Diagnostic spectro-cytology revealing differential recognition of cervical cancer lesions by label-free surface enhanced Raman fingerprints and chemometrics

An All-in-One Nanohole Array for Size-Exclusive Trapping and High-Throughput Digital Counting of Single Extracellular Vesicles for Non-Invasive Cancer Screening

The analysis of single small extracellular vesicles (sEVs) could distinguish the heterogeneity of sEVs thus better extract tumor-related signatures. Current protocols for the analysis of single sEV rely mainly on the advanced techniques and require lengthy isolation procedures, limiting applications in clinical diagnosis. Herein, we developed a one-step procedure for rapid isolation of single sEVs from urine, along with an analytical pipeline for the diagnosis of early bladder cancer (BCa). Single sEVs are isolated by an EV-imprinted gold nanohole (EI-AuNH) array that selectively traps individual sEVs and spatially enhances their Raman spectra. After the invalid spectral data from incomplete or absent sEVs was eliminated using Smart-Filter, a convolutional neural network model identifies the origin of the spectra and generates a digital count matrix for each patient. By integrating the digital count data of both tumor-associated and normal sEVs, our model achieves an accuracy of 97.37% in early diagnosis of BCa. Feature extraction using explainable AI identified nine BCa-related signatures, with noticeable reduction on cholesterol and lipids in BCa-associated sEVs. These signatures could further distinguish BCa from other cancers. Overall, the present non-invasive and highly accurate diagnosis platform may revolutionize clinical disease diagnostics through simplified single sEV isolation and advanced modeling.

Surface-Enhanced Raman Scattering (SERS) combined with machine learning enables accurate diagnosis of cervical cancer: from molecule to cell to tissue level

The rising number of cervical cancer cases is placing a heavy economic strain on the country and its people. Improving survival rates hinges on early detection, precise diagnosis, and thorough treatment. Common screening and diagnostic methods like Pap smears, HPV testing, colposcopy, and histopathological exams are used in clinical practice, but they are often costly, time-consuming, invasive, subjective, and may lack the necessary sensitivity and specificity for accurate diagnosis. Developing a quick, non-invasive, and precise method for cervical cancer screening is crucial. Raman spectroscopy offers structural insights without damaging samples, but its weak signals and interference from biological fluorescence limit its clinical use. Surface-Enhanced Raman Scattering (SERS) overcomes these challenges, and recent advances, especially when combined with machine learning, enhance cervical cancer diagnosis by enabling precise detection of tumor. This paper comprehensively reviews and summarizes the application of SERS in cervical cancer diagnosis, ranging from molecular biomarker detection to live cell level and then to tissue level diagnosis. By integrating with machine learning, it facilitates the development of accurate, non-invasive diagnosis of cervical cancer.

A deep ensemble learning approach for squamous cell classification in cervical cancer

Cervical cancer, arising from the cells of the cervix, the lower segment of the uterus connected to the vagina-poses a significant health threat. The microscopic examination of cervical cells using Pap smear techniques plays a crucial role in identifying potential cancerous alterations. While developed nations demonstrate commendable efficiency in Pap smear acquisition, the process remains laborious and time-intensive. Conversely, in less developed regions, there is a pressing need for streamlined, computer-aided methodologies for the pre-analysis and treatment of cervical cancer. This study focuses on the classification of squamous cells into five distinct classes, providing a nuanced assessment of cervical cancer severity. Utilizing a dataset comprising over 4096 images from SimpakMed, available on Kaggle, we employed ensemble technique which included the Convolutional Neural Network (CNN), AlexNet, and SqueezeNet for image classification, achieving accuracies of 90.8%, 92%, and 91% respectively. Particularly noteworthy is the proposed ensemble technique, which surpasses individual model performances, achieving an impressive accuracy of 94%. This ensemble approach underscores the efficacy of our method in precise squamous cell classification and, consequently, in gauging the severity of cervical cancer. The results represent a promising advancement in the development of more efficient diagnostic tools for cervical cancer in resource-constrained settings.

