Rapid Fiber-optic Raman Spectroscopy for Real-Time In Vivo Detection of Gastric Intestinal Metaplasia during Clinical Gastroscopy

Toward cancer detection by label-free microscopic imaging in oncological surgery: Techniques, instrumentation and applications

This review examines the clinical application of label-free microscopy and spectroscopy, which are based on optical signals emitted by tissue components. Over the past three decades, a variety of techniques have been investigated with the aim of developing an in situ histopathology method that can rapidly and accurately identify tumor margins during surgical procedures. These techniques can be divided into two groups. One group encompasses techniques exploiting linear optical signals, and includes infrared and Raman microspectroscopy, and autofluorescence microscopy. The second group includes techniques based on nonlinear optical signals, including harmonic generation, coherent Raman scattering, and multiphoton autofluorescence microscopy. Some of these methods provide comparable information, while others are complementary. However, all of them have distinct advantages and disadvantages due to their inherent nature. The first part of the review provides an explanation of the underlying physics of the excitation mechanisms and a description of the instrumentation. It also covers endomicroscopy and data analysis, which are important for understanding the current limitations in implementing label-free techniques in clinical settings. The second part of the review describes the application of label-free microscopy imaging to improve oncological surgery with focus on brain tumors and selected gastrointestinal cancers, and provides a critical assessment of the current state of translation of these methods into clinical practice. Finally, the potential of confocal laser endomicroscopy for the acquisition of autofluorescence is discussed in the context of immediate clinical applications.

Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy

This study aimed to identify biomolecular differences between benign gastric tissues (gastritis/intestinal metaplasia) and gastric adenocarcinoma and to evaluate the diagnostic power of Raman spectroscopy-based machine learning in gastric adenocarcinoma. Raman spectroscopy-based machine learning was applied in real-time during endoscopy in 19 patients (aged 51-85 years) with high-risk for gastric adenocarcinoma. Raman spectra were captured from suspicious lesions and adjacent normal mucosa, which were biopsied for matched histopathologic diagnosis. Spectral data were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA) with leave-one-out cross-validation (LOOCV) to develop a machine learning model for diagnosing gastric adenocarcinoma. High-quality spectra (800-3300 cm⁻¹) revealed distinct patterns: adenocarcinoma tissues had higher intensities below 3150 cm⁻¹, while benign tissues exhibited higher intensities between 3150 and 3290 cm⁻¹ (p < 0.001). The model achieved diagnostic accuracy, sensitivity, specificity, and AUC values of 0.905, 0.942, 0.787, and 0.957, respectively. Biochemical correlations suggested adenocarcinoma tissues had increased protein (e.g., phenylalanine), reduced lipids, and lower water content compared to benign tissues. This study highlights the potential of Raman spectroscopy with machine learning as a real-time diagnostic tool for gastric adenocarcinoma. Further validation could establish this technique as a non-invasive, accurate method to aid clinical decision-making during endoscopy.

Feasibility Study of Label-Free Raman Spectroscopy for Parathyroid Gland Identification

We aim to evaluate the feasibility of Raman spectroscopy for parathyroid gland (PG) identification during thyroidectomy. Using a novel side-viewing handheld Raman probe, a total of 324 Raman spectra of four tissue types (i.e., thyroid, lymph node, PG, and lipid) commonly encountered during thyroidectomy were rapidly (< 3 s) acquired from 80 tissue sites (thyroid [n = 10], lymph node [n = 10], PG [n = 40], lipid [n = 20]) of 10 euthanized Wistar rats. Two partial least-squares (PLS)-discriminant analysis (DA) detection models were developed, differentiating the lipid and nonlipid (i.e., thyroid, lymph node, and PG) tissues with an accuracy of 100%, and PG, lymph node, and thyroid could be detected with an accuracy of 98.4%, 93.9%, and 95.4% respectively. This work demonstrates the feasibility of Raman spectroscopy technique for PG identification and protection during thyroidectomy at the molecular level.

Unveiling the Molecular Secrets: A Comprehensive Review of Raman Spectroscopy in Biological Research

Raman spectroscopy has been proven to be a fast, convenient, and nondestructive technique for advancing our understanding of biological systems. The Raman effect originates from the inelastic scattering of light which directly probe vibration/rotational states in biological molecules and materials. Despite numerous advantages over infrared spectroscopy and continuous technical as well as operational improvement in Raman spectroscopy, an advanced development of the device and more applications have become possible. In this review, we explore the principles, techniques, and myriad applications of Raman spectroscopy in the realm of biology. We begin by providing an overview of Raman spectroscopy, highlighting its significance in unraveling the complexities of biological research. The focus of this review is on Raman spectroscopy concepts and methods, clarifying the fundamentals of Raman scattering and spectral interpretation. The review also highlights the key experimental considerations for productive biological applications. We explore the broad range of Raman applications including molecular structure, biomolecular composition, disease detection, and medication discovery. The Raman imaging and mapping can also be used to visualize biological samples at the molecular level. Raman spectroscopy is still developing, giving fresh insights and remedies, from biosensing to its use in tissue engineering and regenerative medicine. This review sheds light on the past, present, and future of Raman spectroscopy; it also highlights promising directions of future research developments and serves as a thorough resource for all researchers.

Raman fiber-optic probe for rapid diagnosis of gastric and esophageal tumors with machine learning analysis or similarity assessments: a comparative study

Gastric and esophageal cancers, the predominant forms of upper gastrointestinal malignancies, contribute significantly to global cancer mortality. Routine detection methods, including medical imaging, endoscopic examination, and pathological biopsy, often suffer from drawbacks such as low sensitivity and laborious and complex procedures. Raman spectroscopy is a non-invasive and label-free optical technique that provides highly sensitive biomolecular information to facilitate effective tumor identification. In this work, we report the use of fiber-optic Raman spectroscopy for the accurate and rapid diagnosis of gastric and esophageal cancers. Using a database of 14,000 spectra from 140 ex vivo tissue pieces of both tumor and normal tissue samples, we compare the random forest (RF) and our established Euclidean distance Raman spectroscopy (EDRS) model. The RF analysis achieves a sensitivity of 85.23% and an accuracy of 83.05% in diagnosing gastric tumors. The EDRS algorithm with improved diagnostic transparency further increases the sensitivity to 92.86% and accuracy to 89.29%. When these diagnostic protocols are extended to esophageal tumors, the RF and EDRS models achieve accuracies of 71.27% and 93.18%, respectively. Finally, we demonstrate that fewer than 20 spectra are sufficient to achieve good Raman diagnostic accuracy for both tumor tissues. This optimizes the balance between acquisition time and diagnostic performance. Our work, although conducted on ex vivo tissue models, offers valuable insights for in vivo in situ endoscopic Raman diagnosis of gastric and esophageal cancer lesions in the future. Our study provides a robust, rapid, and convenient method as a new paradigm in in vivo endoscopic medical diagnostics that integrates spectroscopic techniques and a Raman probe for detecting upper gastrointestinal malignancies.

