when classifying between low-grade gliomas and normal brain by employing a logistic regression model [10]. Although, commonly, fresh tissue specimens are spectroscopically examined intraoperatively in vivo [11] by using a handheld Raman probe or ex vivo [4,12] by using advanced Raman imaging techniques, e.g., Stimulated Raman Histology, other approaches aim to establish RS on formalin-fixed or formalin-fixed and paraffin-embedded (FFPE) tissue.…”