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A set of typical resonance Raman raw spectra collected from a horizontal section of normal human skin sample, and a vertically sliced BCC skin sample measured at different depths. (Top), the spectrum was from dermis layer of normal skin showing nine feature peaks; (middle), the spectrum was from the vertically sliced BCC sample at a depth of 100 µm. There are eight characteristic peaks including increased peaks at 753 cm⁻¹ and 1,589 cm⁻¹, but intense carotenoids peaks at 1,161 cm⁻¹ and 1,521 cm⁻¹ disappeared compared to the normal tissue (top); (bottom), the spectrum was from BCC sample at a depth of 1,100 µm, substantially similar to the depth of 100 µm, with six Raman peaks, but carotenoids peaks at 1,161 cm⁻¹ and 1,521 cm⁻¹ are present and obviously weaker than normal tissue sample (top). Those peaks of 753 cm⁻¹ and 1,589 cm⁻¹ greatly decreased in comparison with the depth of 100 µm. BCC: basal cell carcinoma
Source publication
Aim: The aim of the study is to test visible resonance Raman (VRR) spectroscopy for rapid skin cancer diagnosis, and evaluate its effectiveness as a new optical biopsy method to distinguish basal cell carcinoma (BCC) from normal skin tissues.
Methods: The VRR spectroscopic technique was undertaken using 532 nm excitation. Normal and BCC human skin...
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It is shown that surface-enhanced Raman spectroscopy (SERS) can identify bacteria based on their genomic DNA composition, acting as a "sample-distinguishing marker". Successful spectral differentiation of bacterial species was accomplished with nanogold aggregates synthesized through single-step plasma reduction of the ionic gold-containing vapored...
Citations
... It is important to note that all the model performances were evaluated using a five-cross-validation paradigm. Several other applications follow the traditional approach of a dimensionality reduction technique followed by a traditional ML algorithm to classify skin cancers with the use of RS (e.g. the use of in PCA-SVM of Qiu et al in [169] or of Liu et al in [170]) on small datasets with very promising results. On the other hand, the literature shows also an intensive use of DL techniques that, combined with the Raman technology, are able to produce autonomous skin cancer diagnosis with high accuracy. ...
Raman spectroscopy (RS) is a label-free molecular vibrational spectroscopy technique that is able to identify the molecular fingerprint of various samples making use of the inelastic scattering of monochromatic light. Because of its advantages of non-destructive and accurate detection, RS is finding more and more use for benign and malignant tissues, tumor differentiation, tumor subtype classification, and section pathology diagnosis, operating either
in vivo
or
in vitro
. However, the high specificity of RS comes at a cost. The acquisition rate is low, depth information cannot be directly accessed, and the sampling area is limited. Such limitations can be contained if data pre- and post-processing methods are combined with current methods of Artificial Intelligence (AI), essentially, Machine Learning (ML) and Deep Learning (DL). The latter is modifying the approach to cancer diagnosis currently used to automate many cancer data analyses, and it has emerged as a promising option for improving healthcare accuracy and patient outcomes by abiliting prediction diseases tools. In a very broad context, Artificial Intelligence applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. The application of autonomous methods to datasets generated by RS analysis of benign and malignant tissues could make RS a rapid and stand-alone technique to help pathologists diagnose cancer with very high accuracy. This review describes the current milestones achieved by applying AI-based algorithms to RS analysis, grouped according to seven major types of cancers (Pancreatic, Breast, Skin, Brain, Prostate, Ovarian and Oral cavity). Additionally, it provides a theoretical foundation to tackle both present and forthcoming challenges in this domain. By exploring the current achievements and discussing the relative methodologies, this review offers recapitulative insights on recent and ongoing efforts to position RS as a rapid and effective cancer screening tool for pathologists. Accordingly, we aim to encourage future research endeavors and to facilitate the realization of the full potential of RS and AI applications in cancer grading.
... OCT can provide cross-sectional images of the skin with high resolution, allowing for detailed visualization of tissue structures and potentially aiding in the differentiation of benign and malignant lesions 42,43 . Raman spectroscopy, on the other hand, can provide molecular information about the lesion, offering insights into its biological composition and assisting in determining its malignancy [25][26][27]44 . Deep learning-based approaches, such as convolutional neural networks (CNNs), have shown promising results in both classification 45 and medical image segmentation tasks 43,46 . ...
