Ex vivo Raman spectroscopy mapping of lung tissue: label-free molecular characterization of nontumorous and cancerous tissues

Utility of Raman Spectroscopy in Pulmonary Medicine

The Raman effect, or as per its original description, "modified scattering", is an observation that the number of scattered light waves shifts after photons make nonelastic contact with a molecule. This effect allows Raman spectroscopy to be very useful in various fields. Although it is well known that Raman spectroscopy could be very beneficial in medicine as a diagnostic tool, there are not many applications of Raman spectroscopy in pulmonary medicine. Mostly tumor tissue, sputum and saliva have been used as material for analysis in respiratory medicine. Raman spectroscopy has shown promising results in malignancy recognition and even tumor staging. Saliva is a biological fluid that could be used as a reliable biomarker of the physiological state of the human body, and is easily acquired. Saliva analysis using Raman spectroscopy has the potential to be a relatively inexpensive and quick tool that could be used for diagnostic, screening and phenotyping purposes. Chronic obstructive pulmonary disease (COPD) is a growing cause of disability and death, and its phenotyping using saliva analysis via Raman spectroscopy has a great potential to be a dependable tool to, among other things, help reduce hospitalizations and disease burden. Although existing methods are effective and generally available, Raman spectroscopy has the benefit of being quick and noninvasive, potentially reducing healthcare costs and workload.

A Dual-Modal, Label-Free Raman Imaging Method for Rapid Virtual Staining of Large-Area Breast Cancer Tissue Sections

As one of the most common cancers, accurate, rapid, and simple histopathological diagnosis is very important for breast cancer. Raman imaging is a powerful technique for label-free analysis of tissue composition and histopathology, but it suffers from slow speed when applied to large-area tissue sections. In this study, we propose a dual-modal Raman imaging method that combines Raman mapping data with microscopy bright-field images to achieve virtual staining of breast cancer tissue sections. We validate our method on various breast tissue sections with different morphologies and biomarker expressions and compare it with the golden standard of histopathological methods. The results demonstrate that our method can effectively distinguish various types and components of tissues, and provide staining images comparable to stained tissue sections. Moreover, our method can improve imaging speed by up to 65 times compared to general spontaneous Raman imaging methods. It is simple, fast, and suitable for clinical applications.

The application of Raman spectroscopy for the diagnosis and monitoring of lung tumors

Raman spectroscopy is an optical technique that uses inelastic light scattering in response to vibrating molecules to produce chemical fingerprints of tissues, cells, and biofluids. Raman spectroscopy strategies produce high levels of chemical specificity without requiring extensive sample preparation, allowing for the use of advanced optical tools such as microscopes, fiber optics, and lasers that operate in the visible and near-infrared spectral range, making them increasingly suitable for a wide range of medical diagnostic applications. Metal nanoparticles and nonlinear optical effects can improve Raman signals, and optimized fiber optic Raman probes can make real-time, in vivo, single-point observations. Furthermore, diagnostic speed and spatial accuracy can be improved through the multimodal integration of Raman measurements and other technologies. Recent studies have significantly contributed to the improvement of diagnostic speed and accuracy, making them suitable for clinical application. Lung cancer is a prevalent type of respiratory malignancy. However, the use of computed tomography for detection and screening frequently reveals numerous smaller lung nodules, which makes the diagnostic process more challenging from a clinical perspective. While the majority of small nodules detected are benign, there are currently no direct methods for identifying which nodules represent very early-stage lung cancer. Positron emission tomography and other auxiliary diagnostic methods for non-surgical biopsy samples from these small nodules yield low detection rates, which might result in significant expenses and the possibility of complications for patients. While certain subsets of patients can undergo curative treatment, other individuals have a less favorable prognosis and need alternative therapeutic interventions. With the emergence of new methods for treating cancer, such as immunotherapies, which can potentially extend patient survival and even lead to a complete cure in certain instances, it is crucial to determine the most suitable biomarkers and metrics for assessing the effectiveness of these novel compounds. This will ensure that significant treatment outcomes are accurately measured. This review provides a comprehensive overview of the prospects of Raman spectroscopy and its applications in the diagnosis and analysis of lung tumors.

Subsecond lung cancer detection within a heterogeneous background of normal and benign tissue using single-point Raman spectroscopy

Significance

Lung cancer is the most frequently diagnosed cancer overall and the deadliest cancer in North America. Early diagnosis through current bronchoscopy techniques is limited by poor diagnostic yield and low specificity, especially for lesions located in peripheral pulmonary locations. Even with the emergence of robotic-assisted platforms, bronchoscopy diagnostic yields remain below 80%.

Aim

The aim of this study was to determine whether in situ single-point fingerprint (800 to 1700    cm - 1 ) Raman spectroscopy coupled with machine learning could detect lung cancer within an otherwise heterogenous background composed of normal tissue and tissue associated with benign conditions, including emphysema and bronchiolitis.

Approach

A Raman spectroscopy probe was used to measure the spectral fingerprint of normal, benign, and cancer lung tissue in 10 patients. Each interrogated specimen was characterized by histology to determine cancer type, i.e., small cell carcinoma or non-small cell carcinoma (adenocarcinoma and squamous cell carcinoma). Biomolecular information was extracted from the fingerprint spectra to identify biomolecular features that can be used for cancer detection.

