Raman spectral cytopathology for cancer diagnostic applications

Classification of healthy and cancerous colon cells by Fourier transform infrared spectroscopy

Colorectal cancer is one of the most diagnosed types of cancer in developed countries. Current diagnostic methods are partly dependent on pathologist experience and laboratories instrumentation. In this study, we used Fourier Transform Infrared (FTIR) spectroscopy in transflection mode, combined with Principal Components Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares - Discriminant Analysis (PLS-DA), to build a classification algorithm to diagnose colon cancer in cell samples, based on absorption spectra measured in two spectral ranges of the mid-infrared spectrum. In particular, PCA technique highlights small biochemical differences between healthy and cancerous cells: these are related to the larger lipid content in the former compared with the latter and to the larger relative amount of protein and nucleic acid components in the cancerous cells compared with the healthy ones. Comparison of the classification accuracy of PCA-LDA and PLS-DA methods applied to FTIR spectra measured in the 1000-1800 cm-1 (low wavenumber range, LWR) and 2700-3700 cm-1 (high wavenumber range, HWR) remarks that both algorithms are able to classify hidden class FTIR spectra with excellent accuracy (100 %) in both spectral regions. This is a hopeful result for clinical translation of infrared spectroscopy: in fact, it makes reliable the predictions obtained using FTIR measurements carried out only in the HWR, in which the glass slides used in clinical laboratories are transparent to IR radiation.

Intraoperative rapid assessment of the deep muscle surgical margin of tongue squamous cell carcinoma via Raman spectroscopy

Purpose

An accurate assessment of the surgical margins of tongue squamous cell carcinoma (TSCC), especially the deep muscle tissue, can help completely remove the cancer cells and thus minimize the risk of recurrence. This study aimed to develop a classification model that classifies TSCC and normal tissues in order to aid in the rapid and accurate intraoperative assessment of TSCC surgical deep muscle tissue margins.

Materials and methods

The study obtained 240 Raman spectra from 60 sections (30 TSCC and 30 normal) from 15 patients diagnosed with TSCC. The classification model based on the analysis of Raman spectral data was developed, utilizing principal component analysis (PCA) and linear discriminant analysis (LDA) for the diagnosis and classification of TSCC. The leave-one-out cross-validation was employed to estimate and evaluate the prediction performance model.

Results

This approach effectively classified TSCC tissue and normal muscle tissue, achieving an accuracy of exceeding 90%. The Raman analysis showed that TSCC tissues contained significantly higher levels of proteins, lipids, and nucleic acids compared to the adjacent normal tissues. In addition, we have also explored the potential of Raman spectroscopy in classifying different histological grades of TSCC.

Conclusion

The PCA-LDA tissue classification model based on Raman spectroscopy exhibited good accuracy, which could aid in identifying tumor-free margins during surgical interventions and present a promising avenue for the development of rapid and accurate intraoperative techniques.

Surface Acoustic Wave-Enhanced Multi-View Acoustofluidic Rotation Cytometry (MARC) for Pre-Cytopathological Screening

Cytopathology, crucial in disease diagnosis, commonly uses microscopic slides to scrutinize cellular abnormalities. However, processing high volumes of samples often results in numerous negative diagnoses, consuming significant time and resources in healthcare. To address this challenge, a surface acoustic wave-enhanced multi-view acoustofluidic rotation cytometry (MARC) technique is developed for pre-cytopathological screening. MARC enhances cellular morphology analysis through comprehensive and multi-angle observations and amplifies subtle cell differences, particularly in the nuclear-to-cytoplasmic ratio, across various cell types and between cancerous and normal tissue cells. By prioritizing MARC-screened positive cases, this approach can potentially streamline traditional cytopathology, reducing the workload and resources spent on negative diagnoses. This significant advancement enhances overall diagnostic efficiency, offering a transformative vision for cytopathological screening.