The accurate determination of the size and depth of infiltration is critical to the treatment and excision of melanoma and other skin cancers. However, current techniques, such as skin biopsy and histological examination, pose invasiveness, time-consumption, and have limitations in measuring at the deepest level. Non-invasive imaging techniques like dermoscopy and confocal microscopy also present limitations in accurately capturing contrast and depth information for various skin types and lesion locations. Thus, there is a pressing need for non-invasive devices capable of obtaining high-resolution 3D images of skin lesions. In this study, we introduce a novel device that combines 18 MHz ultrasound and photoacoustic tomography into a single unit, enabling the acquisition of colocalized 3D images of skin lesions. We performed in vivo measurements on 25 suspicious human skin nevi that were promptly excised following measurements. The combined ultrasound/photoacoustic tomography imaging technique exhibited a strong correlation with histological Breslow thickness between 0.2 and 3 mm, achieving a coefficient of determination (R2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document}) of 0.93, which is superior to the coefficients from the individual modalities. The results procured in our study underscore the potential of combined ultrasound and photoacoustic tomography as a promising non-invasive 3D imaging approach for evaluating human nevi and other skin lesions. Furthermore, the system allows for integration of other optical modalities such as optical coherence tomography, microscopy, or Raman spectroscopy in future applications.
... Carotenoids play an important role in the antioxidant defense system in the healthy brain [60]. Carotenoids have been extensively studied and confirmed as a cancer biomarker in human organs using Raman, especially resonance Raman spectroscopy, to identify cancers of the skin, rectum/colon, lung, breast, and brain [42,[61][62][63]. Johnson et al. reported the important role of carotenoids, and particularly the presence of lutein and zeaxanthin, in normal brain tissue [64][65][66]. ...
Simple Summary
Real-time diagnosis tools and methods are desired to aid in the intraoperative grading of glioma and tumor boundary identification to achieve safe maximal tumor removal. Raman spectroscopy is an optical method for real-time glioma detection, but few studies use fresh glioma tissue for biochemical analysis. This study is the first investigation of human glioma using a portable VRR-LRRTM Raman analyzer under quasi-clinical conditions, and reveals significant spectral differences between normal (control) and different grades of glioma. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with histopathology as the gold standard. This result validates the possibility of glioma diagnosis using fresh tissue and provides instant feedback for neurosurgeons in guiding maximal safe resection, and it may support the translation of this portable tool for in vivo and real-time use in tissue biochemical analysis.
Abstract
There is still a lack of reliable intraoperative tools for glioma diagnosis and to guide the maximal safe resection of glioma. We report continuing work on the optical biopsy method to detect glioma grades and assess glioma boundaries intraoperatively using the VRR-LRRTM Raman analyzer, which is based on the visible resonance Raman spectroscopy (VRR) technique. A total of 2220 VRR spectra were collected during surgeries from 63 unprocessed fresh glioma tissues using the VRR-LRRTM Raman analyzer. After the VRR spectral analysis, we found differences in the native molecules in the fingerprint region and in the high-wavenumber region, and differences between normal (control) and different grades of glioma tissues. A principal component analysis–support vector machine (PCA-SVM) machine learning method was used to distinguish glioma tissues from normal tissues and different glioma grades. The accuracy in identifying glioma from normal tissue was over 80%, compared with the gold standard of histopathology reports of glioma. The VRR-LRRTM Raman analyzer may be a new label-free, real-time optical molecular pathology tool aiding in the intraoperative detection of glioma and identification of tumor boundaries, thus helping to guide maximal safe glioma removal and adjacent healthy tissue preservation.
... Предложено большое количество методов диагностики, используемых в онкологии [1, 3, 4, 6-9], но для обеспечения в полной мере эффективности раннего выявления опухолевых заболеваний необходима разработка специальных алгоритмов. Соответственно, следует обратить внимание на использование неинвазивных методов исследования [10,11], таких как конфокальная микроскопия, высокочастотный ультразвук, оптическая когерентная томография, комбинационное рассеяние света и флюоресцентная микроспектроскопия и др., которые позволят клиницисту установить точный диагноз в экспресс-режиме для лечения новообразований кожи на ранней стадии [6][7][8][9][12][13][14][15][16]. ...
... Как показано на рис. 1, 2, наиболее значимыми полосами комбинационного рассеяния света при БКК являются 1012 (диапазон волновых чисел 903-1131); 1312 (1131-1375); 1452 (1375-1583); 1662 (1583-1750); 2930 (2875-3062 см -1 ) см -1 , что коррелирует с данными мировой литературы [8,[12][13][14] и может быть использовано в дальнейшем в качестве диагностического критерия. ...