Integrating multi-spectral imaging and Raman spectroscopy for in vivo endoscopic assessment of rat intestinal tract

An integrated system for in vivo multi-spectral imaging (MSI) and Raman spectroscopy was developed to understand the external morphology and internal molecular information of biological tissues. The achieved MSI images were reconstructed by eighteen separated images from 400 nm to 760 nm, whose illumination bands were selected with six tri-channel band filters. Based on the spectral analysis algorithms, the spatial distribution patterns of blood volume, blood oxygen content and tissue scatterer volume fraction were visualized. In vivo Raman spectral measurements were executed by inserting specially designed optical probe into instrumental channel of endoscope. By this way, the molecular composition at selected sampling points could be identified with its fingerprint spectral information under the guidance of molecular imaging modality. Therefore, both structural and compositional features of intestinal membrane could be addressed without labeling and continuously. The achieved results testified that our presented methodology reveals insights not easily extracted from either MSI or Raman spectroscopy individually, which brings the enrichment of biological and chemical meanings for future in vivo studies.

Toward noncontact macroscopic imaging of multiple cancers using multi-spectral inelastic scattering detection

Here we introduce a Raman spectroscopy approach combining multi-spectral imaging and a new fluorescence background subtraction technique to image individual Raman peaks in less than 5 seconds over a square field-of-view of 1-centimeter sides with 350 micrometers resolution. First, human data is presented supporting the feasibility of achieving cancer detection with high sensitivity and specificity - in brain, breast, lung, and ovarian/endometrium tissue - using no more than three biochemically interpretable biomarkers associated with the inelastic scattering signal from specific Raman peaks. Second, a proof-of-principle study in biological tissue is presented demonstrating the feasibility of detecting a single Raman band - here the CH2/CH3 deformation bands from proteins and lipids - using a conventional multi-spectral imaging system in combination with the new background removal method. This study paves the way for the development of a new Raman imaging technique that is rapid, label-free, and wide field.

Label-Free Optical Technologies to Enhance Noninvasive Endoscopic Imaging of Early-Stage Cancers

White light endoscopic imaging allows for the examination of internal human organs and is essential in the detection and treatment of early-stage cancers. To facilitate diagnosis of precancerous changes and early-stage cancers, label-free optical technologies that provide enhanced malignancy-specific contrast and depth information have been extensively researched. The rapid development of technology in the past two decades has enabled integration of these optical technologies into clinical endoscopy. In recent years, the significant advantages of using these adjunct optical devices have been shown, suggesting readiness for clinical translation. In this review, we provide an overview of the working principles and miniaturization considerations and summarize the clinical and preclinical demonstrations of several such techniques for early-stage cancer detection. We also offer an outlook for the integration of multiple technologies and the use of computer-aided diagnosis in clinical endoscopy.

The accuracy of Fiber-Optic Raman Spectroscopy in the detection and diagnosis of head and neck neoplasm in vivo: a systematic review and meta-analysis

Purpose

The aim of this article was to review and collectively assess the published studies of fiber-optic Raman spectroscopy (RS) of the in vivo detection and diagnosis of head and neck carcinomas, and to derive a consensus average of the accuracy, sensitivity and specificity.

Methods

The authors searched four databases, including Ovid-Medline, Ovid-Embase, Cochrane Library, and the China National Knowledge Infrastructure (CNKI), up to February 2023 for all published studies that assessed the diagnostic accuracy of fiber-optic RS in the in vivo detection of head and neck carcinomas. Nonqualifying studies were screened out in accordance with the specified exclusion criteria, and relevant information about the diagnostic performance of fiber-optic RS was excluded. Publication bias was estimated by Deeks' funnel plot asymmetry test. A random effects model was adopted to calculate the pooled sensitivity, specificity and diagnostic odds ratio (DOR). Additionally, the authors conducted a summary receiver operating characteristic (SROC) curve analysis and threshold analysis, reporting the area under the curve (AUC) to evaluate the overall performance of fiber-optic RS in vivo.

Results

Ten studies (including 16 groups of data) were included in this article, and a total of 5365 in vivo Raman spectra (cancer = 1,746; normal = 3,619) were acquired from 877 patients. The pooled sensitivity and specificity of fiber-optic RS of head and neck carcinomas were 0.88 and 0.94, respectively. SROC curves were generated to estimate the overall diagnostic accuracy, and the AUC was 0.96 (95% CI [0.94-0.97]). No significant publication bias was found in this meta-analysis by Deeks' funnel plot asymmetry test. The heterogeneity of these studies was significant; the Q test values of the sensitivity and specificity were 106.23 (P = 0.00) and 64.21 (P = 0.00), respectively, and the I2 index of the sensitivity and specificity were 85.88 (95% CI [79.99-91.77]) and 76.64 (95% CI [65.45-87.83]), respectively.

Conclusion

Fiber-optic RS was demonstrated to be a reliable technique for the in vivo detection of head and neck carcinoma with high accuracy. However, considering the high heterogeneity of these studies, more clinical studies are needed to reduce the heterogeneity, and further confirm the utility of fiber-optic Raman spectroscopy in vivo.