Artificial Intelligence-Driven Platform: Unveiling Critical Hepatic Molecular Alterations in Hepatocellular Carcinoma Development

Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long-term liver damage, the hepatic molecular characteristics are closely related to the occurrence of HCC. Gaining comprehensive information about the location, morphology, and hepatic molecular alterations related to HCC is essential for accurate diagnosis. However, there is a dearth of technological advancements capable of concurrently providing precise HCC diagnosis and discerning the accompanying hepatic molecular alterations. In this study, an integrated information system is developed for the pathological-level diagnosis of HCC and the revelation of critical molecular alterations in the liver. This system utilizes computed tomography/Surface-enhanced Raman scattering combined with an artificial intelligence strategy to establish connections between the occurrence of HCC and alterations in hepatic biomolecules. Employing artificial intelligence techniques, the SERS spectra from both healthy and HCC groups are successfully classified into two distinct categories with a remarkable accuracy rate of 91.38%. Based on molecular profiling, it is identified that the nucleotide-to-lipid signal ratio holds significant potential as a reliable indicator for the occurrence of HCC, thereby serving as a promising tool for prevention and therapeutic surveillance.

Raman Spectroscopic Insights of Phase-Separated Insulin Aggregates

Phase-separated protein accumulation through the formation of several aggregate species is linked to the pathology of several human disorders and diseases. Our current investigation envisaged detailed Raman signature and structural intricacy of bovine insulin in its various forms of aggregates produced in situ at an elevated temperature (60 °C). The amide I band in the Raman spectrum of the protein in its native-like conformation appeared at 1655 cm-1 and indicated the presence of a high content of α-helical structure as prepared freshly in acidic pH. The disorder content (turn and coils) also was predominately present in both the monomeric and oligomeric states and was confirmed by the presence shoulder amide I maker band at ∼1680 cm-1. However, the band shifted to ∼1671 cm-1 upon the transformation of the protein solution into fibrillar aggregates as produced for a longer time of incubation. The protein, however, maintained most of its helical conformation in the oligomeric phase; the low-frequency backbone α-helical conformation signal at ∼935 cm-1 was similar to that of freshly prepared aqueous protein solution enriched in helical conformation. The peak intensity was significantly weak in the fibrillar aggregates, and it appeared as a good Raman signature to follow the phase separation and the aggregation behavior of insulin and similar other proteins. Tyrosine phenoxy moieties in the protein may maintained its H-bond donor-acceptor integrity throughout the course of fibril formation; however, it entered in more hydrophobic environment in its journey of fibril formation. In addition, it was noticed that oligomeric bovine insulin maintained the orientation/conformation of the disulfide bonds. However, in the fibrillar state, the disulfide linkages became more strained and preferred to maintain a single conformation state.

SERS sensing for cancer biomarker: Approaches and directions

These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection.

Nasopharyngeal carcinoma cell screening based on the electroporation-SERS spectroscopy

Nasopharyngeal carcinoma (NPC) is a malignant tumor in head and neck. Early diagnosis can effectively improve the survival rate of patients. Nasopharyngeal exfoliative cytology, as a convenient and noninvasive auxiliary diagnostic method, is suitable for the population screening of NPC, but its diagnostic sensitivity is low. In this study, an electroporation-based SERS technique was proposed to detect and screen the clinical nasopharyngeal exfoliated cell samples. Firstly, nasopharyngeal swabs was used to collected the nasopharyngeal exfoliated cell samples from NPC patients (n = 54) and healthy volunteers (n = 60). Then, gold nanoparticles, as the Raman scattering enhancing substrates, were rapidly introduced into cells by electroporation technique for surface-enhanced Raman scattering (SERS) detection. Finally, SERS spectra combined with principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to diagnose and distinguish NPC cell samples. Raman peak assignments combined with spectral differences reflected the biochemical changes associated with NPC, including nucleic acid, amino acid and carbohydrates. Based on the PCA-LDA approach, the sensitivity, specificity and accuracy of 98.15 %, 96.67 % and 97.37 %, respectively, were achieved for screening NPC. This study offers valuable assistance for noninvasive NPC auxiliary diagnosis, and has grate potential in expanding the application of the SERS technique in clinical cell sample testing.

For cervical cancer diagnosis: Tissue Raman spectroscopy and multi-level feature fusion with SENet attention mechanism

Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer.

Advancing cervical cancer diagnosis and screening with spectroscopy and machine learning

INTRODUCTION

In the UK alone, the incidence of cervical cancer is increasing, hence an urgent need for early and rapid detection of cancer before it develops. Spectroscopy in conjunction with machine learning offers a disruptive technology that promises to be pick up cancer early as compared to the current diagnostic techniques used.

AREAS COVERED

This review article explores the different spectroscopy techniques that have been used for the analysis of cervical cancer. Along with the extensive description of spectroscopic techniques, the various machine learning techniques are also described as well as the applications that have been explored in the diagnosis of cervical cancer. This review delimits the literature specifically associated with cervical cancer studies performed solely with the use of a spectroscopy technique, and machine learning.

