Spectrochemical differentiation of meningioma tumours based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy

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.

Comparative Blood Profiling Based on ATR-FTIR Spectroscopy and Chemometrics for Differential Diagnosis of Patients with Amyotrophic Lateral Sclerosis-Pilot Study

Amyotrophic lateral sclerosis (ALS) is a motor neurodegenerative disease characterized by poor prognosis. Currently, screening and diagnostic methods for ALS remain challenging, often leading to diagnosis at an advanced stage of the disease. This delay hinders the timely initiation of therapy, negatively impacting patient well-being. Additionally, misdiagnosis with other neurodegenerative disorders that present similar profiles often occurs. Therefore, there is an urgent need for a cost-effective, rapid, and user-friendly tool capable of predicting ALS onset. In this pilot study, we demonstrate that infrared spectroscopy, coupled with chemometric analysis, can effectively identify and predict disease profiles from blood samples drawn from ALS patients. The selected predictive spectral markers, which are used in various discriminant models, achieved an AUROC sensitivity of almost 80% for distinguishing ALS patients from controls. Furthermore, the differentiation of ALS at both the initial and advanced stages from other neurodegenerative disorders showed even higher AUROC values, with sensitivities of 87% (AUROC: 0.70-0.97). These findings highlight the elevated potential of ATR-FTIR spectroscopy for routine clinical screening and early diagnosis of ALS.

Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis

BACKGROUND

Fourier Transform Infrared (FTIR) spectroscopy has emerged as a powerful analytical tool in medical research, offering non-invasive and precise examination of the molecular composition of biological samples. The primary objective of this review is to underscore the benefits of FTIR spectroscopy in medicinal research, emphasizing its ability to delineate molecular fingerprints and assist in the identification of biochemical structures and key peaks in biological samples.

METHODS

This review comprehensively explores the diverse applications of FTIR spectroscopy in medical investigations, with a specific focus on its utility in analyzing tissue, cells, and hair samples. Various sources, including Google Scholar, PubMed, WorledCat and Scopus, were utilized to conduct this comprehensive literature review.

RESULTS

Recent advancements showcase the versatility of FTIR spectroscopy in elucidating cellular and molecular processes, facilitating disease diagnostics, and enabling treatment monitoring. Notably, FTIR spectroscopy has found significant utility in clinical assessment, particularly in screening counterfeit medicines, owing to its user-friendly operation and minimal sample preparation requirements. Furthermore, customs officials can leverage this technique for preliminary analysis of suspicious samples.

CONCLUSION

This review aims to bridge a gap in the literature and serve as a valuable resource for future research endeavors in FTIR spectroscopy within the medical domain. Additionally, it presents fundamental concepts of FTIR spectroscopy and spectral data interpretation, highlighting its utility as a tool for molecular analysis using Mid-Infrared (MIR) radiation.

Prediction of WHO grade and methylation class of aggressive meningiomas: Extraction of diagnostic information from infrared spectroscopic data

Background

Infrared (IR) spectroscopy allows intraoperative, optical brain tumor diagnosis. Here, we explored it as a translational technology for the identification of aggressive meningioma types according to both, the WHO CNS grading system and the methylation classes (MC).

Methods

Frozen sections of 47 meningioma were examined by IR spectroscopic imaging and different classification approaches were compared to discern samples according to WHO grade or MC.

Results

IR spectroscopic differences were more pronounced between WHO grade 2 and 3 than between MC intermediate and MC malignant, although similar spectral ranges were affected. Aggressive types of meningioma exhibited reduced bands of carbohydrates (at 1024 cm-1) and nucleic acids (at 1080 cm-1), along with increased bands of phospholipids (at 1240 and 1450 cm-1). While linear discriminant analysis was able to discern spectra of WHO grade 2 and 3 meningiomas (AUC 0.89), it failed for MC (AUC 0.66). However, neural network classifiers were effective for classification according to both WHO grade (AUC 0.91) and MC (AUC 0.83), resulting in the correct classification of 20/23 meningiomas of the test set.