Development of a two-beveled-fiber polarized fiber-optic Raman probe coupled with a ball lens for in vivo superficial epithelial Raman measurements in endoscopy

We report on the development of a two-beveled-fiber polarized (TBFP) fiber-optic Raman probe coupled with a ball lens for in vivo superficial epithelial Raman measurements in endoscopy. The two-beveled fibers positioned symmetrically along a ball lens, in synergy with paired parallel-polarized polarizers integrated between the fibers and the ball lens, maximize the Raman signal excitation and collection from the superficial epithelium where gastrointestinal (GI) precancer arises. Monte Carlo (MC) simulations and two-layer tissue phantom experiments show that the probe developed detects ∼90% of the Raman signal from the superficial epithelium. The suitability of the probe developed for rapid (<3 s) superficial epithelial Raman measurements is demonstrated on fresh swine esophagus, stomach, and colon tissues, followed by their differentiation with high accuracies (92.1% for esophagus [sensitivity: 89.3%, specificity: 93.2%], 94.1% for stomach [sensitivity: 86.2%, specificity: 97.2%], and 94.1% for colon [sensitivity: 93.2%, specificity: 94.7%]). The presented results suggest the great potential of the developed probe for enhancing in vivo superficial epithelial Raman measurements in endoscopy.

Gastric Intestinal Metaplasia: Challenges and the Opportunity for Precision Prevention

GIM is a persistent, premalignant lesion whereby gastric mucosa is replaced by metaplastic mucosa resembling intestinal tissue, arising in the setting of chronic inflammation, particularly in the context of Helicobacter pylori. While the overall rates of progression to gastric adenocarcinoma are low, estimated at from 0.25 to 2.5%, there are features that confer a much higher risk and warrant follow-up. In this review, we collate and summarise the current knowledge regarding the pathogenesis of GIM, and the clinical, endoscopic and histologic risk factors for cancer. We examine the current state-of-practice with regard to the diagnosis and management of GIM, which varies widely in the published guidelines and in practice. We consider the emerging evidence in population studies, artificial intelligence and molecular markers, which will guide future models of care. The ultimate goal is to increase the detection of early gastric dysplasia/neoplasia that can be cured while avoiding unnecessary surveillance in very low-risk individuals.

A novel detection technology for early gastric cancer based on Raman spectroscopy

Despite universal endoscopic screening, early detection of gastric cancer is challenging, led researchers to seek for a novel approach in detecting. Raman spectroscopy measurements as a fingerprint of biochemical structure, enable accurate prediction of gastric lesions non-destructively. This study aimed to evaluate the diagnostic power of Raman spectroscopy in early gastric cancer (EGC), and reveal dynamic biomolecular changes in vitro from normal to EGC. To clarify the biochemical alterations in Correa's cascade, Raman spectra of human normal gastric mucosa, intestinal metaplasia, dysplasia, and adenocarcinoma were compared at tissue and cellular levels based on a self-developed data processing program. For effectively identify EGC, Raman spectroscopy was used combined with multiple machine learning methods, including partial least-squares discriminant analysis (PLS-DA), support vector machine (SVM), and convolutional neural network (CNN) with leave-one-out (LOO) cross validation. A total of 450 Raman spectra were investigated in this study. The upregulation of ν_sym(O-P-O) backbone (p < 0.001) was identified as a favorable factor for the diagnosis of EGC, the area under the ROC curve (AUC) was up to 0.918. In addition, higher levels of lactic acid (p < 0.001), lipids (p < 0.001), phenylalanine (p = 0.002), and carotenoids (p < 0.001) were detected in EGC. Multivariate machine learning methods for diagnosis of EGC based on Raman spectroscopy, the sensitivity, specificity, accuracy, and AUC were 91.0%, 100%, 94.8%, and 95.8% for SVM, and 84.8%, 92.0%, 88.8%, and 95.5% for CNN, respectively. Raman spectroscopy can be used as a powerful tool for detecting EGC while elucidating biomolecular dynamics in tumorigenesis. (Chictr.org.cn, ChiCTR2200060720.).

Raman spectroscopy: A prospective intraoperative visualization technique for gliomas

The infiltrative growth and malignant biological behavior of glioma make it one of the most challenging malignant tumors in the brain, and how to maximize the extent of resection (EOR) while minimizing the impact on normal brain tissue is the pursuit of neurosurgeons. The current intraoperative visualization assistance techniques applied in clinical practice suffer from low specificity, slow detection speed and low accuracy, while Raman spectroscopy (RS) is a novel spectroscopy technique gradually developed and applied to clinical practice in recent years, which has the advantages of being non-destructive, rapid and accurate at the same time, allowing excellent intraoperative identification of gliomas. In the present work, the latest research on Raman spectroscopy in glioma is summarized to explore the prospect of Raman spectroscopy in glioma surgery.

Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy

Cell misuse and cross-contamination can affect the accuracy of cell research results and result in wasted time, manpower and material resources. Thus, cell line identification is important and necessary. At present, the commonly used cell line identification methods need cell staining and culturing. There is therefore a need to develop a new method for the rapid and automated identification of cell lines. Raman spectroscopy has become one of the emerging techniques in the field of microbial identification, with the advantages of being rapid and noninvasive and providing molecular information for biological samples, which is beneficial in the identification of cell lines. In this study, we built a library of Raman spectra for gastric mucosal epithelial cell lines GES-1 and gastric cancer cell lines, such as AGS, BGC-823, HGC-27, MKN-45, MKN-74 and SNU-16. Five spectral datasets were constructed using spectral data and included the full spectrum, fingerprint region, high-wavelength number region and Raman background of Raman spectra. A stacking ensemble learning model, SL-Raman, was built for different datasets, and gastric cancer cell identification was achieved. For the gastric cancer cells we studied, the differentiation accuracy of SL-Raman was 100% for one of the gastric cancer cells and 100% for six of the gastric cancer cells. Additionally, the separation accuracy for two gastric cancer cells with different degrees of differentiation was 100%. These results demonstrate that Raman spectroscopy combined with SL-Raman may be a new method for the rapid and accurate identification of gastric cancer. In addition, the accuracy of 94.38% for classifying Raman spectral background data using machine learning demonstrates that the Raman spectral background contains some useful spectral features. These data have been overlooked in previous studies.

Miniaturized 7-in-1 fiber-optic Raman probe

This Letter reports a miniature 7-in-1 fiber-optic Raman probe that eliminates the inelastic background Raman signal from a long-fused silica fiber. Its foremost purpose is to enhance a method for investigating extraordinarily tiny substances and effectively capturing Raman inelastic backscattered signals using optical fibers. We successfully used our home-built fiber taper device to combine seven multimode fibers into a single fiber taper with a probe diameter of approximately 35 µm. By experimentally comparing the traditional bare fiber-based Raman spectroscopy system with the miniaturized tapered fiber-optic Raman sensor using liquid solutions, the novel probe's capability is demonstrated. We observed that the miniaturized probe effectively removed the Raman background signal originating from the optical fiber and confirmed expected outcomes for a series of common Raman spectra.