... При БКК отмечается выраженное увеличение белков, включая коллаген типа I в сочетании с амидом I и аминокислотами, и снижение содержания каротиноидов и липидов. Инфракрасная спектроскопия с длинами волн 671, 785, 830 или 1060 нм может иметь ограничения для практического применения [8]. Однако, по нашему мнению, с помощью специального алгоритма диагностики представляется возможным анализировать изменения, происходящие в тканях, при проведении спектрального анализа (как рамановской, так и флюоресцентной составляющей). ...
... Raman spectroscopy can accurately reveal the underlying properties, structures and interactions of biomolecules, mainly by analyzing the vibrational spectra of organic molecules. The unique advantage of VRR spectroscopy using 532 nm excitation wavelength for biological samples is the resonance enhancement of vibrational modes of chemical bonds from cells and tissues [13][14][15][16][17][18]. ...
... As no mechanical movement is required, this axial tuning strategy also ensures the stability and reproducibility of RS measurements. A laser wavelength of 532 nm can excite the spontaneous Raman signals of most biomolecules in human skin thus can be used to study various skin conditions including skin cancer [42]. In particular, carotenoid peaks obtained in our experiment are much stronger than those excited using near infrared light due to the fact that the laser wavelength of 532 nm falls in the resonance band of carotenoids [43]. ...
We report a joint system with both confocal Raman spectroscopy (CRS) and optical coherence tomography (OCT) modules capable of quickly addressing the region of interest in a tissue for targeted Raman measurements from OCT. By using an electrically tunable lens in the Raman module, the focus of the module can be adjusted to address any specific depth indicated in an OCT image in a few milliseconds. We demonstrate the performance of the joint system in the depth dependent measurements of an ex vivo swine tissue and in vivo human skin. This system can be useful in measuring samples embedded with small targets, for example, to identify tumors in skin in vivo and assessment of tumor margins, in which OCT can be used to perform initial real-time screening with high throughput based on morphological features to identify suspicious targets then CRS is guided to address the targets in real time and fully characterize their biochemical fingerprints for confirmation.
... However, the strong band at 1618 cm −1 was assigned to the tryptophan. The 920 C-C stretch of proline ring/glucose/lactic acid (collagen assignment) Protein [53] 1060 C-C stretching of lipid Lipid [54] 1173 Cytosine, guanine, Tyrosine (collagen type I) ...
... Nuclei acid [54] 1283 Amide III and CH 2 wagging vibrations from glycerine and proline Protein [54] 1298 Acyl chains, fatty acids, palmitic acid Lipid [54] 1324 CH 3 or CH 2 wagging mode present in collagen and purine bases of DNA Nuclei acid and Protein [54] 1362 Guanine Nuclei acid [54] 1424 Deoxyribose Nuclei acid [54] 1540 Amide carbonyl group vibrating Protein [54] 1618 Tryptophan Protein [55] 1652 Amide I Protein [51,53] 1673 Amide I Protein [50,51] 1776 Phospholipids, triglycerides (fatty acid) ...
... Nuclei acid [54] 1283 Amide III and CH 2 wagging vibrations from glycerine and proline Protein [54] 1298 Acyl chains, fatty acids, palmitic acid Lipid [54] 1324 CH 3 or CH 2 wagging mode present in collagen and purine bases of DNA Nuclei acid and Protein [54] 1362 Guanine Nuclei acid [54] 1424 Deoxyribose Nuclei acid [54] 1540 Amide carbonyl group vibrating Protein [54] 1618 Tryptophan Protein [55] 1652 Amide I Protein [51,53] 1673 Amide I Protein [50,51] 1776 Phospholipids, triglycerides (fatty acid) ...