Precise Identification of Glioblastoma Micro-Infiltration at Cellular Resolution by Raman Spectroscopy

Precise identification of glioblastoma (GBM) microinfiltration, which is essential for achieving complete resection, remains an enormous challenge in clinical practice. Here, the study demonstrates that Raman spectroscopy effectively identifies GBM microinfiltration with cellular resolution in clinical specimens. The spectral differences between infiltrative lesions and normal brain tissues are attributed to phospholipids, nucleic acids, amino acids, and unsaturated fatty acids. These biochemical metabolites identified by Raman spectroscopy are further confirmed by spatial metabolomics. Based on differential spectra, Raman imaging resolves important morphological information relevant to GBM lesions in a label-free manner. The area under the receiver operating characteristic curve (AUC) for Raman spectroscopy combined with machine learning in detecting infiltrative lesions exceeds 95%. Most importantly, the cancer cell threshold identified by Raman spectroscopy is as low as 3 human GBM cells per 0.01 mm2. Raman spectroscopy enables the detection of previously undetectable diffusely infiltrative cancer cells, which holds potential value in guiding complete tumor resection in GBM patients.

Raman spectroscopic deep learning with signal aggregated representations for enhanced cell phenotype and signature identification

Feature representation is critical for data learning, particularly in learning spectroscopic data. Machine learning (ML) and deep learning (DL) models learn Raman spectra for rapid, nondestructive, and label-free cell phenotype identification, which facilitate diagnostic, therapeutic, forensic, and microbiological applications. But these are challenged by high-dimensional, unordered, and low-sample spectroscopic data. Here, we introduced novel 2D image-like dual signal and component aggregated representations by restructuring Raman spectra and principal components, which enables spectroscopic DL for enhanced cell phenotype and signature identification. New ConvNet models DSCARNets significantly outperformed the state-of-the-art (SOTA) ML and DL models on six benchmark datasets, mostly with >2% improvement over the SOTA performance of 85-97% accuracies. DSCARNets also performed well on four additional datasets against SOTA models of extremely high performances (>98%) and two datasets without a published supervised phenotype classification model. Explainable DSCARNets identified Raman signatures consistent with experimental indications.

Label-Free Surface-Enhanced Raman Spectroscopy with Machine Learning for the Diagnosis of Thyroid Cancer by Using Fine-Needle Aspiration Liquid Samples

The incidence of thyroid cancer is increasing worldwide. Fine-needle aspiration (FNA) cytology is widely applied with the use of extracted biological cell samples, but current FNA cytology is labor-intensive, time-consuming, and can lead to the risk of false-negative results. Surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms holds promise for cancer diagnosis. In this study, we develop a label-free SERS liquid biopsy method with machine learning for the rapid and accurate diagnosis of thyroid cancer by using thyroid FNA washout fluids. These liquid supernatants are mixed with silver nanoparticle colloids, and dispersed in quartz capillary for SERS measurements to discriminate between healthy and malignant samples. We collect Raman spectra of 36 thyroid FNA samples (18 malignant and 18 benign) and compare four classification models: Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The results show that the CNN algorithm is the most precise, with a high accuracy of 88.1%, sensitivity of 87.8%, and the area under the receiver operating characteristic curve of 0.953. Our approach is simple, convenient, and cost-effective. This study indicates that label-free SERS liquid biopsy assisted by deep learning models holds great promise for the early detection and screening of thyroid cancer.

Applications of Data Characteristic AI-Assisted Raman Spectroscopy in Pathological Classification

Raman spectroscopy has been widely used for label-free biomolecular analysis of cells and tissues for pathological diagnosis in vitro and in vivo. AI technology facilitates disease diagnosis based on Raman spectroscopy, including machine learning (PCA and SVM), manifold learning (UMAP), and deep learning (ResNet and AlexNet). However, it is not clear how to optimize the appropriate AI classification model for different types of Raman spectral data. Here, we selected five representative Raman spectral data sets, including endometrial carcinoma, hepatoma extracellular vesicles, bacteria, melanoma cell, diabetic skin, with different characteristics regarding sample size, spectral data size, Raman shift range, tissue sites, Kullback-Leibler (KL) divergence, and significant Raman shifts (i.e., wavenumbers with significant differences between groups), to explore the performance of different AI models (e.g., PCA-SVM, SVM, UMAP-SVM, ResNet or AlexNet). For data set of large spectral data size, Resnet performed better than PCA-SVM and UMAP. By building data characteristic-assisted AI classification model, we optimized the network parameters (e.g., principal components, activation function, and loss function) of AI model based on data size and KL divergence etc. The accuracy improved from 85.1 to 94.6% for endometrial carcinoma grading, from 77.1 to 90.7% for hepatoma extracellular vesicles detection, from 89.3 to 99.7% for melanoma cell detection, from 88.1 to 97.9% for bacterial identification, from 53.7 to 85.5% for diabetic skin screening, and mean time expense of 5 s.

