Biomedical applications of vibrational spectroscopy: Oral cancer diagnostics

An optical photothermal infrared investigation of lymph nodal metastases of oral squamous cell carcinoma

In this study, optical photothermal infrared (O-PTIR) spectroscopy combined with machine learning algorithms were used to evaluate 46 tissue cores of surgically resected cervical lymph nodes, some of which harboured oral squamous cell carcinoma nodal metastasis. The ratios obtained between O-PTIR chemical images at 1252 cm-1 and 1285 cm-1 were able to reveal morphological details from tissue samples that are comparable to the information achieved by a pathologist's interpretation of optical microscopy of haematoxylin and eosin (H&E) stained samples. Additionally, when used as input data for a hybrid convolutional neural network (CNN) and random forest (RF) analyses, these yielded sensitivities, specificities and precision of 98.6 ± 0.3%, 92 ± 4% and 94 ± 5%, respectively, and an area under receiver operator characteristic (AUC) of 94 ± 2%. Our findings show the potential of O-PTIR technology as a tool to study cancer on tissue samples.

Cervical Squamous Cell Carcinoma Diagnosis by FTIR Microspectroscopy

Cervical cancer was considered the fourth most common cancer worldwide in 2020. In order to reduce mortality, an early diagnosis of the tumor is required. Currently, this type of cancer occurs mostly in developing countries due to the lack of vaccination and screening against the Human Papillomavirus. Thus, there is an urgent clinical need for new methods aiming at a reliable screening and an early diagnosis of precancerous and cancerous cervical lesions. Vibrational spectroscopy has provided very good results regarding the diagnosis of various tumors, particularly using Fourier transform infrared microspectroscopy, which has proved to be a promising complement to the currently used histopathological methods of cancer diagnosis. This spectroscopic technique was applied to the analysis of cryopreserved human cervical tissue samples, both squamous cell carcinoma (SCC) and non-cancer samples. A dedicated Support Vector Machine classification model was constructed in order to categorize the samples into either normal or malignant and was subsequently validated by cross-validation, with an accuracy higher than 90%.

Raman spectroscopy and Raman optical activity of blood plasma for differential diagnosis of gastrointestinal cancers

Improving the early diagnosis of gastrointestinal cancers is a crucial step in reducing their mortality. Given the non-specificity of the initial symptoms, the ability of any diagnostic method to differentiate between various types of gastrointestinal cancers also needs to be addressed. To detect disease-specific alterations in biomolecular structure and composition of the blood plasma, we have implemented an approach combining Raman spectroscopy and its conformation-sensitive polarized version, Raman optical activity, to analyze blood plasma samples of patients suffering from three different types of gastrointestinal cancer - hepatocellular, colorectal and pancreatic. First, we aimed to discriminate any type of gastrointestinal cancer from healthy control individuals; inthenext step, the focus was on differentiating among the three cancer types studied. The more straightforward of the two statistical approaches tested, the combination of linear discriminant analysis and principal component analysis applied to the entire spectral dataset, allowed the discrimination of cancer and control samples with 87% accuracy. The three gastrointestinal cancers were classified with an overall accuracy of 76%. The second method, the linear discriminant analysis applied to a selection of spectral bands, yielded even higher values. Cancer and control samples were distinguished with 89% accuracy and hepatocellular, colorectal and pancreatic cancer with an overall accuracy of 87%. The results obtained in our study suggest that the proposed approach may become a disease-specific diagnostic tool in daily clinical practice.

Intraoperative assessment of resection margins by Raman spectroscopy to guide oral cancer surgery

Patients with oral cavity cancer are almost always treated with surgery. The goal is to remove the tumor with a margin of more than 5 mm of surrounding healthy tissue. Unfortunately, this is only achieved in about 15% to 26% of cases. Intraoperative assessment of tumor resection margins (IOARM) can dramatically improve surgical results. However, current methods are laborious, subjective, and logistically demanding. This hinders broad adoption of IOARM, to the detriment of patients. Here we present the development and validation of a high-wavenumber Raman spectroscopic technology, for quick and objective intraoperative measurement of resection margins on fresh specimens. It employs a thin fiber-optic needle probe, which is inserted into the tissue, to measure the distance between a resection surface and the tumor. A tissue classification model was developed to discriminate oral cavity squamous cell carcinoma (OCSCC) from healthy oral tissue, with a sensitivity of 0.85 and a specificity of 0.92. The tissue classification model was then used to develop a margin length prediction model, showing a mean difference between margin length predicted by Raman spectroscopy and histopathology of -0.17 mm.

