Raman spectroscopic analysis of the molecular composition of oral cavity squamous cell carcinoma and healthy tongue tissue

Raman spectroscopy and U-Net deep neural network in antiresorptive drug-related osteonecrosis of the jaw

OBJECTIVE

Application of an optical method for the identification of antiresorptive drug-related osteonecrosis of the jaw (ARONJ).

METHODS

We introduce shifted-excitation Raman difference spectroscopy followed by U-Net deep neural network refinement to determine bone tissue viability. The obtained results are validated through established histological methods.

RESULTS

Discrimination of osteonecrosis from physiological tissues was evaluated at 119 distinct measurement loci in 40 surgical specimens from 28 patients. Mean Raman spectra were refined from 11,900 raw spectra, and characteristic peaks were assigned to their respective molecular origin. Then, following principal component and linear discriminant analyses, osteonecrotic lesions were distinguished from physiological tissue entities, such as viable bone, with a sensitivity, specificity, and overall accuracy of 100%. Moreover, bone mineral content, quality, maturity, and crystallinity were quantified, revealing an increased mineral-to-matrix ratio and decreased carbonate-to-phosphate ratio in ARONJ lesions compared to physiological bone.

CONCLUSION

The results demonstrate feasibility with high classification accuracy in this collective. The differentiation was determined by the spectral features of the organic and mineral composition of bone. This merely optical, noninvasive technique is a promising candidate to ameliorate both the diagnosis and treatment of ARONJ in the future.

Raman micro-spectroscopy as a tool to study immunometabolism

In the past two decades, immunometabolism has emerged as a crucial field, unraveling the intricate molecular connections between cellular metabolism and immune function across various cell types, tissues, and diseases. This review explores the insights gained from studies using the emerging technology, Raman micro-spectroscopy, to investigate immunometabolism. Raman micro-spectroscopy provides an exciting opportunity to directly study metabolism at the single cell level where it can be combined with other Raman-based technologies and platforms such as single cell RNA sequencing. The review showcases applications of Raman micro-spectroscopy to study the immune system including cell identification, activation, and autoimmune disease diagnosis, offering a rapid, label-free, and minimally invasive analytical approach. The review spotlights three promising Raman technologies, Raman-activated cell sorting, Raman stable isotope probing, and Raman imaging. The synergy of Raman technologies with machine learning is poised to enhance the understanding of complex Raman phenotypes, enabling biomarker discovery and comprehensive investigations in immunometabolism. The review encourages further exploration of these evolving technologies in the rapidly advancing field of immunometabolism.

Development of a coaxial DCF-GRIN fiberoptic Raman probe for enhancing in vivo epithelial tissue Raman measurements

We report on the development of a novel, to the best of our knowledge, coaxial double-clad-fiber (DCF) and graded-index (GRIN) fiberoptic Raman probe for enhancing epithelial tissue Raman measurements in vivo. The ultra-thin (140 µm outer diameter) DCF-GRIN fiberoptic Raman probe is designed and fabricated with an efficient coaxial optical configuration, whereby a GRIN fiber is spliced onto the DCF to enhance both the excitation/collection efficiency and depth-resolved selectivity. We demonstrate that the DCF-GRIN Raman probe can be used to acquire high-quality in vivo Raman spectra from various oral tissues (e.g., buccal mucosa, labial mucosa, gingiva, mouth floor, palate, and tongue) covering both the fingerprint (800-1800 cm^-1) and high-wavenumber (2800-3600 cm^-1) regions within sub-seconds. The subtle biochemical differences between different epithelial tissues in the oral cavity can also be detected with high sensitivity, suggesting the potential of the DCF-GRIN fiberoptic Raman probe for in vivo diagnosis and characterization in epithelial tissue.