Raman Spectroscopy: A Novel Technology for Gastric Cancer Diagnosis

Gastric cancer is usually diagnosed at late stage and has a high mortality rate, whereas early detection of gastric cancer could bring a better prognosis. Conventional gastric cancer diagnostic methods suffer from long diagnostic times, severe trauma, and a high rate of misdiagnosis and rely heavily on doctors’ subjective experience. Raman spectroscopy is a label-free molecular vibrational spectroscopy technique that identifies the molecular fingerprint of various samples based on the inelastic scattering of monochromatic light. Because of its advantages of non-destructive, rapid, and accurate detection, Raman spectroscopy has been widely studied for benign and malignant tumor differentiation, tumor subtype classification, and section pathology diagnosis. This paper reviews the applications of Raman spectroscopy for the in vivo and in vitro diagnosis of gastric cancer, methodology related to the spectroscopy data analysis, and presents the limitations of the technique.

Development and characterization of a disposable submillimeter fiber optic Raman needle probe for enhancing real-time in vivo deep tissue and biofluids Raman measurements

We report on the development and characterization of disposable submillimeter fiber optic Raman needle probe for enhancing real-time in vivo tissue and biofluids Raman measurements. The submillimeter Raman probe is designed and fabricated using an aluminum-coated multimode fiber tapered with a semispherical lens, resulting in the coaxial laser excitation/Raman collection configuration for maximizing tissue and biofluid Raman measurements. We demonstrate that, with the use of the Raman needle probe associated with the structured background subtraction algorithms developed, high quality tissue Raman spectra covering both the fingerprint (FP) (800-1800cm^-1) and high-wavenumber (HW) (2800-3300cm^-1) regions can be acquired within subseconds from different tissue types (e.g., skin, muscle, fat, cartilage, liver, and brain) and biofluids (e.g., blood, urine). By advancing the Raman needle probe into the murine brain tissue model, high quality depth-resolved deep tissue Raman spectra can also be acquired rapidly. This work shows that the submillimeter fiberoptic Raman needle probe is capable of achieving real-time collection of deep tissue and biofluids FP/HW Raman spectra with high signal to noise ratios. This opens a new avenue with dual functioning of Raman optical biopsy and fine needle aspiration biopsy for enhancing in vivo deep tissue and biofluids diagnosis and characterization in different organs.

Bioreactor design and validation for manufacturing strategies in tissue engineering

The fields of regenerative medicine and tissue engineering offer new therapeutic options to restore, maintain or improve tissue function following disease or injury. To maximize the biological function of a tissue-engineered clinical product, specific conditions must be maintained within a bioreactor to allow the maturation of the product in preparation for implantation. Specifically, the bioreactor should be designed to mimic the mechanical, electrochemical and biochemical environment that the product will be exposed to in vivo. Real-time monitoring of the functional capacity of tissue-engineered products during manufacturing is a critical component of the quality management process. The present review provides a brief overview of bioreactor engineering considerations. In addition, strategies for bioreactor automation, in-line product monitoring and quality assurance are discussed.

Label-free detection of human enteric nerve system using Raman spectroscopy: A pilot study for diagnosis of Hirschsprung disease

BACKGROUND

Hirschsprung disease (HSCR) is characterized by the absence of an enteric nerve system (ENS). To remove aganglionosis, bowel reconstruction is only a curative treatment. It is mandatory to identify the extent of aganglionosis during surgery. Raman spectroscopy is a nondestructive chemical analysis technique that provides detailed information regarding molecular vibrations. The purpose of this study is to detect the ENS using Raman spectroscopy in the human intestine for diagnosis of HSCR.

METHODS

The Raman spectra of each layer of the gastrointestinal wall were collected from surgical specimens of the human rectum. Based on collected spectral data, principal component analysis was performed to determine the ENS. Subsequently, the Raman spectra of HSCR sections were analyzed.

RESULTS

Molecular structures of the gastrointestinal wall were characterized by Raman spectroscopy. Raman spectroscopy could discriminate between ganglion and muscle layers, and the spectra of the border between muscle layers in the aganglionosis were collagen-associated peaks. Either absence on presence of ENS was also confirmed in HSCR material.

CONCLUSIONS

Label-free detection of the ENS was successfully demonstrated using Raman spectroscopy. Since this is a preliminary study, the strategy which may contribute to differentiate between ganglionic and aganglionic segments using noninvasive techniques in HSCR should be evaluated by prospective studies in near future.

Artificial intelligence in upper GI endoscopy - current status, challenges and future promise

White-light endoscopy with biopsy is the current gold standard modality for detecting and diagnosing upper gastrointestinal (GI) pathology. However, missed lesions remain a challenge. To overcome interobserver variability and learning curve issues, artificial intelligence (AI) has recently been introduced to assist endoscopists in the detection and diagnosis of upper GI neoplasia. In contrast to AI in colonoscopy, current AI studies for upper GI endoscopy are smaller pilot studies. Researchers currently lack large volume, well-annotated, high-quality datasets in gastric cancer, dysplasia in Barrett's esophagus and early esophageal squamous cell cancer. This review will look at the latest studies of AI in upper GI endoscopy, discuss some of the challenges facing researchers, and predict what the future may hold in this rapidly changing field.

Recent advances in diagnostic upper endoscopy

BACKGROUND

Esophageo-gastro-duodenoscopy (EGD) is an important procedure used for detection and diagnosis of esophago-gastric lesions. There exists no consensus on the technique of examination.

AIM

To identify recent advances in diagnostic EGDs to improve diagnostic yield.

METHODS

We queried the PubMed database for relevant articles published between January 2001 and August 2019 as well as hand searched references from recently published endoscopy guidelines. Keywords used included free text and MeSH terms addressing quality indicators and technological innovations in EGDs. Factors affecting diagnostic yield and EGD quality were identified and divided into the follow segments: Pre endoscopy preparation, sedation, examination schema, examination time, routine biopsy, image enhanced endoscopy and future developments.