Evaluation of IR and Raman spectroscopic markers of human collagens: Insides for indicating colorectal carcinogenesis

Vibrational spectroscopic methods are widely used in the molecular diagnostics of carcinogenesis. Collagen, a component of connective tissue, plays a special role as a biochemical marker of pathological changes in tissues. The vibrational bands of collagens are very promising to distinguish between normal colon tissue, benign and malignant colon polyps. Differences in these bands indicate changes in the amount, structure, conformation and the ratio between the individual structural forms (subtypes) of this protein. The screening of specific collagen markers of colorectal carcinogenesis was carried out based on the FTIR and Raman (λ_ex 785 nm) spectra of colon tissue samples and purified human collagens. It was found that individual types of human collagens showed significant differences in their vibrational spectra, and specific spectral markers were found for them. These collagen bands were assigned to specific vibrations in the polypeptide backbone, amino acid side chains and carbohydrate moieties. The corresponding spectral regions for colon tissues and colon polyps were investigated for the contribution of collagen vibrations. Mentioned spectral differences in collagen spectroscopic markers could be of interest for early ex vivo diagnosis of colorectal carcinoma if combine vibrational spectroscopy and colonoscopy.

Metric-based analysis of FTIR data to discriminate tissue types in oral cancer

A machine learning algorithm (MLA) has predicted the prognosis of oral potentially malignant lesions and discriminated between lymph node tissue and metastatic oral squamous cell carcinoma (OSCC). The MLA analyses metrics, which are ratios of Fourier transform infrared absorbances, and identifies key wavenumbers that can be associated with molecular biomarkers. The wider efficacy of the MLA is now shown in the more complex primary OSCC tumour setting, where it is able to identify seven types of tissue. Three epithelial and four non-epithelial tissue types were discriminated from each other with sensitivities between 82% and 96% and specificities between 90% and 99%. The wavenumbers involved in the five best discriminating metrics for each tissue type were tightly grouped, indicating that small changes in the spectral profiles of the different tissue types are important. The number of samples used in this study was small, but the information will provide a basis for further, larger investigations.

Accurate and Rapid Detection of Peritoneal Metastasis from Gastric Cancer by AI-Assisted Stimulated Raman Molecular Cytology

Peritoneal metastasis (PM) is the mostcommon form of distant metastasis and one of the leading causes of death in gastriccancer (GC). For locally advanced GC, clinical guidelines recommend peritoneal lavage cytology for intraoperative PM detection. Unfortunately, current peritoneal lavage cytology is limited by low sensitivity (<60%). Here the authors established the stimulated Raman molecular cytology (SRMC), a chemical microscopy-based intelligent cytology. The authors firstly imaged 53 951 exfoliated cells in ascites obtained from 80 GC patients (27 PM positive, 53 PM negative). Then, the authors revealed 12 single cell features of morphology and composition that are significantly different between PM positive and negative specimens, including cellular area, lipid protein ratio, etc. Importantly, the authors developed a single cell phenotyping algorithm to further transform the above raw features to feature matrix. Such matrix is crucial to identify the significant marker cell cluster, the divergence of which is finally used to differentiate the PM positive and negative. Compared with histopathology, the gold standard of PM detection, their SRMC method could reach 81.5% sensitivity, 84.9% specificity, and the AUC of 0.85, within 20 minutes for each patient. Together, their SRMC method shows great potential for accurate and rapid detection of PM from GC.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Raman spectroscopy and machine learning for the classification of esophageal squamous carcinoma