Is Computer-Assisted Tissue Image Analysis the Future in Minimally Invasive Surgery? A Review on the Current Status of Its Applications

Purpose: Computer-assisted tissue image analysis (CATIA) enables an optical biopsy of human tissue during minimally invasive surgery and endoscopy. Thus far, it has been implemented in gastrointestinal, endometrial, and dermatologic examinations that use computational analysis and image texture feature systems. We review and evaluate the impact of in vivo optical biopsies performed by tissue image analysis on the surgeon’s diagnostic ability and sampling precision and investigate how operation complications could be minimized. Methods: We performed a literature search in PubMed, IEEE, Xplore, Elsevier, and Google Scholar, which yielded 28 relevant articles. Our literature review summarizes the available data on CATIA of human tissues and explores the possibilities of computer-assisted early disease diagnoses, including cancer. Results: Hysteroscopic image texture analysis of the endometrium successfully distinguished benign from malignant conditions up to 91% of the time. In dermatologic studies, the accuracy of distinguishing nevi melanoma from benign disease fluctuated from 73% to 81%. Skin biopsies of basal cell carcinoma and melanoma exhibited an accuracy of 92.4%, sensitivity of 99.1%, and specificity of 93.3% and distinguished nonmelanoma and normal lesions from benign precancerous lesions with 91.9% and 82.8% accuracy, respectively. Gastrointestinal and endometrial examinations are still at the experimental phase. Conclusions: CATIA is a promising application for distinguishing normal from abnormal tissues during endoscopic procedures and minimally invasive surgeries. However, the efficacy of computer-assisted diagnostics in distinguishing benign from malignant states is still not well documented. Prospective and randomized studies are needed before CATIA is implemented in clinical practice.

Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy

The aim of this study was to investigate the clinical potential of Raman spectroscopy (RS) in detecting oral squamous cell carcinoma (OSCC) in tumor and healthy tissues in surgical resection specimens during surgery. Raman experiments were performed on cryopreserved specimens from patients with OSCC. Univariate and multivariate analysis was performed based on the fingerprint region (700–1800 cm−1) of the Raman spectra. One hundred thirty-one ex-vivo Raman experiments were performed on 131 surgical resection specimens obtained from 67 patients. The principal component analysis (PCA) and partial least square (PLS) methods with linear discriminant analysis (LDA) were applied on an independent validation dataset. Both models were able to differentiate between the tissue types, but PLS–LDA showed 100% accuracy, sensitivity, and specificity. In this study, Raman measurements of fresh resection tissue specimens demonstrated that OSCC had significantly higher nucleic acid, protein, and several amino acid contents than adjacent healthy tissues. The specific spectral information obtained in this study can be used to develop an in vivo Raman spectroscopic method for the tumor-free resection boundary during surgery.

Mapping of a Subgingival Dual-Species Biofilm Model Using Confocal Raman Microscopy

Techniques for continuously monitoring the formation of subgingival biofilm, in relation to the determination of species and their accumulation over time in gingivitis and periodontitis, are limited. In recent years, advancements in the field of optical spectroscopic techniques have provided an alternative for analyzing three-dimensional microbiological structures, replacing the traditional destructive or biofilm staining techniques. In this work, we have demonstrated that the use of confocal Raman spectroscopy coupled with multivariate analysis provides an approach to spatially differentiate bacteria in an in vitro model simulating a subgingival dual-species biofilm. The present study establishes a workflow to evaluate and differentiate bacterial species in a dual-species in vitro biofilm model, using confocal Raman microscopy (CRM). Biofilm models of Actinomyces denticolens and Streptococcus oralis were cultured using the “Zürich in vitro model” and were analyzed using CRM. Cluster analysis was used to spatially differentiate and map the biofilm model over a specified area. To confirm the clustering of species in the cultured biofilm, confocal laser scanning microscopy (CLSM) was coupled with fluorescent in vitro hybridization (FISH). Additionally, dense bacteria interface area (DBIA) samples, as an imitation of the clusters in a biofilm, were used to test the developed multivariate differentiation model. This confirmed model was successfully used to differentiate species in a dual-species biofilm and is comparable to morphology. The results show that the developed workflow was able to identify main clusters of bacteria based on spectral “fingerprint region” information from CRM. Using this workflow, we have demonstrated that CRM can spatially analyze two-species in vitro biofilms, therefore providing an alternative technique to map oral multi-species biofilm models.

Diagnostic accuracy of in vivo early tumor imaging from probe-based confocal laser endomicroscopy versus histologic examination in head and neck squamous cell carcinoma

OBJECTIVES

Probe-based confocal laser endomicroscopy (pCLE) is a noninvasive and real-time imaging technique allowing acquisition of in situ images of the tissue microarchitecture during oral surgery. We aimed to assess the diagnostic performance of pCLE combined with patent blue V (PB) in improving the management of early oral cavity, oro/hypopharyngeal, and laryngeal cancer by imaging squamous cell carcinoma in vivo.