RESULTS

We identified 120 relevant abstracts of which we utilized 67 of these studies in our review. Adequate pre-endoscopy preparation with simethicone and pronase increases gastric visibility. Proper sedation, especially with propofol, increases patient satisfaction after procedure and may improve detection of superficial gastrointestinal lesions. There is a movement towards mandatory picture documentation during EGD as well as dedicating sufficient time for examination improves diagnostic yield. The use of image enhanced endoscopy and magnifying endoscopy improves detection of squamous cell carcinoma and gastric neoplasm. The magnifying endoscopy simple diagnostic algorithm is useful for diagnosis of early gastric cancer.

CONCLUSION

There is a steady momentum in the past decade towards improving diagnostic yield, quality and reporting in EGDs. Other interesting innovations, such as Raman spectroscopy, endocytoscopy and artificial intelligence may have widespread endoscopic applications in the near future.

[Application of fiber Raman endoscopic probe in the diagnosis of gastric cancer]

OBJECTIVE

To develop a fiber Raman endoscopic probe that can be integrated in a gastroscope and evaluate its value in the diagnosis of gastric cancer.

METHODS

The Raman spectra of gastric cancer tissues and normal tissues were obtained using the fiber Raman endoscopic probe and confocal microRaman spectroscopy. After preprocessing with smoothing, baseline elimination and normalization, the spectroscopic data were analyzed by the principle component analyses combined with stechiometry. Based on the pathological results, the diagnostic accuracy, sensitiveness and specificity of Raman spectroscopy combined with stechiometry were evaluated.

RESULTS

The fiber Raman endoscopic probe and microRaman spectroscopy revealed significantly different Raman spectra between gastric cancer tissues and normal tissues. The diagnostic accuracy, sensitiveness and specificity of the fiber Raman endoscopic probe was 80.56%, 88.89%, and 84.72% for gastric cancer, respectively.

CONCLUSIONS

The fiber Raman endoscopic probe combined with stechiometry provides an effective modality for the diagnosis of gastric cancer and can well distinguish gastric cancer tissue from normal gastric tissues.

Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer

Background: Artificial intelligence (AI) and machine learning (ML) approaches in combination with Raman spectroscopy (RS) to obtain accurate medical diagnosis and decision-making is a way forward for understanding not only the chemical pathway to the progression of disease, but also for tailor-made personalized medicine. These processes remove unwanted affects in the spectra such as noise, fluorescence and normalization, and help in the optimization of spectral data by employing chemometrics. Methods: In this study, breast cancer tissues have been analyzed by RS in conjunction with principal component (PCA) and linear discriminate (LDA) analyses. Tissue microarray (TMA) breast biopsies were investigated using RS and chemometric methods and classified breast biopsies into luminal A, luminal B, HER2, and triple negative subtypes. Results: Supervised and unsupervised algorithms were applied on biopsy data to explore intra and inter data set biochemical changes associated with lipids, collagen, and nucleic acid content. LDA predicted specificity accuracy of luminal A, luminal B, HER2, and triple negative subtypes were 70%, 100%, 90%, and 96.7%, respectively. Conclusion: It is envisaged that a combination of RS with AI and ML may create a precise and accurate real-time methodology for cancer diagnosis and monitoring.

Dual excitation wavelength system for combined fingerprint and high wavenumber Raman spectroscopy

A fiber optic probe-based Raman spectroscopy system using a single laser module with two excitation wavelengths, at 680 and 785 nm, has been developed for measuring the fingerprint and high wavenumber regions using a single detector. This system is simpler and less expensive than previously reported configurations of combined fingerprint and high wavenumber Raman systems, and its probe-based implementation facilitates numerous in vivo applications. The high wavenumber region of the Raman spectrum ranges from 2800-3800 cm-1 and contains valuable information corresponding to the molecular vibrations of proteins, lipids, and water, which is complimentary to the biochemical signatures found in the fingerprint region (800-1800 cm-1), which probes DNA, lipids, and proteins. The efficacy of the system is demonstrated by tracking changes in water content in tissue-mimicking phantoms, where Voigtian decomposition of the high wavenumber water peak revealed a correlation between the water content and type of water-tissue interactions in the samples. This dual wavelength system was then used for in vivo assessment of cervical remodeling during mouse pregnancy, a physiologic process with known changes in tissue hydration. The system shows that Raman spectroscopy is sensitive to changes in collagen content in the fingerprint region and hydration state in the high wavenumber region, which was verified using an ex vivo comparison of wet and dry weight. Simultaneous fingerprint and high wavenumber Raman spectroscopy will allow precise in vivo quantification of tissue water content in the high wavenumber region, paired with the high biochemical specificity of the fingerprint region.

Raman Spectroscopy and Microscopy Applications in Cardiovascular Diseases: From Molecules to Organs

Noninvasive and label-free vibrational spectroscopy and microscopy methods have shown great potential for clinical diagnosis applications. Raman spectroscopy is based on inelastic light scattering due to rotational and vibrational modes of molecular bonds. It has been shown that Raman spectra provide chemical signatures of changes in biological tissues in different diseases, and this technique can be employed in label-free monitoring and clinical diagnosis of several diseases, including cardiovascular studies. However, there are very few literature reviews available to summarize the state of art and future applications of Raman spectroscopy in cardiovascular diseases, particularly cardiac hypertrophy. In addition to conventional clinical approaches such as electrocardiography (ECG), echocardiogram (cardiac ultrasound), positron emission tomography (PET), cardiac computed tomography (CT), and single photon emission computed tomography (SPECT), applications of vibrational spectroscopy and microscopy will provide invaluable information useful for the prevention, diagnosis, and treatment of cardiovascular diseases. Various in vivo and ex vivo investigations can potentially be performed using Raman imaging to study and distinguish pathological and physiological cardiac hypertrophies and understand the mechanisms of other cardiac diseases. Here, we have reviewed the recent literature on Raman spectroscopy to study cardiovascular diseases covering investigations on the molecular, cellular, tissue, and organ level.

