Thyroid cancer diagnosis by Raman spectroscopy

Raman spectroscopy with an improved support vector machine for discrimination of thyroid and parathyroid tissues

The objective of this study was to discriminate thyroid and parathyroid tissues using Raman spectroscopy combined with an improved support vector machine (SVM) algorithm. In thyroid surgery, there is a risk of inadvertently removing the parathyroid glands. At present, there is a lack of research on using Raman spectroscopy to discriminate parathyroid and thyroid tissues. In this article, samples were obtained from 43 individuals with thyroid and parathyroid tissues for Raman spectroscopy analysis. This study employed partial least squares (PLS) to reduce dimensions of data, and three optimization algorithms are used to improve the classification accuracy of SVM algorithm model in spectral analysis. The results show that PLS-GA-SVM algorithm has higher diagnostic accuracy and better reliability. The sensitivity of this algorithm is 94.67% and the accuracy is 94.44%. It can be concluded that Raman spectroscopy combined with the PLS-GA-SVM diagnostic algorithm has significant potential for discriminating thyroid and parathyroid tissues.

Research on disease diagnosis based on teacher-student network and Raman spectroscopy

Diabetic nephropathy is a serious complication of diabetes, and primary Sjögren's syndrome is a disease that poses a major threat to women's health. Therefore, studying these two diseases is of practical significance. In the field of spectral analysis, although common Raman spectral feature selection models can effectively extract features, they have the problem of changing the characteristics of the original data. The teacher-student network combined with Raman spectroscopy can perform feature selection while retaining the original features, and transfer the performance of the complex deep neural network structure to another lightweight network structure model. This study selects five flow learning models as the teacher network, builds a neural network as the student network, uses multi-layer perceptron for classification, and selects the optimal features based on the evaluation indicators accuracy, precision, recall, and F1-score. After five-fold cross-validation, the research results show that in the diagnosis of diabetic nephropathy, the optimal accuracy rate can reach 98.3%, which is 14.02% higher than the existing research; in the diagnosis of primary Sjögren's syndrome, the optimal accuracy rate can be reached 100%, which is 10.48% higher than the existing research. This study proved the feasibility of Raman spectroscopy combined with teacher-student network in the field of disease diagnosis by producing good experimental results in the diagnosis of diabetic nephropathy and primary Sjögren's syndrome.

Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review

Raman spectroscopy is a widely developing approach for noninvasive analysis that can provide information on chemical composition and molecular structure. High chemical specificity calls for developing different medical diagnostic applications based on Raman spectroscopy. This review focuses on the Raman-based techniques used in medical diagnostics and provides an overview of such techniques, possible areas of their application, and current limitations. We have reviewed recent studies proposing conventional Raman spectroscopy and surface-enhanced Raman spectroscopy for rapid measuring of specific biomarkers of such diseases as cardiovascular disease, cancer, neurogenerative disease, and coronavirus disease (COVID-19). As a result, we have discovered several most promising Raman-based applications to identify affected persons by detecting some significant spectral features. We have analyzed these approaches in terms of their potentially diagnostic power and highlighted the remaining challenges and limitations preventing their translation into clinical settings.

Raman Spectroscopy as a Potential Adjunct of Thyroid Nodule Evaluation: A Systematic Review

The incidence of thyroid nodules (TNs) is estimated at 36.5% and 23% in females and males, respectively. A single thyroid nodule is usually detected during ultrasound assessment in patients with symptoms of thyroid dysfunction or neck mass. TNs are classified as benign tumours (non-malignant hyperplasia), benign neoplasms (e.g., adenoma, a non-invasive follicular tumour with papillary nuclear features) or malignant carcinomas (follicular cell-derived or C-cell derived). The differential diagnosis is based on fine-needle aspiration biopsies and cytological assessment (which is burdened with the bias of subjectivity). Raman spectroscopy (RS) is a laser-based, semiquantitative technique which shows for oscillations of many chemical groups in one label-free measurement. RS, through the assessment of chemical content, gives insight into tissue state which, in turn, allows for the differentiation of disease on the basis of spectral characteristics. The purpose of this study was to report if RS could be useful in the differential diagnosis of TN. The Web of Science, PubMed, and Scopus were searched from the beginning of the databases up to the end of June 2023. Two investigators independently screened key data using the terms "Raman spectroscopy" and "thyroid". From the 4046 records found initially, we identified 19 studies addressing the differential diagnosis of TNs applying the RS technique. The lasers used included 532, 633, 785, 830, and 1064 nm lines. The thyroid RS investigations were performed at the cellular and/or tissue level, as well as in serum samples. The accuracy of papillary thyroid carcinoma detection is approx. 90%. Furthermore, medullary, and follicular thyroid carcinoma can be detected with up to 100% accuracy. These results might be biased with low numbers of cases in some research and overfitting of models as well as the reference method. The main biochemical changes one can observe in malignancies are as follows: increase of protein, amino acids (like phenylalanine, tyrosine, and tryptophan), and nucleic acid content in comparison with non-malignant TNs. Herein, we present a review of the literature on the application of RS in the differential diagnosis of TNs. This technique seems to have powerful application potential in thyroid tumour diagnosis.