MATERIALS AND METHODS

The prospective study enrolled 44 patients with early head and neck lesions. All patients underwent white-light inspection or panendoscopy depending on the lesion's location, followed by pCLE imaging of the tumor core and its margins after topical application of PB. Each zone imaged by pCLE was interpreted at distance of the exam by three pathologists blinded to final histology.

RESULTS

Most imaged zones could be presented to pathologists; the final sensitivity and specificity of pCLE imaging in head and neck cancers was 73.2-75% and 30-57.4%, respectively. During imaging, head and neck surgeons encountered some challenges that required resolving, such as accessing lesions with the flexible optical probe, achieving sufficiently precise imaging on the targeted tissues, and heterogeneous tissue staining by fluorescent dye.

CONCLUSION

Final sensitivity scores were reasonable but final specificity scores were low. pCLE zones used to calculate specificity were acquired in areas of tumor margins, and the poor quality of the images acquired in these areas explains the final low specificity scores.

CLINICAL RELEVANCE

Practical adjustments and technical training are needed to analyze head and neck lesions in various anatomical sites in real-time by pCLE.

An overview of the current clinical status of optical imaging in head and neck cancer with a focus on Narrow Band imaging and fluorescence optical imaging

Early and accurate identification of head and neck squamous cell carcinoma (HNSCC) is important to improve treatment outcomes and prognosis. New optical imaging techniques may assist in both the diagnostic process as well as in the operative setting by real-time visualization and delineation of tumor. Narrow Band Imaging (NBI) is an endoscopic technique that uses blue and green light to enhance mucosal and submucosal blood vessels, leading to better detection of (pre)malignant lesions showing aberrant blood vessel patterns. Fluorescence optical imaging makes use of near-infrared fluorescent agents to visualize and delineate HNSCC, resulting in fewer positive surgical margins. Targeted fluorescent agents, such as fluorophores conjugated to antibodies, show the most promising results. The aim of this review is: (1) to provide the clinical head and neck surgeon an overview of the current clinical status of various optical imaging techniques in head and neck cancer; (2) to provide an in-depth review of NBI and fluorescence optical imaging, as these techniques have the highest potential for clinical implementation; and (3) to describe future improvements and developments within the field of these two techniques.

Characterization of Ex Vivo Nonmelanoma Skin Tissue Using Raman Spectroscopy

Raman spectroscopy has shown great potential in detecting nonmelanoma skin cancer accurately and quickly; however, little direct evidence exists on the sensitivity of measurements to the underlying anatomy. Here, we aimed to correlate Raman measurements directly to the underlying tissue anatomy. We acquired Raman spectra of ex vivo skin tissue from 25 patients undergoing Mohs surgery with a fiber probe. We utilized a previously developed biophysical model to extract key biomarkers in the skin from the Raman spectra. We then examined the correlations between the biomarkers and the major skin structures (including the dermis, sebaceous glands, hair follicles, fat, and two types of nonmelanoma skin cancer—basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)). SCC had a significantly different concentration of keratin, collagen, and nucleic acid than normal structures, while ceramide differentiated BCC from normal structures. Our findings identified the key proteins, lipids, and nucleic acids that discriminate nonmelanoma tumors and healthy skin using Raman spectroscopy. These markers may be promising surgical guidance tools for detecting tumors in resection margins.

Biomedical applications of vibrational spectroscopy: Oral cancer diagnostics

Vibrational spectroscopy, based on either infrared absorption or Raman scattering, has attracted increasing attention for biomedical applications. Proof of concept explorations for diagnosis of oral potentially malignant disorders and cancer are reviewed, and recent advances critically appraised. Specific examples of applications of Raman microspectroscopy for analysis of histological, cytological and saliva samples are presented for illustrative purposes, and the future prospects, ultimately for routine, chairside in vivo screening are discussed.

The Potential of Raman Spectroscopy in the Diagnosis of Dysplastic and Malignant Oral Lesions

Simple Summary

Raman spectroscopy, a light scattering technique that provides the biochemical fingerprint of a sample, was used on samples taken from patients with cancer and precancerous lesions. This information was then used to build a classifier to identify cancer and the precancerous phases. The ability to distinguish cancerous tissue from normal and precancerous tissue is diagnostically crucial as it can alter the patients’ prognosis and management. Moreover, as cellular changes are often present at the tumour margin, the ability to distinguish these changes from cancer can help in preserving more of the tissue and maintaining aesthetics and functionality for the patient.

