Raman spectroscopy is a non-destructive method providing information about the chemical structure, phase, crystallinity, and interactions of molecules in different samples. Although Raman spectroscopy has been around for nearly 100 years, its use was primarily restricted to analytical chemistry. However, the recent advances in Raman spectroscopy, including stimulated Raman scattering and its integration with high-resolution microscopes, have allowed the utilization of Raman spectroscopy for the analysis of single cells and tissues, opening a new avenue for modern pathology.1,2
Raman spectroscopy unveils the chemical fingerprint of a sample based on how light interacts with chemical bonds
During Raman spectroscopy, a high-intensity laser source is used to emit light, which is scattered upon interacting with a molecule. The vast majority of the scattered light has the same wavelength as the laser source and does not provide information on the sample composition. Nevertheless, some scattered photons have different wavelengths due to energy loss or gain, providing insight into the chemical composition of a sample—this phenomenon is known as Raman scattering or Raman effect.3
A typical output of Raman spectroscopy is a graph of intensity peaks representing light scattered at different wavelengths due to molecular bond vibrations. This spectrum provides insight into the chemical profile of a sample and, thus, can be used to characterize a sample or discern different types of samples (e.g., physiological versus pathological). When combined with imaging systems, Raman spectroscopy can provide maps or images showing the distribution of individual chemical components within a sample.4
Using Raman spectroscopy for histopathology analysis: Stimulated Raman histology
The use of Raman microscopes combining a Raman spectrometer and an optical microscope renders Raman scattering-based histopathological analyses simple and fast and provides a spatial resolution ranging between 0.5 and 1 μm. Moreover, the development of confocal Raman microscopes has enabled the analysis of complex tissues at the cellular or subcellular level with submicron spatial resolutions.5
Stimulated Raman histology (SRH) is a label-free method to generate high-resolution digital histology images from unprocessed tissue specimens. SRH combines stimulated Raman scattering and second harmonic generation, overcoming the need for laborious and time-consuming traditional histopathology techniques.
By detecting scattered laser light, SRH can identify cancer cells infiltrating healthy tissues, as well as uncover key histological and diagnostic features that are often missed by standard histological analysis methods. Furthermore, machine learning algorithms have been developed to automate SRH image analysis, further accelerating the process and minimizing the risk of misdiagnosis due to human error.
In a recent study comparing SRH with standard H&E staining, Sarri et al. showed that the two methods provided a similar diagnostic performance when used on human cryogenic slides from the gastrointestinal tract and freshly excised biopsies from endoscopic surgery.6
Additionally, confocal Raman microscopy imaging enables comprehensive label-free characterization of the structure and composition of the extracellular matrix in tissue sections, facilitating disease diagnosis and monitoring.7
Stimulated Raman histology can aid surgical decision making by accelerating intraoperative diagnosis
SRH has proved to be particularly useful for intraoperative tissue analysis, which is key to rapid and accurate clinical decision making while still in the operating theater. Recent evidence suggests that when combined with artificial intelligence algorithms, SRH accurately uncovers the histologic characteristics of brain tumor tissues and detects peritoneal metastasis, and can, therefore, be used to help guide surgical decision making.6,8,9
Sarri et al. showed that SRH can be used intraoperatively to detect peritoneal metastases. When applied on freshly excised unprocessed biopsy samples, SRH provided similar information to conventional H&E staining, though requiring substantially less time.6
By combining optical imaging with a deep convolutional neural network, Hollon et al. showed that the diagnostic accuracy of SRH was similar to that of traditional histologic techniques (94.6% for SRH versus 93.9% for standard histologic methods). Notably, SRH was significantly faster than routine histologic techniques, facilitating brain tumor diagnosis in less than 3 minutes and proving to be a promising method for near real-time intraoperative diagnosis of brain tumors.8,10
“As surgeons, we’re limited to acting on what we can see; this technology allows us to see what would otherwise be invisible, to improve speed and accuracy in the OR, and reduce the risk of misdiagnosis,” said Daniel A. Orringer, MD, the corresponding author of the study.11 “With this imaging technology, cancer operations are safer and more effective than ever before.”
The robustness of SRH in intraoperatively detecting brain tumors was confirmed by a recent prospective blinded study, where SRH provided similar diagnostic accuracy to frozen section evaluation though requiring significantly less time.12 Moreover, SRH can be used to identify tissue boundaries during the excision of invasive skull base tumors and thereby maintain critical anatomic structures of the brain intact.13,14
Advantages of Raman histology over routine H&E staining
Raman spectrometry provides both qualitative and quantitative information. In addition to chemical and molecular characterization of tissues based on Raman spectra, the intensity of a spectrum (i.e., peak heights) is directly proportional to the concentration of different molecules; hence, SRH could, in theory, shed light on the levels of metabolites associated with a particular disease or genetic mutation (e.g., 2-hydroxyglutarate accumulation in IDH-mutated gliomas).8
Another key advantage of Raman spectroscopy over traditional H&E staining is its reproducibility. H&E staining is often hard to standardize, and its quality may vary depending on the staining protocol, the reagents used, and tissue preparation methods. Such staining variations can complicate diagnosis and compromise patient care. Compared with H&E staining, Raman spectrum interpretation is less prone to errors and observer bias, and, when combined with machine learning, could potentially overcome the requirement for expert pathologists.9
In contrast to H&E staining, SRH is a stain-free, non-destructive technique preserving tissue integrity for downstream in vivo and molecular analyses. As it does not entail sample preparation and staining, SRH overcomes discrepancies due to variations in staining protocols, reagent quality, and tissue preparation procedures.8,15
Importantly, SRH is a rapid technique, providing a good quality spectrum within seconds; therefore, Raman spectroscopy can be used for near real-time intraoperative tumor diagnosis. In contrast, traditional intraoperative pathology procedures based on H&E staining can entail long turnaround times, leading to delays in diagnosis and potentially impacting treatment outcomes.8,16
Drawbacks of Raman histology
Even though Raman spectroscopy can yield a good quality spectrum within a few seconds, several parameters affect the time required for Raman spectral image acquisition. These factors include the number of pixels, the spectral quality required, the number of Raman spectra, and the size of the image area. Thus, depending on these factors, Raman spectral image acquisition sometimes takes several days.
Moreover, the Raman spectral resolution can be affected by numerous factors, including the laser wavelength, the detector, the spectrometer focal length, and the diffraction grating. Concerns regarding laser safety and performance are also significant issues hampering the clinical translation of SRH.1,15
Despite encouraging data from proof-of-concept studies, future prospective randomized controlled studies are warranted to comprehensively evaluate the clinical value of Raman histology. Ongoing research efforts focus on developing strategies to automate and streamline SRH-mediated intraoperative diagnosis, with artificial intelligence and machine learning-assisted image classification being among the most promising strategies.
Most importantly, the clinical implementation of SRH image analysis of surgical specimens requires multi-disciplinary teams consisting of surgeons, pathologists, statisticians, IT scientists, and medical device experts. Only with the establishment of multi-disciplinary partnerships will we be able to harness the full potentials of SRH to accelerate clinical decision making based on intraoperative histology and advance surgical oncology, among other fields.
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11. New Laser-Based Imaging System & Artificial Intelligence Algorithm, Used in Conjunction, Accurately Identify Brain Tumors. NYU Langone Health Press Release. 2020.
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