An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis

Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent approach and train the algorithms with the relative prominence of the peaks in the selected set. The performance of the considered models, quantified by area under the Receiver Operating Characteristic curve, exceeds 0.9. To enhance the interpretability of the results, we employ eXplainable Artificial Intelligence and compute the contribution of each feature to the prediction of each sample.

Auxiliary Diagnosis of Papillary Thyroid Carcinoma Based on Spectral Phenotype

Thyroid cancer, a common endocrine malignancy, is one of the leading death causes among endocrine tumors. The diagnosis of pathological section analysis suffers from diagnostic delay and cumbersome operating procedures. Therefore, we intend to construct the models based on spectral data that can be potentially used for rapid intraoperative papillary thyroid carcinoma (PTC) diagnosis and characterize PTC characteristics. To alleviate any concerns pathologists may have about using the model, we conducted an analysis of the used bands that can be interpreted pathologically. A spectra acquisition system was first built to acquire spectra of pathological section images from 91 patients. The obtained spectral dataset contains 217 spectra of normal thyroid tissue and 217 spectra of PTC tissue. Clinical data of the corresponding patients were collected for subsequent model interpretability analysis. The experiment has been approved by the Ethics Review Committee of the Wuhu Hospital of East China Normal University. The spectral preprocessing method was used to process the spectra, and the preprocessed signal respectively optimized by the first and secondary informative wavelengths selection was used to develop the PTC detection models. The PTC detection model using mean centering (MC) and multiple scattering correction (MSC) has optimal performance, and the reasons for the good performance were analyzed in combination with the spectral acquisition process and composition of the test slide. For model interpretable analysis, the near-ultraviolet band selected for modeling corresponds to the location of amino acid absorption peak, and this is consistent with the clinical phenomenon of significantly lower amino acid concentrations in PTC patients. Moreover, the absorption peak of hemoglobin selected for modeling is consistent with the low hemoglobin index in PTC patients. In addition, the correlation analysis was performed between the selected wavelengths and the clinical data, and the results show: the reflection intensity of selected wavelengths in normal cells has a moderate correlation with cell arrangement structure, nucleus size and free thyroxine (FT4), and has a strong correlation with triiodothyronine (T3); the reflection intensity of selected bands in PTC cells has a moderate correlation with free triiodothyronine (FT3).

Trajectory Inference for Unraveling Dynamic Biological Processes from Raman Spectral Data

Cell heterogeneity is a crucial parameter for understanding the complexity of numerous biomedical issues. Trajectory inference-based approaches are recent tools developed for single-cell transcriptomics (scRNA-seq) data analysis. They aim to reconstruct evolving pathways from the variety of cell states that coexist simultaneously in a cell population. We propose to expand this concept to Raman spectroscopy, a label-free modality that probes the global molecular nature of a sample, by investigating the dynamics of adipocyte differentiation.