Abstract

Early diagnosis, treatment and/or surveillance of oral premalignant lesions are important in preventing progression to oral squamous cell carcinoma (OSCC). The current gold standard is through histopathological diagnosis, which is limited by inter- and intra-observer errors and sampling errors. The objective of this work was to use Raman spectroscopy to discriminate between benign, mild, moderate and severe dysplasia and OSCC in formalin fixed paraffin preserved (FFPP) tissues. The study included 72 different pathologies from which 17 were benign lesions, 20 mildly dysplastic, 20 moderately dysplastic, 10 severely dysplastic and 5 invasive OSCC. The glass substrate and paraffin wax background were digitally removed and PLSDA with LOPO cross-validation was used to differentiate the pathologies. OSCC could be differentiated from the other pathologies with an accuracy of 70%, while the accuracy of the classifier for benign, moderate and severe dysplasia was ~60%. The accuracy of the classifier was lowest for mild dysplasia (~46%). The main discriminating features were increased nucleic acid contributions and decreased protein and lipid contributions in the epithelium and decreased collagen contributions in the connective tissue. Smoking and the presence of inflammation were found to significantly influence the Raman classification with respective accuracies of 76% and 94%.

Optical diagnosis of oral cavity lesions by label-free Raman spectroscopy

Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers and frequently preceded by non-malignant lesions. Using Shifted-Excitation Raman Difference Spectroscopy (SERDS), principal component and linear discriminant analysis in native tissue specimens, 9500 raw Raman spectra of OSCC, 4300 of non-malignant lesions and 4200 of physiological mucosa were evaluated. Non-malignant lesions were distinguished from physiological mucosa with a classification accuracy of 95.3% (95.4% sensitivity, 95.2% specificity, area under the curve (AUC) 0.99). Discriminating OSCC from non-malignant lesions showed an accuracy of 88.4% (93.7% sensitivity, 76.7% specificity, AUC 0.93). OSCC was identified against physiological mucosa with an accuracy of 89.8% (93.7% sensitivity, 81.0% specificity, AUC 0.90). These findings underline the potential of SERDS for the diagnosis of oral cavity lesions.

Raman microspectroscopic study for the detection of oral field cancerisation using brush biopsy samples

Field cancerisation (FC) is potentially an underlying cause of poor treatment outcomes of oral squamous cell carcinoma (OSCC). To explore the phenomenon using Raman microspectroscopy, brush biopsies from the buccal mucosa, tongue, gingiva and alveolus of healthy donors (n = 40) and from potentially malignant lesions (PML) of Dysplasia Clinic patients (n = 40) were examined. Contralateral normal samples (n = 38) were also collected from the patients. Raman spectra were acquired from the nucleus and cytoplasm of each cell, and subjected to partial least squares-discriminant analysis (PLS-DA). High discriminatory accuracy for donor and PML samples was achieved for both cytopalmic and nuclear data sets. Notably, contralateral normal (patient) samples were also accurately discriminated from donor samples and contralateral normal samples from patients with multiple lesions showed a similar spectral profile to PML samples, strongly indicating a FC effect. These findings support the potential of Raman microspectroscopy as a screening tool for PML using oral exfoliated cells.

Mass spectrometry-based lipidomics of oral squamous cell carcinoma tissue reveals aberrant cholesterol and glycerophospholipid metabolism - A Pilot study

Lipid metabolic reprogramming is one hallmark of cancer. Lipid metabolism is regulated by numerous enzymes, many of which are targeted by several drugs on the market. We aimed to characterize the lipid alterations in oral squamous cell carcinoma (OSCC) as a basis for understanding its lipid metabolism, thus identifying potential therapeutic targets. We compared lipid species, classes, and glycerophospholipid (GPL) fatty acid species between paired tumor tissue and healthy oral tongue mucosa samples from 10 OSCC patients using a QExactive mass spectrometer. After filtering the 1370 lipid species identified, we analyzed 349 species: 71 were significantly increased in OSCC. The GPL metabolism pathway was most represented by the lipids differing in OSCC (P = .005). Cholesterol and the GPLs phosphatidylcholines, phosphatidylethanolamines, and phosphatidylinositols were most significantly increased in OSCC tissue (FC 1.8, 2.0, 2.1, and 2.3 and, P = .003, P = .005, P = .002, P = .007). In conclusion, we have demonstrated a shift in the lipid metabolism in these OSCC samples by characterizing the detailed landscape. Predominantly, cholesterol and GPL metabolism were altered, suggesting that interactions with sterol regulatory binding proteins may be involved. The FA composition changes of the GPLs suggest increased de novo lipogenesis.