Conclusions

IR spectroscopy proved capable of extracting information about the malignancy of meningiomas, not only according to the WHO grade, but also for a diagnostic system based on molecular tumor characteristics. In future clinical use, physicians could assess the goodness of the classification by considering classification probabilities and cross-measurement validation. This might enhance the overall accuracy and clinical utility, reinforcing the potential of IR spectroscopy in advancing precision medicine for meningioma characterization.

Fiber optic probe-based ATR-FTIR spectroscopy for rapid breast cancer detection: A pilot study

Breast cancer diagnosis is crucial for timely treatment and improved outcomes. This paper proposes a novel approach for rapid breast cancer diagnosis using optical fiber probe-based attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy from 750 to 4000 cm-1 . The technique enables direct analysis of tissue samples, eliminating the need for microtome sectioning and staining, thus saving time and resources. By capturing molecular fingerprint information, various machine-learning models were used to analyze the spectroscopic data to classify cancerous and non-cancerous tissues accurately. Comparing deparaffinized and paraffinized samples reveals the impact of sample preparation and experimental methods. The study demonstrates a strong correlation between the cancerous nature of a sample and its ATR-FTIR spectrum, suggesting its potential for breast cancer diagnosis (sensitivity of 74.2% and specificity of 78.3%). The proposed approach holds promise for integration into clinical operations, providing a rapid method for preliminary breast cancer diagnosis.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Evaluation of Fourier transform infrared (FTIR) spectroscopy with multivariate analysis as a novel diagnostic tool for lymph node metastasis in gastric cancer

Fourier transform infrared (FTIR) spectroscopy is a vibration spectroscopy that uses infrared radiation to vibrate to absorb the molecular bonds in its absorbed sample. The main purpose of this study was to evaluate FTIR spectroscopy as a novel diagnostic tool for lymph node metastasis (LNM) of gastric cancer. We collected 160 fresh non-metastatic and metastatic lymph nodes (80 each) from 60 patients with gastric cancer for spectral analysis. FTIR spectra of lymph node (LN) samples were obtained in the wavenumber range of 4000 cm^-1 to 900 cm^-1. We calculated the changes in the ratio of spectral intensity (/ I1460). Principal component analysis (PCA) and Fisher's discriminant analysis (FDA) were used to distinguish malignant from normal LN. Four significant bands at 1080 cm^-1, 1640 cm^-1, 1740 cm^-1 and 3260 cm^-1 separated metastatic and non-metastatic LN spectra into two distinct groups by PCA.T-tests showed that, along with the relative intensity ratios (I₁₀₈₀/I₁₄₆₀, I₁₆₄₀/I₁₄₆₀, I₃₂₆₀/I₁₄₆₀, I₁₇₄₀/I₁₄₆₀), these band ratios were also able to differentiate between malignant and benign LN spectra. Six parameters (P1080 cm^-1, P1300 cm^-1, I₁₀₈₀/I₁₄₆₀, I₁₆₄₀/I₁₄₆₀, I₃₂₆₀/I₁₄₆₀, I₁₇₄₀/I₁₄₆₀) were selected as independent factors to set up discriminant functions. The sensitivity of FTIR spectroscopy in diagnosing LNM was 95 % by discriminant analysis. Our study suggested that FTIR spectroscopy can be a useful tool to examine LNM with high sensitivity and specificity for LNM diagnosis. Therefore it can be used in clinical practice as a non-invasive method.

Extraction of Reduced Infrared Biomarker Signatures for the Stratification of Patients Affected by Parkinson’s Disease: An Untargeted Metabolomic Approach

An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson’s disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification of three main categories: PD patients from subjects with Alzheimer’s disease (AD) and healthy controls (HC). Moreover, sub-categories, such as PD at the early stage (PDI) from PD in the advanced stage (PDD), and PDD vs. AD, were stratified. Every classification step with selected wavenumbers achieved 90.11% to 100% correct assignment rates in classification and internal validation. Therefore, selected metabolic signatures from new patients could be used as input features for screening and diagnostic purposes.