Generating a tissue model mimicking the cervix could be useful for studying treatment of precancerous lesions. In this work, bioprinting of hexagon shaped alginate-gelatin scaffolds laden with HeLa spheroids was presented. The three-dimensional (3D) printing system was designed to extrude alginate-gelatin bioink of different viscosities at an extrusion rate of 1–5 mL/min and printing speed from 10 to 50 mm/s. The biophysical properties of the bioink were characterized using dynamic mechanical analysis, viscometer, degradation test, contact angle measurement, Fourier transform infrared spectroscopy (FTIR), live/dead cell stainings and Raman spectroscopy. The bioink formulated with 10% w/v of alginate and 50% w/v of gelatin (ALG10-Gel50) enabled high fidelity printing for the construction of a multilayered 3D structure. The viscosity of the bioink within 12 Pa s and viscoelasticity of the polymerized bioink (G′ = 0.074 MPa > G″ = 0.028 MPa) exhibited mechanical properties close to the in-vivo system. The scaffolds degraded 35% on the day 16 of culture. The polymerized bioinks exhibited hydrophilicity and contained amino groups as characterized by contact angles and FTIR measurements, respectively. In addition, the 3D microtissues laden in the scaffold were indicated with high cell viability at 95.25 ± 1.75% based on the live/dead cell stainings. The printed microtissues were characterized with the presence of deoxyribonucleic acid, lipids and amino acids associated with the collagen. This paper demonstrated the success in the bioprinting of multilayer hexagon shaped tissue model which is potentially useful for development of an in-vitro cervical cancer model.
... Optical spectroscopy includes absorption [28], Raman [16,[29][30][31][32][33], emission [34], scattering [35][36][37], and time-resolved [38] methods, which were widely investigated by the researchers. Currently, Stokes Shift Spectroscopy (S3) offers a novel and simpler way to rapidly recognize spectral fingerprints of fluorophores in complex mixtures such as in tissue [39]. ...
Early detection of prostate cancer is critical for the success of cancer therapy. It is believed that the biochemical changes that cause the optical spectra changes would appear earlier than the histological aberration. The aim of this ex vivo study was to evaluate the ability of Stokes Shift Spectra (S3) to identify human prostate cancerous tissues from the normal. Fifteen (15) pairs of with pathologically confirmed human prostate cancerous and normal tissues underwent Stokes Shift Spectra measurements with selective wavelength interval of 40 nm. The spectra were then analyzed using machine learning (ML) algorithms to classify the two types of tissues. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key fluorophores related to carcinogenesis. The results show that these key fluorophores within tissue, e.g., tryptophan, collagen, and NADH, have different relative concentrations between cancerous and normal tissues. A multi-class classification was performed using support vector machines (SVMs). A leave-one-out cross validation was used to evaluate the performance of the classification with the gold standard histopathological results as the ground truth. The results with high sensitivity and specificity indicate that the S3 method is effective for detecting changes of fluorophore composition in human prostate tissues due to the development of cancer.
... For BCC: The band 1240-1350 cm À1 is called Amide III and the absorbance in this band has contributions due to CH vibrations. [46][47][48] For SCC: The band 1030-1100 cm À1 may be related to motions of C-O, C-C, C-H 2 , or C-H subgroups. 49,50 For melanomas: The range 1030-1100 cm À1 may be linked to PO 2 symmetric stretching. ...
Purpose
Melanoma is the most lethal of the three primary skin cancers, including also basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), which are less lethal. The accepted diagnosis process involves manually observing a suspicious lesion through a Dermascope (i.e., a magnifying glass), followed by a biopsy. This process relies on the skill and the experience of a dermatologist. However, to the best of our knowledge, there is no accepted automatic, noninvasive, and rapid method for the early detection of the three types of skin cancer, distinguishing between them and noncancerous lesions, and identifying each of them. It is our aim to develop such a system.
Methods
We developed a fiber‐optic evanescent wave spectroscopy (FEWS) system based on middle infrared (mid‐IR) transmitting AgClBr fibers and a Fourier‐transform infrared spectrometer (FTIR). We used the system to perform mid‐IR spectral measurements on suspicious lesions in 90 patients, before biopsy, in situ, and in real time. The lesions were then biopsied and sent for pathology. The spectra were analyzed and the differences between pathological and healthy tissues were found and correlated.
Results
Five of the lesions measured were identified as melanomas, seven as BCC, and three as SCC. Using mathematical analyses of the spectra of these lesions we were able to tell that all were skin cancers and we found specific and easily identifiable differences between them.
Conclusions
This FEWS method lends itself to rapid, automatic and noninvasive early detection and characterization of skin cancers. It will be easily implemented in community clinics and has the potential to greatly simplify the diagnosis process.
... tryptophan, DNA, amides, lipids and proteins. These advantages of VRR have led to a rapid progress in its applications in diagnosis of brain and other human cancers such as breast, skin, heart, GI, gynecologic and the endocrine system lesions [4][5][6]15,16] that are difficult to achieve by conventional non-resonance Raman method [17][18][19][20]. This work is a continuation of Alfano's group's optical biopsy (OB) research, which has made significant progress since the pioneering reports in 1984 and 1987 [21,22] using optical spectroscopy to detect cancer. ...