RaT: Raman Transformer for highly accurate melanoma detection with critical features visualization

Melanoma is an important cause of death from skin cancer. Early and accurate diagnosis can effectively reduce mortality. But the current diagnosis relies on the experience of pathologists, increasing the rate of misdiagnosis. In this paper, Raman Transformer (RaT) model is proposed by combining Raman spectroscopy and a Transformer encoder to distinguish the Raman spectra of melanoma and normal tissue. To make the spectral data more suitable for the Transformer encoder, we split the Raman spectrum into segments and map them into block vectors, which are then input into the Transformer encoder and classified using the multi-head self-attention mechanism and the Multilayer Perceptron (MLP). The RaT model achieves 99.69% accuracy, 99.61% sensitivity, and 99.82% specificity, which is higher than the classical principal component analysis with the neural network (PCA + NNET) method. In addition, we visualize and explain the fingerprint peaks found by the RaT model and their corresponding biological information. Our proposed RaT model provides a novel and reliable method for processing Raman spectral data, which is expected to help distinguish melanoma from normal cells, diagnose other diseases, and save human lives.

Combining electromyography and Raman spectroscopy: optical EMG

INTRODUCTION/AIMS

Electromyography (EMG) remains a key component of the diagnostic work-up for suspected neuromuscular disease, but it does not provide insight into the molecular composition of muscle which can provide diagnostic information. Raman spectroscopy is an emerging neuromuscular biomarker capable of generating highly specific, molecular fingerprints of tissue. Here, we present "optical EMG," a combination of EMG and Raman spectroscopy, achieved using a single needle.

METHODS

An optical EMG needle was created to collect electrophysiological and Raman spectroscopic data during a single insertion. We tested functionality with in vivo recordings in the SOD1G93A mouse model of amyotrophic lateral sclerosis (ALS), using both transgenic (n = 10) and non-transgenic (NTg, n = 7) mice. Under anesthesia, compound muscle action potentials (CMAPs), spontaneous EMG activity and Raman spectra were recorded from both gastrocnemius muscles with the optical EMG needle. Standard concentric EMG needle recordings were also undertaken. Electrophysiological data were analyzed with standard univariate statistics, Raman data with both univariate and multivariate analyses.

RESULTS

A significant difference in CMAP amplitude was observed between SOD1G93A and NTg mice with optical EMG and standard concentric needles (p = .015 and p = .011, respectively). Spontaneous EMG activity (positive sharp waves) was detected in transgenic SOD1G93A mice only. Raman spectra demonstrated peaks associated with key muscle components. Significant differences in molecular composition between SOD1G93A and NTg muscle were identified through the Raman spectra.

DISCUSSION

Optical EMG can provide standard electrophysiological data and molecular Raman data during a single needle insertion and represents a potential biomarker for neuromuscular disease.

Impacts of polypropylene microplastics on lipid profiles of mouse liver uncovered by lipidomics analysis and Raman spectroscopy

Microplastics (MPs) are emerging contaminants, and there are only limited studies reporting the impacts of some MPs on liver lipid metabolism in animals. In this study, we investigated the accumulation of polypropylene-MPs in mouse liver and unraveled the change in lipid metabolic profiles by both lipidomics and Raman spectroscopy. Polypropylene-MP exposure did not cause obvious health symptoms, but hematoxylin-eosin staining showed pathological changes that polypropylene-MPs induced lipid droplet accumulation in liver. Lipidomics results showed a significant change in lipid metabolic profiles and the most influenced categories were triglycerides, fatty acids, free fatty acids and lysophosphatidylcholine, implying the effects of polypropylene-MPs on the hemostasis of lipid droplet biogenesis and catabolism. Most altered lipids contained unsaturated bonds and polyunsaturated phospholipids, possibly affecting the fluidity and curvature of membrane surfaces. Raman spectroscopy confirmed that the major spectral alterations of liver tissues were related to lipids, evidencing the altered lipid metabolism and cell membrane components in the presence of polypropylene-MPs. Our findings firstly disclosed the impacts of polypropylene-MPs on lipid metabolisms in mouse liver and hinted at their detrimental disturbance on membrane properties, cellular lipid storage and oxidation regulation, helping our deeper understanding on the toxicities and corresponding risks of polypropylene-MPs to mammals.