Raman spectroscopy discriminates malignant follicular lymphoma from benign follicular hyperplasia and from tumour metastasis

Raman spectroscopy is a non-destructive label-free technique providing biochemical tissue fingerprint. The objective of the present work was to test if Raman spectroscopy is a suitable tool to differentiate lymph nodes affected by different conditions, such as reactive follicular hyperplasia (benign), follicular lymphoma (low grade primary tumour), diffuse large B cell lymphoma (high grade primary tumour) and tumour metastasis (secondary tumours). Moreover, we tested its ability to discriminate follicular lymphomas by the tumour grade and the BCL2 protein expression. Lymph nodes collected from 20 patients, who underwent surgery for suspected malignancy, were investigated. Imaging of tissue areas from about 400 µm² up to 2 mm² was performed collecting Raman maps containing thousands of spectra. Partial least squares discriminant analysis (PLS-DA) - a bilinear classification method - was used to calculate lymph node classification models, in order to discriminate at first between benign and malignant tissues and successively among cancer types, grades and the BCL2 protein expression. This proof-of-concept study paves the way for the development of clinical optical biopsy tools for lymph node cancer diagnosis, complementary to histopathological assessment.

Combining high wavenumber and fingerprint Raman spectroscopy for the detection of prostate cancer during radical prostatectomy

For prostate cancer (PCa) patients, radical prostatectomy (complete removal of the prostate) is the only curative surgical option. To date, there is no clinical technique allowing for real-time assessment of surgical margins to minimize the extent of residual cancer. Here, we present a tissue interrogation technique using a dual excitation wavelength Raman spectroscopy system capable of sequentially acquiring fingerprint (FP) and high wavenumber (HWN) Raman spectra. Results demonstrate the ability of the system to detect PCa in post-prostatectomy specimens. In total, 477 Raman spectra were collected from 18 human prostate slices. Each area measured with Raman spectroscopy was characterized as either normal or cancer based on histopathological analyses, and each spectrum was classified based on supervised learning using support vector machines (SVMs). Based on receiver operating characteristic (ROC) analysis, FP (area under the curve [AUC] = 0.89) had slightly superior cancer detection capabilities compared with HWN (AUC = 0.86). Optimal performance resulted from combining the spectral information from FP and HWN (AUC = 0.91), suggesting that the use of these two spectral regions may provide complementary molecular information for PCa detection. The use of leave-one-(spectrum)-out (LOO) or leave-one-patient-out (LOPO) cross-validation produced similar classification results when combining FP with HWN. Our findings suggest that the application of machine learning using multiple data points from the same patient does not result in biases necessarily impacting the reliability of the classification models.

In-vivo Raman spectroscopy: from basics to applications

For more than two decades, Raman spectroscopy has found widespread use in biological and medical applications. The instrumentation and the statistical evaluation procedures have matured, enabling the lengthy transition from ex-vivo demonstration to in-vivo examinations. This transition goes hand-in-hand with many technological developments and tightly bound requirements for a successful implementation in a clinical environment, which are often difficult to assess for novice scientists in the field. This review outlines the required instrumentation and instrumentation parameters, designs, and developments of fiber optic probes for the in-vivo applications in a clinical setting. It aims at providing an overview of contemporary technology and clinical trials and attempts to identify future developments necessary to bring the emerging technology to the clinical end users. A comprehensive overview of in-vivo applications of fiber optic Raman probes to characterize different tissue and disease types is also given.

Sensitivity of Transmission Raman Spectroscopy Signals to Temperature of Biological Tissues

Optical properties of biological tissues can be influenced by their temperature, thus affecting light transport inside the sample. This could potentially be exploited to deliver more photons inside large biological samples, when compared with experiments at room temperature, overcoming some of difficulties due to highly scattering nature of the tissue. Here we report a change in light transmitted inside biological tissue with temperature elevation from 20 to 40 °C, indicating a considerable enhancement of photons collected by the detector in transmission geometry. The measurement of Raman signals in porcine tissue samples, as large as 40 mm in thickness, indicates a considerable increase in signal ranging from 1.3 to 2 fold, subject to biological variability. The enhancements observed are ascribed to phase transitions of lipids in biological samples. This indicates that: 1) experiments performed on tissue at room temperature can lead to an underestimation of signals that would be obtained at depth in the body in vivo and 2) that experiments at room temperature could be modified to increase detection limits by elevating the temperature of the material of interest.

Blood hyperviscosity identification with reflective spectroscopy of tongue tip based on principal component analysis combining artificial neural network

Background

With spectral methods, noninvasive determination of blood hyperviscosity in vivo is very potential and meaningful in clinical diagnosis. In this study, 67 male subjects (41 health, and 26 hyperviscosity according to blood sample analysis results) participate.

Methods

Reflectance spectra of subjects’ tongue tips is measured, and a classification method bases on principal component analysis combined with artificial neural network model is built to identify hyperviscosity. Hold-out and Leave-one-out methods are used to avoid significant bias and lessen overfitting problem, which are widely accepted in the model validation.

Results

To measure the performance of the classification, sensitivity, specificity, accuracy and F-measure are calculated, respectively. The accuracies with 100 times Hold-out method and 67 times Leave-one-out method are 88.05% and 97.01%, respectively.

Conclusions

Experimental results indicate that the built classification model has certain practical value and proves the feasibility of using spectroscopy to identify hyperviscosity by noninvasive determination.

Real time near-infrared Raman spectroscopy for the diagnosis of nasopharyngeal cancer

Near-infrared (NIR) Raman spectroscopy has been investigated as a tool to differentiate nasopharyngeal cancer (NPC) from normal nasopharyngeal tissue in an ex-vivo setting. Recently, we have miniaturized the fiber-optic Raman probe to investigate its utility in real time in-vivo surveillance of NPC patients. A posterior probability model using partial linear square (PLS) mathematical technique was constructed to verify the sensitivity and specificity of Raman spectroscopy in diagnosing NPC from post-irradiated and normal tissue using a diagnostic algorithm from three significant latent variables. NIR-Raman signals of 135 sites were measured from 79 patients with either newly diagnosed NPC (N = 12), post irradiated nasopharynx (N = 37) and normal nasopharynx (N = 30). The mean Raman spectra peaks identified differences at several Raman peaks at 853 cm⁻¹, 940 cm⁻¹, 1078 cm⁻¹, 1335 cm⁻¹, 1554 cm⁻¹, 2885 cm⁻¹ and 2940 cm⁻¹ in the three different nasopharyngeal conditions. The sensitivity and specificity of distinguishing Raman signatures among normal nasopharynx versus NPC and post-irradiated nasopharynx versus NPC were 91% and 95%; and 77% and 96% respectively. Real time near-infrared Raman spectroscopy has a high specificity in distinguishing malignant from normal nasopharyngeal tissue in vivo, and may be investigated as a novel non-invasive surveillance tool in patients with nasopharyngeal cancer.