Biomineralogical signatures of breast microcalcifications

Microcalcifications, primarily biogenic apatite, occur in cancerous and benign breast pathologies and are key mammographic indicators. Outside the clinic, numerous microcalcification compositional metrics (e.g., carbonate and metal content) are linked to malignancy, yet microcalcification formation is dependent on microenvironmental conditions, which are notoriously heterogeneous in breast cancer. We interrogate multiscale heterogeneity in 93 calcifications from 21 breast cancer patients using an omics-inspired approach: For each microcalcification, we define a "biomineralogical signature" combining metrics derived from Raman microscopy and energy-dispersive spectroscopy. We observe that (i) calcifications cluster into physiologically relevant groups reflecting tissue type and local malignancy; (ii) carbonate content exhibits substantial intratumor heterogeneity; (iii) trace metals including zinc, iron, and aluminum are enhanced in malignant-localized calcifications; and (iv) the lipid-to-protein ratio within calcifications is lower in patients with poor composite outcome, suggesting that there is potential clinical value in expanding research on calcification diagnostic metrics to include "mineral-entrapped" organic matrix.

Combining Raman spectroscopy and machine learning to assist early diagnosis of gastric cancer

Gastric cancers, with gastric adenocarcinoma (GAC) as the most common histological type, cause quite a few of deaths. In order to improve the survival rate after GAC treatment, it is important to develop a method for early detection and therapy support of GAC. Raman spectroscopy is a potential tool for probing cancer cell due to its real-time and non-destructive measurements without any additional reagents. In this study, we use Raman spectroscopy to examine GAC samples, and distinguish cancerous gastric mucosa from normal gastric mucosa. Average Raman spectra of two groups show differences at 750 cm^-1, 1004 cm^-1, 1449 cm^-1, 1089-1128 cm^-1, 1311-1367 cm^-1 and 1585-1665 cm^-1, These peaks were assigned to cytochrome c, phenylalanine, phospholipid, collagen, lipid, and unsaturated fatty acid respectively. Furthermore, we build a SENet-LSTM model to realize the automatic classification of cancerous gastric mucosa and normal gastric mucosa, with all preprocessed Raman spectra in the range of 400-1800 cm^-1 as input. An accuracy 96.20% was achieved. Besides, by using masking method, we found the Raman spectral features which determine the classification and explore the explainability of the classification model. The results are consistent with the conclusions obtained from the average spectrum. All results indicate it is potential for pre-cancerous screening to combine Raman spectroscopy and machine learning.

Analytical chemistry toward on-site diagnostics

Analytical Chemistry, through quantitative and/or qualitative analysis (identification), is a discipline that involves the development of methodologies and the exploration of new principles to obtain answers to given problems. In situ analysis techniques have attracted attention for its ability to elucidate phenomena occurring and to evaluate amount of a certain component in substances at real time and biological samples as applications of such analysis technology. Lots of techniques have been performed to understand the fundamental phenomena in varied fields such as X-ray, vibrational, and electrochemical impedance spectroscopies and also analytical reagents that enable to semi-quantitative analysis just observation. In fact, applying various in situ techniques in analytical chemistry expands to the medical diagnosis, which leads to be able to detect early diseases. Here, we describe some of previous researches in many fields such as electrochemical device for energy storage, biology, environment, and pathology and briefly introduce our recent challenges to analytical chemistry toward the on-site diagnosis.

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) of liquid biofluids enables the probing of biomolecular markers for disease diagnosis, characterized as a time and cost-effective approach. It remains poorly understood for fast and deep diagnosis of digestive tract cancers (DTC) to detect abundant changes and select specific markers in a broad spectrum of molecular species. Here, we present a diagnostic protocol of DTC in which the in-situ blood-based ATR-FTIR spectroscopic data mining pathway was designed for the identification of DTC triages in 252 blood serum samples, divided into the following groups: liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and their different three stages respectively. The infrared molecular fingerprints (IMFs) of DTC were measured and used to build a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset for classification, including absorbance and wavenumber shifts of FTIR vibration peaks. By comparison, the Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks are suitable to differentiate DTCs and pathological stages with a high sensitivity and specificity of 100% and averaged more than 95%. Furthermore, the measured IMF data was mutually validated via clinical blood biochemistry testing, which indicated that the proposed 2D-SD-IR-based machine learning protocol greatly improved DTC classification performance.