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.

Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level

Breast cancer threatens the health of women worldwide, and its molecular subtypes largely determine the therapy and prognosis of patients. However, an uncomplicated and accurate method to identify subtypes is currently lacking. This study utilized photoacoustic spectral analysis (PASA) based on the partial least squares discriminant algorithm (PLS-DA) to identify molecular breast cancer subtypes at the biomacromolecular level in vivo. The area of power spectrum density (APSD) was extracted to semi-quantify the biomacromolecule content. The feature wavelengths were obtained via the variable importance in projection (VIP) score and the selectivity ratio (Sratio), to identify the biomarkers. The PASA achieved an accuracy of 84%. Most of the feature wavelengths fell into the collagen-dominated absorption waveband, which was consistent with the histopathological results. This paper proposes a successful method for identifying molecular breast cancer subtypes and proves that collagen can be treated as a biomarker for molecular breast cancer subtyping.

Raman Spectroscopy for Early Detection of Cervical Cancer, a Global Women’s Health Issue—A Review

This review focuses on recent advances and future perspectives in the use of Raman spectroscopy for cervical cancer, a global women’s health issue. Cervical cancer is the fourth most common women’s cancer in the world, and unfortunately mainly affects younger women. However, when detected at the early precancer stage, it is highly treatable. High-quality cervical screening programmes and the introduction of the human papillomavirus (HPV) vaccine are reducing the incidence of cervical cancer in many countries, but screening is still essential for all women. Current gold standard methods include HPV testing and cytology for screening, followed by colposcopy and histopathology for diagnosis. However, these methods are limited in terms of sensitivity/specificity, cost, and time. New methods are required to aid clinicians in the early detection of cervical precancer. Over the past 20 years, the potential of Raman spectroscopy together with multivariate statistical analysis has been shown for the detection of cervical cancer. This review discusses the research to date on Raman spectroscopic approaches for cervical cancer using exfoliated cells, biofluid samples, and tissue ex vivo and in vivo.

In Vitro Diagnosis and Visualization of Cerebral Ischemia/Reperfusion Injury in Rats and Protective Effects of Ferulic Acid by Raman Biospectroscopy and Machine Learning

Ischemic stroke is a major cause of mortality with complicated pathophysiological mechanisms, and hematoxylin and eosin (HE) staining is a histochemical diagnosis technique heavily relying on subjective observation. In this study, we developed a noninvasive assay using Raman spectroscopy for in vitro diagnosis and visualization of cerebral ischemia/reperfusion injury and protective effects of ferulic acid. By establishing a middle cerebral artery occlusion (MCAO) model in Sprague-Dawley male rats, we found effective interventions by ferulic acid using the neurological function score and HE staining. Raman spectra of neuronal and neuroglial cells exhibited significant intensity changes of protein, nucleotide, lipid, and carbohydrate at 780, 814, 1002, 1012, 1176, 1224, 1402, 1520, 1586, 1614, and 1752 cm^-1. Cluster vector analysis highlighted the alterations at 1002, 1080, 1298, 1430, 1478, 1508, 1586, and 1676 cm^-1. To evaluate the levels of neuron injury and intervention performance, a random forest model was developed on Raman spectral data and achieved satisfactory accuracy (0.9846), sensitivity (0.9679-0.9932), and specificity (0.9945-0.9989), ranking peaks around 1002 cm^-1 as key fingerprint for classification. Spectral phenylalanine-to-tryptophan ratio was the biomarker to visualize neuronal injury and intervention performance of ferulic acid with a resolution of 1 μm. Our results unravel the biochemical changes in neuronal cells with cerebral ischemia/reperfusion injury and ferulic acid treatment, and prove Raman spectroscopy coupled with machine learning as a power tool to classify neuron viability and evaluate the intervention performance in pharmacological research.

Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis

Recent advances in label-free histology promise a new era for real-time diagnosis in neurosurgery. Deep learning using autofluorescence is promising for tumor classification without histochemical staining process. The high image resolution and minimally invasive diagnostics with negligible tissue damage is of great importance. The state of the art is raster scanning endoscopes, but the distal lens optics limits the size. Lensless fiber bundle endoscopy offers both small diameters of a few 100 microns and the suitability as single-use probes, which is beneficial in sterilization. The problem is the inherent honeycomb artifacts of coherent fiber bundles (CFB). For the first time, we demonstrate an end-to-end lensless fiber imaging with exploiting the near-field. The framework includes resolution enhancement and classification networks that use single-shot CFB images to provide both high-resolution imaging and tumor diagnosis. The well-trained resolution enhancement network not only recovers high-resolution features beyond the physical limitations of CFB, but also helps improving tumor recognition rate. Especially for glioblastoma, the resolution enhancement network helps increasing the classification accuracy from 90.8 to 95.6%. The novel technique enables histological real-time imaging with lensless fiber endoscopy and is promising for a quick and minimally invasive intraoperative treatment and cancer diagnosis in neurosurgery.

Diagnostic accuracy of Raman spectroscopy in oral squamous cell carcinoma

Background

Raman spectroscopy (RS) has shown great potential in the diagnosis of oral squamous cell carcinoma (OSCC). Although many single-central original studies have been carried out, it is difficult to use RS in real clinical settings based on the current limited evidence. Herein, we conducted this meta-analysis of diagnostic studies to evaluate the overall performance of RS in OSCC diagnosis.

Methods

We systematically searched databases including Medline, Embase, and Web of Science for studies from January 2000 to March 2022. Data of true positives, true negatives, false positives, and false negatives were extracted from the included studies to calculate the pooled sensitivity, specificity, accuracy, positive and negative likelihood ratios (LRs), and diagnostic odds ratio (DOR) with 95% confidence intervals, then we plotted the summary receiver operating characteristic (SROC) curve and the area under the curve (AUC) to evaluate the overall performance of RS. Quality assessments and publication bias were evaluated by Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) checklist in Review Manager 5.3. The statistical parameters were calculated with StataSE version 12 and MetaDiSc 1.4.

Results

In total, 13 studies were included in our meta-analysis. The pooled diagnostic sensitivity and specificity of RS in OSCC were 0.89 (95% CI, 0.85–0.92) and 0.84 (95% CI, 0.78–0.89). The AUC of SROC curve was 0.93 (95% CI, 0.91–0.95).

Conclusions

RS is a non-invasive diagnostic technology with high specificity and sensitivity for detecting OSCC and has the potential to be applied clinically.

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.

Development and Validation of a Raman Spectroscopic Classification Model for Cervical Intraepithelial Neoplasia (CIN)

The mortality associated with cervical cancer can be reduced if detected at the precancer stage, but current methods are limited in terms of subjectivity, cost and time. Optical spectroscopic methods such as Raman spectroscopy can provide a rapid, label-free and nondestructive measurement of the biochemical fingerprint of a cell, tissue or biofluid. Previous studies have shown the potential of Raman spectroscopy for cervical cancer diagnosis, but most were pilot studies with small sample sizes. The aim of this study is to show the clinical utility of Raman spectroscopy for identifying cervical precancer in a large sample set with validation in an independent test set. Liquid-based cervical cytology samples (n = 662) (326 negative, 200 cervical intraepithelial neoplasia (CIN)1 and 136 CIN2+) were obtained as a training set. Raman spectra were recorded from single-cell nuclei and subjected to a partial least squares discriminant analysis (PLSDA). In addition, the PLSDA classification model was validated using a blinded independent test set (n = 69). A classification accuracy of 91.3% was achieved with only six of the blinded samples misclassified. This study showed the potential clinical utility of Raman spectroscopy with a good classification of negative, CIN1 and CIN2+ achieved in an independent test set.

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.