Characterizing biochemical and morphological variations of clinically relevant anatomical locations of oral tissue in vivo with hybrid Raman spectroscopy and optical coherence tomography technique

This study aims to characterize biochemical and morphological variations of the clinically relevant anatomical locations of in vivo oral tissue (ie, alveolar process, lateral tongue and floor of the mouth) by using hybrid Raman spectroscopy (RS) and optical coherence tomography (OCT) technique. A total of 1049 in vivo fingerprint (FP: 800-1800 cm^-1 ) and high wavenumber (HW: 2800-3600 cm^-1 ) Raman spectra were acquired from different oral tissue (alveolar process = 331, lateral tongue = 339 and floor of mouth = 379) of 26 normal subjects in the oral cavity under the OCT imaging guidance. The total Raman dataset were split into 2 parts: 80% for training and 20% for testing. Tissue optical attenuation coefficients of alveolar process, lateral tongue and the floor of the mouth were derived from OCT images, revealing the inter-anatomical morphological differences; while RS uncovers subtle FP/HW Raman spectral differences among different oral tissues that can be attributed to the differences in inter- and intra-cellular proteins, lipids, DNA and water structures and conformations, enlightening biochemical variability of different oral tissues at the molecular level. Partial least squares-discriminant analysis implemented on the training dataset show that the integrated tissue optical attenuation coefficients and FP/HW Raman spectra provide diagnostic sensitivities of 99.6%, 82.3%, 50.2%, and specificities of 97.0%, 75.1%, 92.1%, respectively, which are superior to using either RS (sensitivities of 90.2%, 77.5%, 48.8%, and specificities of 95.8%, 72.1%, 88.8%) or optical attenuation coefficients derived from OCT (sensitivities of 75.0%, 78.2%, 47.2%, and specificities of 96.2%, 67.7%, 85.0%) for the differentiation among alveolar process, lateral tongue and the floor of the mouth. Furthermore, the diagnostic algorithms applied to the independent testing dataset based on hybrid RS-OCT technique gives predictive diagnostic sensitivities of 100%, 76.5%, 51.3%, and specificities of 95.1%, 77.6%, 89.6%, respectively, for the classifications among alveolar process, lateral tongue and the floor of the mouth, which performs much better than either RS or optical attenuation coefficient derived from OCT imaging. This work suggests that inter-anatomical morphological and biochemical variability are significant which should be considered as an important parameter in the interpretation and rendering of hybrid RS-OCT technique for oral tissue diagnosis and characterization.

Proof-of-concept Raman spectroscopy study aimed to differentiate thyroid follicular patterned lesions

Inter-observer variability and cancer over-diagnosis are emerging clinical problems, especially for follicular patterned thyroid lesions. This challenge strongly calls for a new clinical tool to reliably identify neoplastic lesions and to improve the efficiency of differentiation between benign and malignant neoplasms, especially considering the increased diagnosis of small carcinomas and the growing number of thyroid nodules. In this study, we employed a Raman spectroscopy (RS) microscope to investigate frozen thyroid tissues from fourteen patients with thyroid nodules. To generate tissue classification models, a supervised statistical analysis of the Raman spectra was performed. The results obtained demonstrate an accuracy of 78% for RS based diagnosis to discriminate between normal parenchyma and follicular patterned thyroid nodules, and 89% accuracy - for very challenging follicular lesions (carcinoma versus adenoma). RS translation into intraoperative diagnosis of frozen sections and in preoperative analysis of biopsies can be very helpful to reduce unnecessary surgery in patients with indeterminate cytological reports.

Real-time In vivo Diagnosis of Nasopharyngeal Carcinoma Using Rapid Fiber-Optic Raman Spectroscopy

We report the utility of a simultaneous fingerprint (FP) (i.e., 800-1800 cm^-1) and high-wavenumber (HW) (i.e., 2800-3600 cm^-1) fiber-optic Raman spectroscopy developed for real-time in vivo diagnosis of nasopharyngeal carcinoma (NPC) at endoscopy. A total of 3731 high-quality in vivo FP/HW Raman spectra (normal=1765; cancer=1966) were acquired in real-time from 204 tissue sites (normal=95; cancer=109) of 95 subjects (normal=57; cancer=38) undergoing endoscopic examination. FP/HW Raman spectra differ significantly between normal and cancerous nasopharyngeal tissues that could be attributed to changes of proteins, lipids, nucleic acids, and the bound water content in NPC. Principal components analysis (PCA) and linear discriminant analysis (LDA) together with leave-one subject-out, cross-validation (LOO-CV) were implemented to develop robust Raman diagnostic models. The simultaneous FP/HW Raman spectroscopy technique together with PCA-LDA and LOO-CV modeling provides a diagnostic accuracy of 93.1% (sensitivity of 93.6%; specificity of 92.6%) for nasopharyngeal cancer identification, which is superior to using either FP (accuracy of 89.2%; sensitivity of 89.9%; specificity of 88.4%) or HW (accuracy of 89.7%; sensitivity of 89.0%; specificity of 90.5%) Raman technique alone. Further receiver operating characteristic (ROC) analysis reconfirms the best performance of the simultaneous FP/HW Raman technique for in vivo diagnosis of NPC. This work demonstrates for the first time that simultaneous FP/HW fiber-optic Raman spectroscopy technique has great promise for enhancing real-time in vivo cancer diagnosis in the nasopharynx during endoscopic examination.

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

Gastrointestinal cancer is a leading contributor to cancer-related morbidity and mortality worldwide. Early diagnosis currently plays a key role in the prognosis of patients with gastrointestinal cancer. Despite the advances in endoscopy over the last decades, missing lesions, undersampling and incorrect sampling in biopsies, as well as invasion still result in a poor diagnostic rate of early gastrointestinal cancers. Accordingly, there is a pressing need to develop non-invasive methods for the early detection of gastrointestinal cancers. Biomedical optical spectroscopy, including infrared spectroscopy, Raman spectroscopy, diffuse scattering spectroscopy and autofluorescence, is capable of providing structural and chemical information about biological specimens with the advantages of non-destruction, non-invasion and reagent-free and waste-free analysis and has thus been widely investigated for the diagnosis of oesophageal, gastric and colorectal cancers. This review will introduce the advances of biomedical optical spectroscopy techniques, highlight their applications for the early detection of gastrointestinal cancers and discuss their limitations.