Results

Supervised machine learning models were trained using leave-one-patient-out cross-validation, showing lung cancer could be detected with a sensitivity of 94% and a specificity of 80%.

Conclusions

This proof of concept demonstrates fingerprint Raman spectroscopy is a promising tool for the detection of lung cancer during diagnostic procedures and can capture biomolecular changes associated with the presence of cancer among a complex heterogeneous background within less than 1 s.

Pathogenesis of Pulmonary Calcification and Homologies with Biomineralization in Other Tissues

Lungs often present tissue calcifications and even ossifications, both in the context of high or normal serum calcium levels. Precise mechanisms governing lung calcifications have not been explored. Herein, we emphasize recent advances about calcification processes in other tissues (especially vascular and bone calcifications) and discuss potential sources of calcium precipitates in the lungs, involvement of mineralization promoters and crystallization inhibitors, as well as specific cytokine milieu and cellular phenotypes characteristic for lung diseases, which may be involved in pulmonary calcifications. Further studies are necessary to demonstrate the exact mechanisms underlying calcifications in the lungs, document homologies in biomineralization processes between various tissues in physiological and pathologic conditions, and unravel any locally specific characteristics of mineralization processes that may be targeted to reduce or prevent functionally relevant lung calcifications without negatively affecting the skeleton.

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.

Development of deep learning algorithms to discriminate giant cell tumors of bone from adjacent normal tissues by confocal Raman spectroscopy

Raman spectroscopy is a non-destructive analysis technique that provides detailed information about the chemical structure of tumors. Raman spectra of 52 giant cell tumors of bone (GCTB) and 21 adjacent normal tissues of formalin-fixed paraffin embedded (FFPE) and frozen specimens were obtained using a confocal Raman spectrometer and analyzed with machine learning and deep learning algorithms. We discovered characteristic Raman shifts in the GCTB specimens. They were assigned to phenylalanine and tyrosine. Based on the spectroscopic data, classification algorithms including support vector machine, k-nearest neighbors and long short-term memory (LSTM) were successfully applied to discriminate GCTB from adjacent normal tissues of both the FFPE and frozen specimens, with the accuracy ranging from 82.8% to 94.5%. Importantly, our LSTM algorithm showed the best performance in the discrimination of the frozen specimens, with a sensitivity and specificity of 93.9% and 95.1% respectively, and the AUC was 0.97. The results of our study suggest that confocal Raman spectroscopy accomplished by the LSTM network could non-destructively evaluate a tumor margin by its inherent biochemical specificity which may allow intraoperative assessment of the adequacy of tumor clearance.

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.

Imaging of Oral SCC Cells by Raman Micro-Spectroscopy Technique

We used Raman micro-spectroscopy technique to analyze the molecular changes associated with oral squamous cell carcinoma (SCC) cells in the form of frozen tissue. Previously, Raman micro-spectroscopy technique on human tissue was mainly based on spectral analysis, but we worked on imaging of molecular structure. In this study, we evaluated the distribution of four components at the cell level (about 10 μm) to describe the changes in protein and molecular structures of protein belonging to malignant tissue. We analyzed ten oral SCC samples of five patients without special pretreatments of the use of formaldehyde. We obtained cell level images of the oral SCC cells at various components (peak at 935 cm⁻¹: proline and valine, 1004 cm⁻¹: phenylalanine, 1223 cm⁻¹: nucleic acids, and 1650 cm⁻¹: amide I). These mapping images of SCC cells showed the distribution of nucleic acids in the nuclear areas; meanwhile, proline and valine, phenylalanine, and amide I were detected in the cytoplasm areas of the SCC cells. Furthermore, the peak of amide I in the cancer area shifts to the higher wavenumber side, which indicates the α-helix component may decrease in its relative amounts of protein in the β-sheet or random coil conformation. Imaging of SCC cells with Raman micro-spectroscopy technique indicated that such a new observation of cancer cells is useful for analyzing the detailed distribution of various molecular conformation within SCC cells.

Recent advances in surface-enhanced Raman spectroscopy based liquid biopsy for colorectal cancer (Review)

As colorectal cancer (CRC) is one of the forms of cancer with the highest prevalence globally and with a high mortality, screening and early detection remains a major issue. Colonoscopy is still the gold standard for detecting premalignant lesions, but it is burdened by some complications. For instance, it is laborious, with some difficulties of acceptance for some patients, and is ultimately an imperfect standard, given that some premalignant lesions or incipient malignancies can be missed by colonoscopic evaluation. In this context, new non-invasive approaches such as surface-enhanced Raman spectroscopy (SERS) based liquid biopsy have gained ground in recent years, showing promising results in oncological pathology diagnosis. These new methods have enabled the detection of subtle molecular profile alterations prior to any macroscopic morphological changes, thus providing a useful tool for early CRC detection. In the present review, we provide a summary of published studies applying SERS in CRC detection, along with our personal experience in using SERS in the diagnosis of different oncological pathologies, including CRC.