Early diagnosis of esophageal squamous cell carcinoma (ESCC), a common malignant tumor with a low overall survival rate due to metastasis and recurrence, is critical for effective treatment and improved prognosis. Raman spectroscopy, an advanced detection technology for esophageal cancer, was developed to improve diagnosis sensitivity, specificity, and accuracy. This study proposed a novel, effective, and noninvasive Raman spectroscopy technique to differentiate and classify ESCC cell lines. Seven ESCC cell lines and tissues of an ESCC patient with staging of T3N1M0 and T3N2M0 at low and high differentiation levels were investigated through Raman spectroscopy. Raman spectral data analysis was performed with four machine learning algorithms, namely principal components analysis (PCA)- linear discriminant analysis (LDA), PCA-eXtreme gradient boosting (XGB), PCA- support vector machine (SVM), and PCA- (LDA, XGB, SVM)-stacked Gradient Boosting Machine (GBM). Four machine learning algorithms were able to classifiy ESCC cell subtypes from normal esophageal cells. The PCA-XGB model achieved an overall predictive accuracy of 85% for classifying ESCC and adjacent tissues. Moreover, an overall predictive accuracy of 90.3% was achieved in distinguishing low differentiation and high differentiation ESCC tissues with the same stage when PCA-LDA, XGM, and SVM models were combined. This study illustrated the Raman spectral traits of ESCC cell lines and esophageal tissues related to clinical pathological diagnosis. Future studies should investigate the role of Raman spectral features in ESCC pathogenesis.

A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra

Vibrational spectroscopies provide information about the biochemical and structural environment of molecular functional groups inside samples. Over the past few decades, Raman and infrared-absorption-based techniques have been extensively used to investigate biological materials under different pathological conditions. Interesting results have been obtained, so these techniques have been proposed for use in a clinical setting for diagnostic purposes, as complementary tools to conventional cytological and histological techniques. In most cases, the differences between vibrational spectra measured for healthy and diseased samples are small, even if these small differences could contain useful information to be used in the diagnostic field. Therefore, the interpretation of the results requires the use of analysis techniques able to highlight the minimal spectral variations that characterize a dataset of measurements acquired on healthy samples from a dataset of measurements relating to samples in which a pathology occurs. Multivariate analysis techniques, which can handle large datasets and explore spectral information simultaneously, are suitable for this purpose. In the present study, two multivariate statistical techniques, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to analyse three different datasets of vibrational spectra, each one including spectra of two different classes: (i) a simulated dataset comprising control-like and exposed-like spectra, (ii) a dataset of Raman spectra measured for control and proton beam-exposed MCF10A breast cells and (iii) a dataset of FTIR spectra measured for malignant non-metastatic MCF7 and metastatic MDA-MB-231 breast cancer cells. Both PCA-LDA and PLS-DA techniques were first used to build a discrimination model by using calibration sets of spectra extracted from the three datasets. Then, the classification performance was established by using test sets of unknown spectra. The achieved results point out that the built classification models were able to distinguish the different spectra types with accuracy between 93% and 100%, sensitivity between 86% and 100% and specificity between 90% and 100%. The present study confirms that vibrational spectroscopy combined with multivariate analysis techniques has considerable potential for establishing reliable diagnostic models.

Breast cancer or surrounding normal tissue? A successful discrimination by FTIR or Raman microspectroscopy

Breast cancer is a type of cancer with the highest incidence worldwide in 2021, with early diagnosis and rapid treatment intervention being the reasons for the decreasing mortality rate associated with the disease.

A New Look into Cancer-A Review on the Contribution of Vibrational Spectroscopy on Early Diagnosis and Surgery Guidance

Simple Summary

Cancer is a leading cause of death worldwide, with the detection of the disease in its early stages, as well as a correct assessment of the tumour margins, being paramount for a successful recovery. While breast cancer is one of most common types of cancer, head and neck cancer is one of the types of cancer with a lower prognosis and poor aesthetic results. Vibrational spectroscopy detects molecular vibrations, being sensitive to different sample compositions, even when the difference was slight. The use of spectroscopy in biomedicine has been extensively explored, since it allows a broader assessment of the biochemical fingerprint of several diseases. This literature review covers the most recent advances in breast and head and neck cancer early diagnosis and intraoperative margin assessment, through Raman and Fourier transform infrared spectroscopies. The rising field of spectral histopathology was also approached. The authors aimed at expounding in a more concise and simple way the challenges faced by clinicians and how vibrational spectroscopy has evolved to respond to those needs for the two types of cancer with the highest potential for improvement regarding an early diagnosis, surgical margin assessment and histopathology.