EXPERT OPINION

Although there are several methods and techniques to detect cervical cancer, the clinical sector requires to introduce new diagnostic technologies that help improving the quality of life of patient. These innovative technologies involve spectroscopy as a qualitative method and machine learning as a quantitative method. In this article, both the techniques and methodologies that allow and promise to be a new screening tool for the detection of cervical cancer is covered.

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.

Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics

The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.

SERS-Based Biosensors Combined with Machine Learning for Medical Application

Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.

Novel exploration of Raman microscopy and non-linear optical imaging in adenomyosis

Background

Adenomyosis is a common gynecological disease in women. A relevant literature search found that approximately 82% of patients with adenomyosis chose to undergo hysterectomy. However, women of childbearing age are more likely to undergo surgery to preserve the uterus. Because it is difficult to determine the extent of adenomyosis, it is almost impossible to resect adenomyotic tissue and retain the uterus at the same time.

Materials and methods

Following ethics approval and patient consent, tissue samples were resected and prepared to create frozen slices for analysis. One slice was subjected to H&E staining while the remaining slices were photographed with Coherent Anti-Stokes Raman Scattering (CARS), Second-Harmonic Generation (SHG) microscopy, and Raman spectroscopy. Comparative observations and analyses at the same positions were carried out to explore the diagnostic ability of CARS, SHG, and Raman spectroscopy for adenomyosis.

Results

In adenomyotic tissue, we found two characteristic peaks at 1,155 and 1,519 cm^–1 in the Raman spectrum, which were significantly different from normal tissue. The substances shown in the CARS spectrum were represented by peaks of 1,519 cm^–1. SHG microscopy showed a distribution of collagen at the focus of the adenomyosis.

Conclusion

This study represents a novel analysis of Raman microscopy, CARS, and SHG in the analysis of adenomyotic lesions. We found the diffraction spectrum useful in determining the focal boundary and the diagnosis of adenomyosis in the tested samples.

Early assessment of chemotherapeutic response in hepatocellular carcinoma based on serum surface-enhanced Raman spectroscopy

In clinical practice, the transcatheter arterial chemoembolization (TACE) has been widely accepted as the first option for non-surgical hepatocellular carcinoma (HCC) treatment. However, patients with HCC often suffer from poor response to TACE therapy. This can be prevented if the chemotherapeutic response can be early and accurately assessed, which is essential to guide timely and rational management. In this study, the serum SERS technique was for the first time investigated as a potential prognostic tool for early assessment of HCC chemotherapeutic response. According to the SERS spectral analysis results, it is newly found that not only the absolute circulating nucleic acids and collagen levels in pre-therapeutic serum but also the changes in circulating nucleic acids and amino acids between pre-therapeutic and post-therapeutic serum are expected to be potential serum markers for HCC prognosis. By further applying chemometrics methods to establish prognostic models, excellent prognostic accuracies were achieved within only 3 days after TACE therapy. Thus, the proposed method is expected to provide guidance on timely and rational management of HCC to improve its survival rate.

A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence

Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.

Efficacy of Raman Spectroscopy in the Diagnosis of Uterine Cervical Neoplasms: A Meta-Analysis

Background

Uterine cervical neoplasms is widely concerned due to its high incidence rate. Early diagnosis is extremely important for prognosis. The purpose of this article is evaluating the efficacy of Raman spectroscopy in the diagnosis of suspected uterine cervical neoplasms.

Methods

We searched PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of science up to September 1, 2021. By analyzing the true positive (TP), false positive (FP), true negative (TN) and false negative (FN) of six included study, we evaluated the pooled and grouping sensitivity, specificity, positive, and negative likelihood ratios (LR), and diagnostic odds ratio (DOR), with 95% confidence intervals (CI), based on random effects models. The overall diagnostic accuracy of Raman spectrum was evaluated by SROC curve analysis and AUC.

Results

After screening with inclusion and exclusion criteria, a total of six study were included in the study. The pooled sensitivity and specificity was 0.98 (95% Cl, 0.93–0.99) and 0.95 (95% Cl, 0.89–0.98). The total PLR and NLR were 21.05 (95% CI, 8.23–53.86) and 0.03 (95% CI, 0.01–0.07), respectively. And the AUC of the SROC curve which show the overall diagnostic accuracy was 0.99 (0.98–1.00).

Conclusion

Through analysis, we confirmed the role of Raman spectroscopy (RS) in the diagnosis of suspected uterine cervical tumors.

Systematic Review Registration

[https://www.crd.york.ac.uk/prospero/], identifier [CRD42021284966].

