Significance: Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons.
Aim: We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices.
Approach: We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting.
Results: The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing.
Conclusions: The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.
KEYWORDS: Raman spectroscopy, Tissues, Signal to noise ratio, In vivo imaging, Cancer, Brain, Data acquisition, Luminescence, Tissue optics, Visualization
Significance: Ensuring spectral quality is prerequisite to Raman spectroscopy applied to surgery. This is because the inclusion of poor-quality spectra in the training phase of Raman-based pathology detection models can compromise prediction robustness and generalizability to new data. Currently, there exists no quantitative spectral quality assessment technique that can be used to either reject low-quality data points in existing Raman datasets based on spectral morphology or, perhaps more importantly, to optimize the in vivo data acquisition process to ensure minimal spectral quality standards are met.
Aim: To develop a quantitative method evaluating Raman signal quality based on the variance associated with stochastic noise in important tissue bands, including C─C stretch, CH2 / CH3 deformation, and the amide bands.
Approach: A single-point hand-held Raman spectroscopy probe system was used to acquire 315 spectra from 44 brain cancer patients. All measurements were classified as either high or low quality based on visual assessment (qualitative) and using a quantitative quality factor (QF) metric. Receiver-operator-characteristic (ROC) analyses were performed to evaluate the performance of the quantitative metric to assess spectral quality and improve cancer detection accuracy.
Results: The method can separate high- and low-quality spectra with a sensitivity of 89% and a specificity of 90% which is shown to increase cancer detection sensitivity and specificity by up to 20% and 12%, respectively.
Conclusions: The QF threshold is effective in stratifying spectra in terms of spectral quality and the observed false negatives and false positives can be linked to limitations of qualitative spectral quality assessment.
Raman spectroscopy is an optical technique that can assess a sample’s molecular content by probing its vibrational modes and has been used over the last decades to diagnose multiple types of cancer. The standard method used to build the classification models, based on machine learning algorithms, is the source of two majors limitations: the small size of the collected training datasets and the issue of portability of statistical models across imaging systems and medical centers. Model portability can be adressed by using a spectrum processing method that totally removes the hardware influence from the processed Raman measurements. We focus here on the results of two experiments conducted to evaluate the reproductibility of Raman measurements made with nine different point-probe systems. For the first experiment, we used a nylon phantom to assess inter-systems differences and applied the data processing method which lowered the inter-systems deviation for the processed nylon peaks under 3%. Furthermore, system #1 was used in vivo in a human brain surgery to acquire 15 Raman measurements from normal and tumor tissue. We evaluated the deviation between classes and found that it was superior to the 3% inter-systems reproductibility for 10 Raman peaks associated with proteins, lipids and nucleic acids. The second experiment was done with the system #1 as a master system and systems #2 to #9 as slave systems. The master system was used to build a Support Vector Machine classification model to discriminate white matter from grey matter on fixed ex vivo monkey brain slices. The model was exported from master to slaves performing a diagnosis accuracy consistently over 95%. The reported results indicate the possibility to succesfully export statistical model from one system to another and to greatly increase the size of dataset using multiple imaging systems.
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