Abstract

In 2020, approximately 10 million people died of cancer, rendering this disease the second leading cause of death worldwide. Detecting cancer in its early stages is paramount for patients’ prognosis and survival. Hence, the scientific and medical communities are engaged in improving both therapeutic strategies and diagnostic methodologies, beyond prevention. Optical vibrational spectroscopy has been shown to be an ideal diagnostic method for early cancer diagnosis and surgical margins assessment, as a complement to histopathological analysis. Being highly sensitive, non-invasive and capable of real-time molecular imaging, Raman and Fourier transform infrared (FTIR) spectroscopies give information on the biochemical profile of the tissue under analysis, detecting the metabolic differences between healthy and cancerous portions of the same sample. This constitutes tremendous progress in the field, since the cancer-prompted morphological alterations often occur after the biochemical imbalances in the oncogenic process. Therefore, the early cancer-associated metabolic changes are unnoticed by the histopathologist. Additionally, Raman and FTIR spectroscopies significantly reduce the subjectivity linked to cancer diagnosis. This review focuses on breast and head and neck cancers, their clinical needs and the progress made to date using vibrational spectroscopy as a diagnostic technique prior to surgical intervention and intraoperative margin assessment.

Discrimination of Different Breast Cell Lines on Glass Substrate by Means of Fourier Transform Infrared Spectroscopy

Fourier transform infrared (FTIR) micro-spectroscopy has been attracting the interest of many cytologists and histopathologists for several years. This is related to the possibility of FTIR translation in the clinical diagnostic field. In fact, FTIR spectra are able to detect changes in biochemical cellular components occurring when the cells pass to a pathological state. Recently, this interest has increased because it has been shown that FTIR spectra carried out just in the high wavenumber spectral range (2500-4000 cm^-1), where information mainly relating to lipids and proteins can be obtained, are able to discriminate cell lines related to different tissues. This possibility allows to perform IR absorption measurements of cellular samples deposited onto microscopy glass slides (widely used in the medical environment) which are transparent to IR radiation only for wavenumber values larger than 2000 cm^-1. For these reasons, we show that FTIR spectra in the 2800-3000 cm^-1 spectral range can discriminate three different cell lines from breast tissue: a non-malignant cell line (MCF10A), a non-metastatic adenocarcinoma cell line (MCF7) and a metastatic adenocarcinoma cell line (MDA). All the cells were grown onto glass slides. The spectra were discriminated by means of a principal component analysis, according to the PC1 component, whose values have the opposite sign in the pairwise score plots. This result supports the wide studies that are being carried out to promote the translation of the FTIR technique in medical practice, as a complementary diagnostic tool.

Insight into metastatic oral cancer tissue from novel analyses using FTIR spectroscopy and aperture IR-SNOM