Confocal Raman microscopy to identify bacteria in oral subgingival biofilm models

The study of oral disease progression, in relation to the accumulation of subgingival biofilm in gingivitis and periodontitis is limited, due to either the ability to monitor plaque in vitro. When compared, optical spectroscopic techniques offer advantages over traditional destructive or biofilm staining approaches, making it a suitable alternative for the analysis and continued development of three-dimensional structures. In this work, we have developed a confocal Raman spectroscopy analysis approach towards in vitro subgingival plaque models. The main objective of this study was to develop a method for differentiating multiple oral subgingival bacterial species in planktonic and biofilm conditions, using confocal Raman microscopy. Five common subgingival bacteria (Fusobacterium nucleatum, Streptococcus mutans, Veillonella dispar, Actinomyces naeslundii and Prevotella nigrescens) were used and differentiated using a 2-way orthogonal Partial Least Square with Discriminant Analysis (O2PLS-DA) for the collected spectral data. In addition to planktonic growth, mono-species biofilms cultured using the 'Zürich Model' were also analyzed. The developed method was successfully used to predict planktonic and mono-species biofilm species in a cross validation setup. The results show differences in the presence and absence of chemical bands within the Raman spectra. The O2PLS-DA model was able to successfully predict 100% of all tested planktonic samples and 90% of all mono-species biofilm samples. Using this approach we have shown that Confocal Raman microscopy can analyse and predict the identity of planktonic and mono-species biofilm species, thus enabling its potential as a technique to map oral multi-species biofilm models.

Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning

This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.

Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection

Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer.

Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy

With deep convolutional neural networks and fiber optic Raman spectroscopy, this study presents a novel classification method that discriminates tongue squamous cell carcinoma (TSCC) from non-tumorous tissue. To achieve this purpose, 24 tissues spectral data were first collected from 12 patients who had undergone a surgical resection due to the tongue squamous cell carcinomas. Then 6 blocks with each block including 1 convolutional layer and 1 max-pooling layer are used to extract the nonlinear feature representations from Raman spectra. The derived features form a representative vector, which is fed into a fully-connected network for performing classification task. Experimental results demonstrated that the proposed method achieved high sensitivity (99.31%) and specificity (94.44%). To show the superiority for the ConvNets classifier, comparison results with the state-of-the-art methods show it had a competitive classification accuracy. Moreover, these promising results may pave the way to apply the deep ConvNets model in the fiber optic Raman instrument for intra-operative evaluation of TSCC resection margins and improve patient survival.

Biophysical basis of skin cancer margin assessment using Raman spectroscopy

Achieving adequate margins during tumor margin resection is critical to minimize the recurrence rate and maximize positive patient outcomes during skin cancer surgery. Although Mohs micrographic surgery is by far the most effective method to treat nonmelanoma skin cancer, it can be limited by its inherent required infrastructure, including time-consuming and expensive on-site histopathology. Previous studies have demonstrated that Raman spectroscopy can accurately detect basal cell carcinoma (BCC) from surrounding normal tissue; however, the biophysical basis of the detection remained unclear. Therefore, we aim to explore the relevant Raman biomarkers to guide BCC margin resection. Raman imaging was performed on skin tissue samples from 30 patients undergoing Mohs surgery. High correlations were found between the histopathology and Raman images for BCC and primary normal structures (including epidermis, dermis, inflamed dermis, hair follicle, hair shaft, sebaceous gland and fat). A previously developed model was used to extract the biochemical changes associated with malignancy. Our results showed that BCC had a significantly different concentration of nucleus, keratin, collagen, triolein and ceramide compared to normal structures. The nucleus accounted for most of the discriminant power (90% sensitivity, 92% specificity - balanced approach). Our findings suggest that Raman spectroscopy is a promising surgical guidance tool for identifying tumors in the resection margins.