Contributions of vibrational spectroscopy to virology: A review

Vibrational spectroscopic techniques, both infrared absorption and Raman scattering, are high precision, label free analytical techniques which have found applications in fields as diverse as analytical chemistry, pharmacology, forensics and archeometrics and, in recent times, have attracted increasing attention for biomedical applications. As analytical techniques, they have been applied to the characterisation of viruses as early as the 1970 s, and, in the context of the coronavirus disease 2019 (COVID-19) pandemic, have been explored in response to the World Health Organisation as novel methodologies to aid in the global efforts to implement and improve rapid screening of viral infection. This review considers the history of the application of vibrational spectroscopic techniques to the characterisation of the morphology and chemical compositions of viruses, their attachment to, uptake by and replication in cells, and their potential for the detection of viruses in population screening, and in infection response monitoring applications. Particular consideration is devoted to recent efforts in the detection of severe acute respiratory syndrome coronavirus 2, and monitoring COVID-19.

Clinical Use of Raman Spectroscopy Improves Diagnostic Accuracy for Indeterminate Thyroid Nodules

BACKGROUND AND OBJECTIVE

Molecular analysis of thyroid fine-needle aspiration (FNA) specimens is believed to improve the management of indeterminate nodules. Raman spectroscopy (RS) can differentiate benign and malignant thyroid lesions in surgically removed tissues, generating distinctive structural profiles. Herein, the diagnostic performance of RS was tested on FNA biopsies of thyroid gland.

DESIGN

Prospective, blinded, and single-center study.

METHODS

We enrolled 123 patients with indeterminate or more ominous cytologic diagnoses (TIR3A-low-risk indeterminate lesion, TIR3B-high-risk indeterminate lesion, TIR4-suspicious of malignancy, TIR5-malignant). All subjects were surgical candidates (defined by international guidelines) and submitted to FNA procedures for RS analysis. We compared RS data, cytologic findings, and final histologic assessments (as reference standard) using various statistical techniques.

RESULTS

The distribution of our study population was as follows: TIR3A:37, TIR3B:32, TIR4:16, and TIR5:38. In 30.9% of patients, histologic diagnoses were benign. For predicting thyroid malignancy in FNA samples, the overall specificity of RS was 86.8%, with 86.5% specificity in indeterminate cytologic categories. In patients with high-risk ultrasound categories, the specificity of RS increased to 87.5% for TIR3A, reaching 100% for TIR3B. Benign histologic diagnoses accounted for 72.9% of patients classified as TIR3A and 31.3% of those classified as TIR3B. Based on positive RS testing, unnecessary surgery was reduced to 7.4% overall (TIR3A-33.3%, TIR3B-6.7%).

CONCLUSIONS

This premier use of RS for thyroid cytology confirms its role as a valuable diagnostic tool and a valid alternative to molecular studies, capable of improving the management of indeterminate nodules and reducing unnecessary surgery.

Rapid label-free detection of cholangiocarcinoma from human serum using Raman spectroscopy

Cholangiocarcinoma (CCA) is highly prevalent in the northeastern region of Thailand. Current diagnostic methods for CCA are often expensive, time-consuming, and require medical professionals. Thus, there is a need for a simple and low-cost CCA screening method. This work developed a rapid label-free technique by Raman spectroscopy combined with the multivariate statistical methods of principal component analysis and linear discriminant analysis (PCA-LDA), aiming to analyze and classify between CCA (n = 30) and healthy (n = 30) serum specimens. The model's classification performance was validated using k-fold cross validation (k = 5). Serum levels of cholesterol (548, 700 cm-1), tryptophan (878 cm-1), and amide III (1248,1265 cm-1) were found to be statistically significantly higher in the CCA patients, whereas serum beta-carotene (1158, 1524 cm-1) levels were significantly lower. The peak heights of these identified Raman marker bands were input into an LDA model, achieving a cross-validated diagnostic sensitivity and specificity of 71.33% and 90.00% in distinguishing the CCA from healthy specimens. The PCA-LDA technique provided a higher cross-validated sensitivity and specificity of 86.67% and 96.67%. To conclude, this work demonstrated the feasibility of using Raman spectroscopy combined with PCA-LDA as a helpful tool for cholangiocarcinoma serum-based screening.

