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Rimskaya E, Gorevoy A, Shelygina S, Perevedentseva E, Timurzieva A, Saraeva I, Melnik N, Kudryashov S, Kuchmizhak A. Multi-Wavelength Raman Differentiation of Malignant Skin Neoplasms. Int J Mol Sci 2024; 25:7422. [PMID: 39000528 PMCID: PMC11242141 DOI: 10.3390/ijms25137422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/16/2024] Open
Abstract
Raman microspectroscopy has become an effective method for analyzing the molecular appearance of biomarkers in skin tissue. For the first time, we acquired in vitro Raman spectra of healthy and malignant skin tissues, including basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), at 532 and 785 nm laser excitation wavelengths in the wavenumber ranges of 900-1800 cm-1 and 2800-3100 cm-1 and analyzed them to find spectral features for differentiation between the three classes of the samples. The intensity ratios of the bands at 1268, 1336, and 1445 cm-1 appeared to be the most reliable criteria for the three-class differentiation at 532 nm excitation, whereas the bands from the higher wavenumber region (2850, 2880, and 2930 cm-1) were a robust measure of the increased protein/lipid ratio in the tumors at both excitation wavelengths. Selecting ratios of the three bands from the merged (532 + 785) dataset made it possible to increase the accuracy to 87% for the three classes and reach the specificities for BCC + SCC equal to 87% and 81% for the sensitivities of 95% and 99%, respectively. Development of multi-wavelength excitation Raman spectroscopic techniques provides a versatile non-invasive tool for research of the processes in malignant skin tumors, as well as other forms of cancer.
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Affiliation(s)
- Elena Rimskaya
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Alexey Gorevoy
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Svetlana Shelygina
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Elena Perevedentseva
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Alina Timurzieva
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
- Semashko National Research Institute of Public Health, 105064 Moscow, Russia
| | - Irina Saraeva
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Nikolay Melnik
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Sergey Kudryashov
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (A.G.); (S.S.); (E.P.); (A.T.); (I.S.); (N.M.); (S.K.)
| | - Aleksandr Kuchmizhak
- Institute of Automation and Control Processes, Far Eastern Branch, Russian Academy of Science, 690041 Vladivostok, Russia
- Far Eastern Federal University, 690922 Vladivostok, Russia
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Kopřivová H, Kiss K, Krbal L, Stejskal V, Buday J, Pořízka P, Kaška M, Ryška A, Kaiser J. Imaging the elemental distribution within human malignant melanomas using Laser-Induced Breakdown Spectroscopy. Anal Chim Acta 2024; 1310:342663. [PMID: 38811130 DOI: 10.1016/j.aca.2024.342663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/20/2024] [Accepted: 04/27/2024] [Indexed: 05/31/2024]
Abstract
The diagnosis of malignant melanoma, often an inconspicuous but highly aggressive tumor, is most commonly done by histological examination, while additional diagnostic methods on the level of elements and molecules are constantly being developed. Several studies confirmed differences in the chemical composition of healthy and tumor tissue. Our study presents the potential of the LIBS (Laser-Induced-Breakdown Spectroscopy) technique as a diagnostic tool in malignant melanoma (MM) based on the quantitative changes in elemental composition in cancerous tissue. Our patient group included 17 samples of various types of malignant melanoma and one sample of healthy skin tissue as a control. To achieve a clear perception of results, we have selected two biogenic elements (calcium and magnesium), which showed a dissimilar distribution in cancerous tissue from its healthy surroundings. Moreover, we observed indications of different concentrations of these elements in different subtypes of malignant melanoma, a hypothesis that requires confirmation in a more extensive sample set. The information provided by the LIBS Imaging method could potentially be helpful not only in the diagnostics of tumor tissue but also be beneficial in broadening the knowledge about the tumor itself.