Surface enhanced Raman scattering for probing cellular biochemistry

Surface enhanced Raman scattering (SERS) from biomolecules in living cells enables the sensitive, but also very selective, probing of their biochemical composition. This minireview discusses the developments of SERS probing in cells over the past years from the proof-of-principle to observe a biochemical status to the characterization of molecule-nanostructure and molecule-molecule interactions and cellular processes that involve a wide variety of biomolecules and cellular compartments. Progress in applying SERS as a bioanalytical tool in living cells, to gain a better understanding of cellular physiology and to harness the selectivity of SERS, has been achieved by a combination of live cell SERS with several different approaches. They range from organelle targeting, spectroscopy of relevant molecular models, and the optimization of plasmonic nanostructures to the application of machine learning and help us to unify the information from defined biomolecules and from the cell as an extremely complex system.

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.

Raman Spectroscopy of Individual Cervical Exfoliated Cells in Premalignant and Malignant Lesions

Cervical cancer is frequent neoplasia. Currently, the diagnostic approach includes cervical cytology, colposcopy, and histopathology studies; combining detection techniques increases the sensitivity and specificity of the tests. Raman spectroscopy is a high-resolution technique that supports the diagnosis of malignancies. This study aimed to evaluate the Raman spectroscopy technique discriminating between healthy and premalignant/malignant cervical cells. We included 81 exfoliative cytology samples, 29 in the “healthy group” (negative cytology), and 52 in the “CIN group” (premalignant/malignant lesions). We obtained the nucleus and cytoplasm Raman spectra of individual cells. We tested the spectral differences between groups using Permutational Multivariate Analysis of Variance (PERMANOVA) and Canonical Analysis of Principal Coordinates (CAP). We found that Raman spectra have increased intensity in premalignant/malignant cells compared with healthy cells. The characteristic Raman bands corresponded to proteins and nucleic acids, in concordance with the increased replication and translation processes in premalignant/malignant states. We found a classification efficiency of 76.5% and 82.7% for cytoplasmic and nuclear Raman spectra, respectively; cell nucleus Raman spectra showed a sensitivity of 84.6% in identifying cervical anomalies. The classification efficiency and sensitivity obtained for nuclear spectra suggest that Raman spectroscopy could be helpful in the screening and diagnosis of premalignant lesions and cervical cancer.

Current and Future Advancements of Raman Spectroscopy Techniques in Cancer Nanomedicine

Raman scattering is one of the most used spectroscopy and imaging techniques in cancer nanomedicine due to its high spatial resolution, high chemical specificity, and multiplexity modalities. The flexibility of Raman techniques has led, in the past few years, to the rapid development of Raman spectroscopy and imaging for nanodiagnostics, nanotherapy, and nanotheranostics. This review focuses on the applications of spontaneous Raman spectroscopy and bioimaging to cancer nanotheranostics and their coupling to a variety of diagnostic/therapy methods to create nanoparticle-free theranostic systems for cancer diagnostics and therapy. Recent implementations of confocal Raman spectroscopy that led to the development of platforms for monitoring the therapeutic effects of anticancer drugs in vitro and in vivo are also reviewed. Another Raman technique that is largely employed in cancer nanomedicine, due to its ability to enhance the Raman signal, is surface-enhanced Raman spectroscopy (SERS). This review also explores the applications of the different types of SERS, such as SERRS and SORS, to cancer diagnosis through SERS nanoprobes and the detection of small-size biomarkers, such as exosomes. SERS cancer immunotherapy and immuno-SERS (iSERS) microscopy are reviewed.

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.

Raman Imaging: An Impending Approach Towards Cancer Diagnosis

In accordance with the recent studies, Raman spectroscopy is well experimented as a highly sensitive analytical and imaging technique in biomedical research, mainly for various disease diagnosis including cancer. In comparison with other imaging modalities, Raman spectroscopy facilitate numerous assistances owing to its low background signal, immense spatial resolution, high chemical specificity, multiplexing capability, excellent photo stability and non-invasive detection capability. In cancer diagnosis Raman imaging intervened as a promising investigative tool to provide molecular level information to differentiate the cancerous vs non-cancerous cells, tissues and even in body fluids. Anciently, spontaneous Raman scattering is very feeble due to its low signal intensity and long acquisition time but new advanced techniques like coherent Raman scattering (CRS) and surface enhanced Raman scattering (SERS) gradually superseded these issues. So, the present review focuses on the recent developments and applications of Raman spectroscopy-based imaging techniques for cancer diagnosis.