Unraveling the Effects of Carotenoids Accumulation in Human Papillary Thyroid Carcinoma

Among the thyroid cancers, papillary thyroid cancer (PTC) accounts for 90% of the cases. In addition to the necessity to identify new targets for PTC treatment, early diagnosis and management are highly demanded. Previous data indicated that the multivariate statistical analysis of the Raman spectra allows the discrimination of healthy tissues from PTC ones; this is characterized by bands typical of carotenoids. Here, we dissected the molecular effects of carotenoid accumulation in PTC patients by analyzing whether they were required to provide increased retinoic acid (RA) synthesis and signaling and/or to sustain antioxidant functions. HPLC analysis revealed the lack of a significant difference in the overall content of carotenoids. For this reason, we wondered whether the carotenoid accumulation in PTC patients could be related to vitamin A derivative retinoic acid (RA) biosynthesis and, consequently, the RA-related pathway activation. The transcriptomic analysis performed using a dedicated PCR array revealed a significant downregulation of RA-related pathways in PTCs, suggesting that the carotenoid accumulation in PTC could be related to a lower metabolic conversion into RA compared to that of healthy tissues. In addition, the gene expression profile of 474 PTC cases previously published in the framework of the Cancer Genome Atlas (TGCA) project was examined by hierarchical clustering and heatmap analyses. This metanalysis study indicated that the RA-related pathways resulted in being significantly downregulated in PTCs and being associated with the follicular variant of PTC (FV-PTC). To assess whether the possible fate of the carotenoids accumulated in PTCs is associated with the oxidative stress response, the expression of enzymes involved in ROS scavenging was checked. An increased oxidative stress status and a reduced antioxidant defense response were observed in PTCs compared to matched healthy thyroids; this was possibly associated with the prooxidant effects of high levels of carotenoids. Finally, the DepMap datasets were used to profile the levels of 225 metabolites in 12 thyroid cancer cell lines. The results obtained suggested that the high carotenoid content in PTCs correlates with tryptophan metabolism. This pilot provided novel possible markers and possible therapeutic targets for PTC diagnosis and therapy. For the future, a larger study including a higher number of PTC patients will be necessary to further validate the molecular data reported here.

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.

Metabolic Profile Characterization of Different Thyroid Nodules Using FTIR Spectroscopy: A Review

Thyroid cancer’s incidence has increased in the last decades, and its diagnosis can be a challenge. Further and complementary testing based in biochemical alterations may be important to correctly identify thyroid cancer and prevent unnecessary surgery. Fourier-transform infrared (FTIR) spectroscopy is a metabolomic technique that has already shown promising results in cancer metabolome analysis of neoplastic thyroid tissue, in the identification and classification of prostate tumor tissues and of breast carcinoma, among others. This work aims to gather and discuss published information on the ability of FTIR spectroscopy to be used in metabolomic studies of the thyroid, including discriminating between benign and malignant thyroid samples and grading and classifying different types of thyroid tumors.

A Comprehensive Review on Raman Spectroscopy Applications

Raman spectroscopy is a very powerful tool for material analysis, allowing for exploring the properties of a wide range of different materials. Since its discovery, Raman spectroscopy has been used to investigate several features of materials such carbonaceous and inorganic properties, providing useful information on their phases, functions, and defects. Furthermore, techniques such as surface and tip enhanced Raman spectroscopy have extended the field of application of Raman analysis to biological and analytical fields. Additionally, the robustness and versatility of Raman instrumentations represent a promising solution for performing on-field analysis for a wide range of materials. Recognizing the many hot applications of Raman spectroscopy, we herein overview the main and more recent applications for the investigation of a wide range of materials, such as carbonaceous and biological materials. We also provide a brief but exhaustive theoretical background of Raman spectroscopy, also providing deep insight into the analytical achievements.

Recent Advances in the Label-Free Characterization of Exosomes for Cancer Liquid Biopsy: From Scattering and Spectroscopy to Nanoindentation and Nanodevices