Effect of physiological factors on the biochemical properties of colon tissue – an in vivo Raman spectroscopy study

Raman spectroscopy provides diagnostic information by detecting disease‐associated subtle biochemical changes in the tissue. Yet the physiological variations among normal subjects could confound data interpretation and thus compromise the sensitivity in disease discrimination. We analyzed Raman spectra acquired from colon tissue in vivo through an endoscopic Raman system and evaluated potential physiological factors affecting tissue biochemistry. Fifty‐six healthy patients scheduled for colonoscopy screening were enrolled in the study. Intra‐subject variability was evaluated via univariate analysis by comparing the intensities of major Raman bands from different anatomical locations. Inter‐subject variability was investigated based on various physiological variables, such as age, gender, ethnicity (White/Caucasian, African American, and Hispanic), and body mass index (BMI). Both univariate analysis and principal component analysis‐based multivariate analysis were implemented for the investigation of inter‐subject variability. The differences among certain physiological variables were further analyzed after accounting for intra‐subject variation by generalized estimating equation method due to its advantage in handling repeated measurements. The results showed that physiological factors including gender, ethnicity, age, BMI, and anatomical locations along the colon were significant sources of variability, resulting from different abundance in lipids and proteins. Further correlation analysis revealed that the variability from gender, ethnicity, and age is significantly associated with that from BMI, indicating that BMI might be the key contributor to the inter‐subject variability in the spectra. This study suggested the importance of including normal variability, especially BMI and anatomical locations, into the interpretation of Raman spectra for in vivo application. Copyright © 2017 John Wiley & Sons, Ltd.

Optical techniques for cervical neoplasia detection

This paper provides an overview of the current research in the field of optical techniques for cervical neoplasia detection and covers a wide range of the existing and emerging technologies. Using colposcopy, a visual inspection of the uterine cervix with a colposcope (a binocular microscope with 3- to 15-fold magnification), has proven to be an efficient approach for the detection of invasive cancer. Nevertheless, the development of a reliable and cost-effective technique for the identification of precancerous lesions, confined to the epithelium (cervical intraepithelial neoplasia) still remains a challenging problem. It is known that even at early stages the neoplastic transformations of cervical tissue induce complex changes and modify both structural and biochemical properties of tissues. The different methods, including spectroscopic (diffuse reflectance spectroscopy, induced fluorescence and autofluorescence spectroscopy, Raman spectroscopy) and imaging techniques (confocal microscopy, optical coherence tomography, Mueller matrix imaging polarimetry, photoacoustic imaging), probe different tissue properties that may serve as optical biomarkers for diagnosis. Both the advantages and drawbacks of these techniques for the diagnosis of cervical precancerous lesions are discussed and compared.

Nanoporous Gold Disks Functionalized with Stabilized G-Quadruplex Moieties for Sensing Small Molecules

We report label-free small molecule sensing on nanoporous gold disks functionalized with stabilized Guanine-quadruplex (G4) moieties using surface-enhanced Raman spectroscopy (SERS). By utilizing the unique G4 topological structure, target molecules can be selectively captured onto nanoporous gold (NPG) disk surfaces via π-π stacking and electrostatic attractions. Together with high-density plasmonic "hot spots" of NPG disks, the captured molecules produce a remarkable SERS signal. Our strategy represents the first example of the detection of foreign molecules conjugated to nondouble helical DNA nanostructures using SERS while providing a new technique for studying the formation and evolution of G4 moieties. The molecular specificity of G4 is known to be controlled by its unit sequence. Without losing generality, we have selected d(GGT)₇GG sequence for the sensing of malachite green (MG), a known carcinogen frequently abused illegally in aquaculture. The newly developed technique achieved a lowest detectable concentration at an impressive 50 pM, two orders of magnitude lower than the European Union (EU) regulatory requirement, with high specificity against potential interferents. To demonstrate the translational potential of this technology, we achieved a lowest detectable concentration of 5.0 nM, meeting the EU regulatory requirement, using a portable probe based detection system.

Raman Spectroscopy for the Endoscopic Diagnosis of Esophageal, Gastric, and Colonic Diseases

Globally white-light endoscopy with biopsy sampling is the gold standard diagnostic modality for esophageal, gastric, and colonic pathologies. However, there is overwhelming evidence to highlight the deficiencies of an approach based predominantly on eyeball visualization. Biopsy sampling is also problematic due in part to excessive sampling and hence attendant cost. Various innovations are currently taking place in the endoscopic domain to aid operators in diagnosis forming. These include narrow band imaging which aims to enhance the surface anatomy and vasculature, and confocal laser endomicroscopy which provides real time histological information. However, both of these tools are limited by the skill of the operator and the extensive learning curve associated with their use. There is a gap therefore for a new form of technology that relies solely on an objective measure of disease and reduces the need for biopsy sampling. Raman spectroscopy (RS) is a potential platform that aims to satisfy these criteria. It enables a fingerprint capture of tissue in relation to the protein, DNA, and lipid content. This focused review highlights the strong potential for the use of RS during endoscopic gastroenterological examination.

Development of a hybrid Raman spectroscopy and optical coherence tomography technique for real-time in vivo tissue measurements

We report on the development of a unique sideview handheld hybrid Raman spectroscopy (RS) and optical coherence tomography (OCT) technique for real-time in vivo tissue measurements. A sideview handheld RS-OCT optical probe is designed to coalign the optical paths of RS and OCT sampling arms, whereby a compact long-pass dichroic mirror (LPDM) is utilized to transmit the OCT signal through a gradient index rod lens and a reflection mirror, whereas the LPDM deflects the tissue Raman signal by 90°, leading to coaligned RS/OCT optical samplings on the tissue. Further study shows that the hybrid RS and OCT technique developed is capable of simultaneously acquiring both morphological and biochemical information about the oral tissue in vivo, facilitating real-time, in vivo tissue diagnoses and characterizations in the oral cavity.