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Affiliation(s)
- Hana Kopřivová
- Central European Institute of Technology (CEITEC), Brno University of Technology, Purkyňova 123, 612 00, Brno, Czech Republic
| | - Kateřina Kiss
- Charles University, Faculty of Medicine in Hradec Kralove, Šimkova 870, 500 03, Hradec Králové, Czech Republic; Charles University, Third Faculty of Medicine, Department of Plastic Surgery, Ruská 2411, 100 00, Praha 10, Czech Republic; Surgical Department, University Hospital Hradec Králové, Sokolská 571, 500 05, Hradec Králové, Czech Republic
| | - Lukáš Krbal
- Charles University, Faculty of Medicine in Hradec Kralove, Šimkova 870, 500 03, Hradec Králové, Czech Republic; The Fingerland Department of Pathology, Faculty of Medicine at Charles University and University Hospital, Sokolská 581, 500 05, Hradec Králové, Czech Republic
| | - Václav Stejskal
- Charles University, Faculty of Medicine in Hradec Kralove, Šimkova 870, 500 03, Hradec Králové, Czech Republic; The Fingerland Department of Pathology, Faculty of Medicine at Charles University and University Hospital, Sokolská 581, 500 05, Hradec Králové, Czech Republic
| | - Jakub Buday
- Faculty of Mechanical Engineering (FME), Brno University of Technology, Technická 2 896, 616 69, Brno, Czech Republic
| | - Pavel Pořízka
- Central European Institute of Technology (CEITEC), Brno University of Technology, Purkyňova 123, 612 00, Brno, Czech Republic; Faculty of Mechanical Engineering (FME), Brno University of Technology, Technická 2 896, 616 69, Brno, Czech Republic.
| | - Milan Kaška
- Charles University, Faculty of Medicine in Hradec Kralove, Šimkova 870, 500 03, Hradec Králové, Czech Republic; Surgical Department, University Hospital Hradec Králové, Sokolská 571, 500 05, Hradec Králové, Czech Republic
| | - Aleš Ryška
- The Fingerland Department of Pathology, Faculty of Medicine at Charles University and University Hospital, Sokolská 581, 500 05, Hradec Králové, Czech Republic
| | - Jozef Kaiser
- Central European Institute of Technology (CEITEC), Brno University of Technology, Purkyňova 123, 612 00, Brno, Czech Republic; Faculty of Mechanical Engineering (FME), Brno University of Technology, Technická 2 896, 616 69, Brno, Czech Republic
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Ranasinghe JC, Wang Z, Huang S. Unveiling brain disorders using liquid biopsy and Raman spectroscopy. NANOSCALE 2024; 16:11879-11913. [PMID: 38845582 PMCID: PMC11290551 DOI: 10.1039/d4nr01413h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Abstract
Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.
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Affiliation(s)
- Jeewan C Ranasinghe
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
| | - Ziyang Wang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
| | - Shengxi Huang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
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Zhao J, Lui H, Kalia S, Lee TK, Zeng H. Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation. Front Oncol 2024; 14:1320220. [PMID: 38962264 PMCID: PMC11219827 DOI: 10.3389/fonc.2024.1320220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 05/23/2024] [Indexed: 07/05/2024] Open
Abstract
Background Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.
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Affiliation(s)
- Jianhua Zhao
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Harvey Lui
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Sunil Kalia
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
| | - Tim K. Lee
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
| | - Haishan Zeng
- Photomedicine Institute, Department of Dermatology and Skin Science, University of British Columbia and Vancouver Coastal Health Research Institute, Vancouver, BC, Canada
- BC Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada
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Troncoso-Afonso L, Vinnacombe-Willson GA, García-Astrain C, Liz-Márzan LM. SERS in 3D cell models: a powerful tool in cancer research. Chem Soc Rev 2024; 53:5118-5148. [PMID: 38607302 PMCID: PMC11104264 DOI: 10.1039/d3cs01049j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Indexed: 04/13/2024]
Abstract
Unraveling the cellular and molecular mechanisms underlying tumoral processes is fundamental for the diagnosis and treatment of cancer. In this regard, three-dimensional (3D) cancer cell models more realistically mimic tumors compared to conventional 2D cell cultures and are more attractive for performing such studies. Nonetheless, the analysis of such architectures is challenging because most available techniques are destructive, resulting in the loss of biochemical information. On the contrary, surface-enhanced Raman spectroscopy (SERS) is a non-invasive analytical tool that can record the structural fingerprint of molecules present in complex biological environments. The implementation of SERS in 3D cancer models can be leveraged to track therapeutics, the production of cancer-related metabolites, different signaling and communication pathways, and to image the different cellular components and structural features. In this review, we highlight recent progress in the use of SERS for the evaluation of cancer diagnosis and therapy in 3D tumoral models. We outline strategies for the delivery and design of SERS tags and shed light on the possibilities this technique offers for studying different cellular processes, through either biosensing or bioimaging modalities. Finally, we address current challenges and future directions, such as overcoming the limitations of SERS and the need for the development of user-friendly and robust data analysis methods. Continued development of SERS 3D bioimaging and biosensing systems, techniques, and analytical strategies, can provide significant contributions for early disease detection, novel cancer therapies, and the realization of patient-tailored medicine.