Exosomes (EXOs) are nano-sized vesicles secreted by most cell types. They are abundant in bio-fluids and harbor specific molecular constituents from their parental cells. Due to these characteristics, EXOs have a great potential in cancer diagnostics for liquid biopsy and personalized medicine. Despite this unique potential, EXOs are not yet widely applied in clinical settings, with two main factors hindering their translational process in diagnostics. Firstly, conventional extraction methods are time-consuming, require large sample volumes and expensive equipment, and often do not provide high-purity samples. Secondly, characterization methods have some limitations, because they are often qualitative, need extensive labeling or complex sampling procedures that can induce artifacts. In this context, novel label-free approaches are rapidly emerging, and are holding potential to revolutionize EXO diagnostics. These methods include the use of nanodevices for EXO purification, and vibrational spectroscopies, scattering, and nanoindentation for characterization. In this progress report, we summarize recent key advances in label-free techniques for EXO purification and characterization. We point out that these methods contribute to reducing costs and processing times, provide complementary information compared to the conventional characterization techniques, and enhance flexibility, thus favoring the discovery of novel and unexplored EXO-based biomarkers. In this process, the impact of nanotechnology is systematically highlighted, showing how the effectiveness of these techniques can be enhanced using nanomaterials, such as plasmonic nanoparticles and nanostructured surfaces, which enable the exploitation of advanced physical phenomena occurring at the nanoscale level.

Spectroscopic evaluation of carcinogenesis in endometrial cancer

Carcinogenesis is a multifaceted process of cancer formation. The transformation of normal cells into cancerous ones may be difficult to determine at a very early stage. Therefore, methods enabling identification of initial changes caused by cancer require novel approaches. Although physical spectroscopic methods such as FT-Raman and Fourier Transform InfraRed (FTIR) are used to detect chemical changes in cancer tissues, their potential has not been investigated with respect to carcinogenesis. The study aimed to evaluate the usefulness of FT-Raman and FTIR spectroscopy as diagnostic methods of endometrial cancer carcinogenesis. The results indicated development of endometrial cancer was accompanied with chemical changes in nucleic acid, amide I and lipids in Raman spectra. FTIR spectra showed that tissues with development of carcinogenesis were characterized by changes in carbohydrates and amides vibrations. Principal component analysis and hierarchical cluster analysis of Raman spectra demonstrated similarity of tissues with cancer cells and lesions considered precursor of cancer (complex atypical hyperplasia), however they differed from the control samples. Pearson correlation test showed correlation between cancer and complex atypical hyperplasia tissues and between non-cancerous tissue samples. The results of the study indicate that Raman spectroscopy is more effective in assessing the development of carcinogenesis in endometrial cancer than FTIR.

Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy

The liquid and lyophilized blood plasma of patients with benign or malignant thyroid nodules and healthy individuals were studied by terahertz (THz) time-domain spectroscopy and machine learning. The blood plasma samples from malignant nodule patients were shown to have higher absorption. The glucose concentration and miRNA-146b level were correlated with the sample's absorption at 1 THz. A two-stage ensemble algorithm was proposed for the THz spectra analysis. The first stage was based on the Support Vector Machine with a linear kernel to separate healthy and thyroid nodule participants. The second stage included additional data preprocessing by Ornstein-Uhlenbeck kernel Principal Component Analysis to separate benign and malignant thyroid nodule participants. Thus, the distinction of malignant and benign thyroid nodule patients through their lyophilized blood plasma analysis by terahertz time-domain spectroscopy and machine learning was demonstrated.

Raman Spectroscopy Discloses Altered Molecular Profile in Thyroid Adenomas

Follicular patterned nodules are sometimes complex to be classified due to ambiguous nuclear features and/or questionable capsular or vascular invasion. In this setting, there is a poor inter-observer concordance even among expert pathologists. Raman spectroscopy was recently used to separate benign and malignant thyroid nodules based on their molecular fingerprint; anyway, some histologically proved follicular adenomas were clustered as having a characteristic profile of malignant lesions. In this study, we analyzed five follicular thyroid adenomas with a malignant spectroscopic profile compared to five follicular adenomas with a benign Raman spectrum in order to assess possible molecular differences between the two groups. Morphological, immunohistochemical, and molecular analyses evidenced expression of malignancy-associated proteins in four out of five malignant clustered adenomas. The remaining malignant clustered adenoma showed a TSHR mutation previously associated with autonomously functioning follicular carcinomas. In conclusion, thyroid follicular adenomas are a group of morphologically benign neoplasms that may have altered the mutational or expression profile; cases of adenomas with altered immunophenotype are recognized as showing a profile associated with malignancy by Raman spectroscopy. This correlation warrants a more extensive evaluation and suggests a potential predictive value of spectroscopic assessment in recognizing characteristics associated with tumor progression in follicular thyroid neoplasms.