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.

Raman hyperspectral imaging coupled to three-dimensional discriminant analysis: Classification of meningiomas brain tumour grades

Meningiomas remains a clinical dilemma. They are the commonest "benign" types of brain tumours and, although being typically benign, they are divided into three WHO grades categories (I, II and III) which are associated with the tumour growth rate and likelihood of recurrence. Recurrence depends on extend of surgery as well as histopathological diagnosis. There is a marked variation amongst surgeons in the follow-up arrangements for their patients even within the same unit which has a significant clinical, and financial implication. Knowing the tumour grade rapidly is an important factor to predict surgical outcomes and adequate patient treatment. Clinical follow up sometimes is haphazard and not based on clear evidence. Spectrochemical techniques are a powerful tool for cancer diagnostics. Raman hyperspectral imaging is able to generate spatially-distributed spectrochemical signatures with great sensitivity. Using this technique, 95 brain tissue samples (66 meningiomas WHO grade I, 24 meningiomas WHO grade II and 5 meningiomas that reoccurred) were analysed in order to discriminate grade I and grade II samples. Newly-developed three-dimensional discriminant analysis algorithms were used to process the hyperspectral imaging data in a 3D fashion. Three-dimensional principal component analysis quadratic discriminant analysis (3D-PCA-QDA) was able to distinguish grade I and grade II meningioma samples with 96% test accuracy (100% sensitivity and 95% specificity). This technique is here shown to be a high-throughput, reagent-free, non-destructive, and can give accurate predictive information regarding the meningioma tumour grade, hence, having enormous clinical potential with regards to being developed for intra-operative real-time assessment of disease.

Multi- and transgenerational biochemical effects of low-dose exposure to bisphenol A and 4-nonylphenol on testicular interstitial (Leydig) cells

Bisphenol A (BPA) and 4-nonylphenol (NP) are well-known endocrine-disrupting chemicals (EDCs) that have been proven to affect Leydig cell (LC) functions and testosterone production, but whether BPA and NP have multi- and transgenerational biochemical effects on Leydig cells (LCs) is unknown. Fourier transform infrared (FTIR) spectroscopy is a powerful analytical technique that enables label-free and non-destructive analysis of the tissue specimen. Herein we employed FTIR coupled with chemometrics analysis to identify biomolecular changes in testicular interstitial (Leydig) cells of rats after chronic exposure to low doses of BPA and NP. Cluster segregations between exposed and control groups were observed based on the fingerprint region of 1800-900 cm^-1 in all generations. The main biochemical alterations for segregation were amide I, amide II and nucleic acids. BPA and NP single and co-exposure induced significant differences in the ratio of amide I to amide II compared to the corresponding control group in all generations. BPA exposure resulted in remarkable changes of cellular gene transcription and DNA oxidative damage across all generations. Direct exposure to BPA, NP, and BPA&NP of F0 and F1 generations could significantly decrease lipid accumulation in LCs in the F2 and F3 generations. The overall findings revealed that single or co-exposure to BPA and NP at environmental concentrations affects the biochemical structures and properties of LCs.

Grade classification of human glioma using a convolutional neural network based on mid-infrared spectroscopy mapping

This study proposes a convolutional neural network (CNN)-based computer-aided diagnosis (CAD) system for the grade classification of human glioma by using mid-infrared (MIR) spectroscopic mappings. Through data augmentation of pixels recombination, the mappings in the training set increased almost 161 times relative to the original mappings. The pixels of the recombined mappings in the training set came from all of the one-dimensional (1D) vibrational spectroscopy of 62 (almost 80% of all 77 patients) patients at specific bands. Compared with the performance of the CNN-CAD system based on the 1D vibrational spectroscopy, we found that the mean diagnostic accuracy of the recombined MIR spectroscopic mappings at peaks of 2917 cm^-1 , 1539 cm^-1 and 1234 cm^-1 on the test set performed higher and the model also had more stable patterns. This research demonstrates that two-dimensional MIR mapping at a single frequency can be used by the CNN-CAD system for diagnosis and the research also gives a prompt that the mapping collection process can be replaced by a single-frequency IR imaging system, which is cheaper and more portable than a Fourier transform infrared microscopy and thus may be widely utilized in hospitals to provide meaningful assistance for pathologists in clinics.