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Affiliation(s)
- Lara Troncoso-Afonso
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
- Department of Applied Chemistry, University of the Basque Country, 20018 Donostia-San Sebastián, Gipuzkoa, Spain
| | - Gail A Vinnacombe-Willson
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
| | - Clara García-Astrain
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería Biomateriales, y Nanomedicina (CIBER-BBN), Paseo de Miramón 182, 20014 Donostia-San Sebastián, Spain
| | - Luis M Liz-Márzan
- BioNanoPlasmonics Laboratory, CIC biomaGUNE, Basque Research and Technology Alliance (BRTA), 20014 Donostia-San Sebastián, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería Biomateriales, y Nanomedicina (CIBER-BBN), Paseo de Miramón 182, 20014 Donostia-San Sebastián, Spain
- Ikerbasque Basque Foundation for Science, 48013 Bilbao, Spain
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Matthies L, Amir-Kabirian H, Gebrekidan MT, Braeuer AS, Speth US, Smeets R, Hagel C, Gosau M, Knipfer C, Friedrich RE. Raman difference spectroscopy and U-Net convolutional neural network for molecular analysis of cutaneous neurofibroma. PLoS One 2024; 19:e0302017. [PMID: 38603731 PMCID: PMC11008861 DOI: 10.1371/journal.pone.0302017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 03/26/2024] [Indexed: 04/13/2024] Open
Abstract
In Neurofibromatosis type 1 (NF1), peripheral nerve sheaths tumors are common, with cutaneous neurofibromas resulting in significant aesthetic, painful and functional problems requiring surgical removal. To date, determination of adequate surgical resection margins-complete tumor removal while attempting to preserve viable tissue-remains largely subjective. Thus, residual tumor extension beyond surgical margins or recurrence of the disease may frequently be observed. Here, we introduce Shifted-Excitation Raman Spectroscopy in combination with deep neural networks for the future perspective of objective, real-time diagnosis, and guided surgical ablation. The obtained results are validated through established histological methods. In this study, we evaluated the discrimination between cutaneous neurofibroma (n = 9) and adjacent physiological tissues (n = 25) in 34 surgical pathological specimens ex vivo at a total of 82 distinct measurement loci. Based on a convolutional neural network (U-Net), the mean raw Raman spectra (n = 8,200) were processed and refined, and afterwards the spectral peaks were assigned to their respective molecular origin. Principal component and linear discriminant analysis was used to discriminate cutaneous neurofibromas from physiological tissues with a sensitivity of 100%, specificity of 97.3%, and overall classification accuracy of 97.6%. The results enable the presented optical, non-invasive technique in combination with artificial intelligence as a promising candidate to ameliorate both, diagnosis and treatment of patients affected by cutaneous neurofibroma and NF1.