A novel machine learning algorithm is shown to accurately discriminate between oral squamous cell carcinoma (OSCC) nodal metastases and surrounding lymphoid tissue on the basis of a single metric, the ratio of Fourier transform infrared (FTIR) absorption intensities at 1252 cm⁻¹ and 1285 cm⁻¹. The metric yields discriminating sensitivities, specificities and precision of 98.8 ± 0.1%, 99.89 ± 0.01% and 99.78 ± 0.02% respectively, and an area under receiver operator characteristic (AUC) of 0.9935 ± 0.0006. The delineation of the OSCC and lymphoid tissue revealed by the image formed from the metric is in better agreement with an immunohistochemistry (IHC) stained image than are either of the FTIR images obtained at the individual wavenumbers. Scanning near-field optical microscopy (SNOM) images of the tissue obtained at a number of key wavenumbers, with high spatial resolution, show variations in the chemical structure of the tissue with a feature size down to ∼4 μm. The image formed from the ratio of the SNOM images obtained at 1252 cm⁻¹ and 1285 cm⁻¹ shows more contrast than the SNOM images obtained at these or a number of other individual wavenumbers. The discrimination between the two tissue types is dominated by the contribution from the 1252 cm⁻¹ signal, which is representative of nucleic acids, and this shows the OSCC tissue to be accompanied by two wide arcs of tissue which are particularly low in nucleic acids. Haematoxylin and eosin (H&E) staining shows the tumour core in this specimen to be ∼40 μm wide and the SNOM topography shows that the core centre is raised by ∼1 μm compared to the surrounding tissue. Line profiles of the SNOM signal intensity taken through the highly keratinised core show that the increase in height correlates with an increase in the protein signal. SNOM line profiles show that the nucleic acids signal decreases at the centre of the tumour core between two peaks of higher intensity. All these nucleic acid features are ∼25 μm wide, roughly the width of two cancer cells.

A SNOM image (a) provides chemical insight into a metastatic tumour identified by H&E staining (b).

Molecular Origin of Blood-Based Infrared Spectroscopic Fingerprints*

Infrared spectroscopy of liquid biopsies is a time- and cost-effective approach that may advance biomedical diagnostics. However, the molecular nature of disease-related changes of infrared molecular fingerprints (IMFs) remains poorly understood, impeding the method's applicability. Here we probe 148 human blood sera and reveal the origin of the variations in their IMFs. To that end, we supplemented infrared spectroscopy with biochemical fractionation and proteomic profiling, providing molecular information about serum composition. Using lung cancer as an example of a medical condition, we demonstrate that the disease-related differences in IMFs are dominated by contributions from twelve highly abundant proteins-that, if used as a pattern, may be instrumental for detecting malignancy. Tying proteomic to spectral information and machine learning advances our understanding of the infrared spectra of liquid biopsies, a framework that could be applied to probing of any disease.

Analysis of Saliva Lipids in Breast and Prostate Cancer by IR Spectroscopy

We have developed a method for studying the lipid profile of saliva, combining preliminary extraction and IR spectroscopic detection. The case-control study involved patients with a histologically verified diagnosis of breast and prostate cancer and healthy volunteers. The comparison group included patients with non-malignant pathologies of the breast (fibroadenomas) and prostate gland (prostatic intraepithelial neoplasia). Saliva was used as a material for biochemical studies. It has been shown that the lipid profile of saliva depends on gender, and for males it also depends on the age group. In cancer pathologies, the lipid profile changes significantly and also depends on gender and age characteristics. The ratio of 1458/1396 cm-1 for both breast and prostate cancer has a potential diagnostic value. In both cases, this ratio decreases compared to healthy controls. For prostate cancer, the ratio of 2923/2957 cm-1 is also potentially informative, which grows against the background of prostate pathologies. It is noted that, in all cases, changes in the proposed ratios are more pronounced in the early stages of diseases, which increases the relevance of their study in biomedical applications.

Raman spectral cytopathology for cancer diagnostic applications

Raman spectroscopy can provide a rapid, label-free, nondestructive measurement of the chemical fingerprint of a sample and has shown potential for cancer screening and diagnosis. Here we report a protocol for Raman microspectroscopic analysis of different exfoliative cytology samples (cervical, oral and lung), covering sample preparation, spectral acquisition, preprocessing and data analysis. The protocol takes 2 h 20 min for sample preparation, measurement and data preprocessing and up to 8 h for a complete analysis. A key feature of the protocol is that it uses the same sample preparation procedure as commonly used in diagnostic cytology laboratories (i.e., liquid-based cytology on glass slides), ensuring compatibility with clinical workflows. Our protocol also covers methods to correct for the spectral contribution of glass and sample pretreatment methods to remove contaminants (such as blood and mucus) that can obscure spectral features in the exfoliated cells and lead to variability. The protocol establishes a standardized clinical routine allowing the collection of highly reproducible data for Raman spectral cytopathology for cancer diagnostic applications for cervical and lung cancer and for monitoring suspicious lesions for oral cancer.