Non-destructive molecular FTIR spectromicroscopy for real time assessment of redox metallodrugs

Recent emergence of FTIR spectromicroscopy (micro-FTIR) as a dynamic spectroscopy for imaging to study biological chemistry has opened new possibilities for investigating in situ drug release, redox chemistry effects on biological molecules, DNA and drug interactions, membrane dynamics, and redox reactions with proteins at the single cell level. Micro-FTIR applied to metallodrugs has been playing an important role since the last decade because of its great potential to achieve more robust and controlled pharmacological effects against several diseases, including cancer. An important aspect in the development of these drugs is to understand their cellular properties, such as uptake, accumulation, activity, and toxicity. In this review, we present the potential application of micro-FTIR and its importance for studying metal-based drugs, highlighting the perspectives of chemistry of living cells. We also emphasise bioimaging, which is of high importance to localize the cellular processes, for a proper understanding of the mechanism of action.

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

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

Addressing Delicate and Variable Cancer Morphology in Spectral Histopathology Using Canine Visceral Hemangiosarcoma

Spectral histopathology has shown promise for the classification and diagnosis of tumors with defined morphology, but application in tumors with variable or diffuse morphologies is yet to be investigated. To address this gap, we evaluated the application of Fourier transform infrared (FTIR) imaging as an accessory diagnostic tool for canine hemangiosarcoma (HSA), a vascular endothelial cell cancer that is difficult to diagnose. To preserve the delicate vascular tumor tissue structure, and potential classification of single endothelial cells, paraffin removal was not performed, and a partial least square discrimination analysis (PLSDA) and Random Forest (RF) models to classify different tissue types at individual pixel level were established using a calibration set (24 FTIR images from 13 spleen specimens). Next, the prediction capability of the PLSDA model was tested with an independent test set (n = 11), resulting in 74% correct classification of different tissue types at an individual pixel level. Finally, the performance of the FTIR spectropathology and chemometric algorithm for diagnosis of HSA was established in a blinded set of tissue samples (n = 24), with sensitivity and specificity of 80 and 81%, respectively. Taken together, these results show that FTIR imaging without paraffin removal can be applied to tumors with diffuse morphology, and this technique is a promising tool to assist in canine splenic HSA differential diagnosis.

Identification of antemortem and postmortem fractures in a complex environment by FTIR spectroscopy based on a rabbit tibial fracture self-control model

The identification of antemortem and postmortem fractures is a critical and challenging task for forensic researchers. Based on our preliminary studies, we explored whether the combination of Fourier transform infrared spectroscopy (FTIR) and chemometrics can identify antemortem and postmortem fractures in complex environments. The impacts of the four environments on the bone spectrum were analyzed by principal component analysis (PCA). It was found that the bone degradation rate in the submerged and ground surface (GS) environments was higher than that in the buried and constant temperature and moisture (CTM) environments. Additionally, the bone degradation rate in buried environment higher than that in the CTM environment. The average spectrum, PCA and partial least squares discriminant analysis (PLS-DA) results all revealed that there were significant differences between the antemortem fracture and the remaining three groups in a complex environment. Compared with the antemortem fracture, the antemortem fracture control (AFC) and postmortem fracture control (PFC) tended to be more similar to the postmortem fracture. According to the loading plot, amide I and amide II were the main components that contributed to the identification of the antemortem fracture, AFC, postmortem fracture, and PFC. Finally, we established a differential model for the antemortem and postmortem fractures (an accuracy of 96.9%), and a differentiation model for the antemortem fracture, AFC, postmortem fracture, and PFC (an accuracy of 87.5%). In conclusion, FTIR spectroscopy is a reliable tool for the identification of antemortem and postmortem fractures in complex environments.