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Affiliation(s)
- Levi Matthies
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Mildred Scheel Cancer Career Center HaTriCS4, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hendrik Amir-Kabirian
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Medhanie T. Gebrekidan
- Institute of Thermal-, Environmental- and Resources‘ Process Engineering, Technische Universität Bergakademie Freiberg, Freiberg, Germany
| | - Andreas S. Braeuer
- Institute of Thermal-, Environmental- and Resources‘ Process Engineering, Technische Universität Bergakademie Freiberg, Freiberg, Germany
| | - Ulrike S. Speth
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Ralf Smeets
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Division of “Regenerative Orofacial Medicine”, Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Hagel
- Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martin Gosau
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Christian Knipfer
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Reinhard E. Friedrich
- Department of Oral and Maxillofacial Surgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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Taha BA, Addie AJ, Kadhim AC, Azzahran AS, Haider AJ, Chaudhary V, Arsad N. Photonics-powered augmented reality skin electronics for proactive healthcare: multifaceted opportunities. Mikrochim Acta 2024; 191:250. [PMID: 38587660 DOI: 10.1007/s00604-024-06314-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 03/18/2024] [Indexed: 04/09/2024]
Abstract
Rapid technological advancements have created opportunities for new solutions in various industries, including healthcare. One exciting new direction in this field of innovation is the combination of skin-based technologies and augmented reality (AR). These dermatological devices allow for the continuous and non-invasive measurement of vital signs and biomarkers, enabling the real-time diagnosis of anomalies, which have applications in telemedicine, oncology, dermatology, and early diagnostics. Despite its many potential benefits, there is a substantial information vacuum regarding using flexible photonics in conjunction with augmented reality for medical purposes. This review explores the current state of dermal augmented reality and flexible optics in skin-conforming sensing platforms by examining the obstacles faced thus far, including technical hurdles, demanding clinical validation standards, and problems with user acceptance. Our main areas of interest are skills, chiroptical properties, and health platform applications, such as optogenetic pixels, spectroscopic imagers, and optical biosensors. My skin-enhanced spherical dichroism and powerful spherically polarized light enable thorough physical inspection with these augmented reality devices: diabetic tracking, skin cancer diagnosis, and cardiovascular illness: preventative medicine, namely blood pressure screening. We demonstrate how to accomplish early prevention using case studies and emergency detection. Finally, it addresses real-world obstacles that hinder fully realizing these materials' extraordinary potential in advancing proactive and preventative personalized medicine, including technical constraints, clinical validation gaps, and barriers to widespread adoption.
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Affiliation(s)
- Bakr Ahmed Taha
- Photonics Technology Lab, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Malaysia.
| | - Ali J Addie
- Center of Advanced Materials/Directorate of Materials Research/Ministry of Science and Technology, Baghdad, Iraq
| | - Ahmed C Kadhim
- Communication Engineering Department, University of Technology, Baghdad, Iraq
| | - Ahmad S Azzahran
- Electrical Engineering Department, Northern Border University, Arar, Kingdom of Saudi Arabia.
| | - Adawiya J Haider
- Applied Sciences Department/Laser Science and Technology Branch, University of Technology, Baghdad, Iraq
| | - Vishal Chaudhary
- Research Cell &, Department of Physics, Bhagini Nivedita College, University of Delhi, New Delhi, 110045, India
| | - Norhana Arsad
- Photonics Technology Lab, Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Malaysia.
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Ilchenko O, Pilhun Y, Kutsyk A, Slobodianiuk D, Goksel Y, Dumont E, Vaut L, Mazzoni C, Morelli L, Boisen S, Stergiou K, Aulin Y, Rindzevicius T, Andersen TE, Lassen M, Mundhada H, Jendresen CB, Philipsen PA, Hædersdal M, Boisen A. Optics miniaturization strategy for demanding Raman spectroscopy applications. Nat Commun 2024; 15:3049. [PMID: 38589380 PMCID: PMC11001912 DOI: 10.1038/s41467-024-47044-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 03/12/2024] [Indexed: 04/10/2024] Open
Abstract
Raman spectroscopy provides non-destructive, label-free quantitative studies of chemical compositions at the microscale as used on NASA's Perseverance rover on Mars. Such capabilities come at the cost of high requirements for instrumentation. Here we present a centimeter-scale miniaturization of a Raman spectrometer using cheap non-stabilized laser diodes, densely packed optics, and non-cooled small sensors. The performance is comparable with expensive bulky research-grade Raman systems. It has excellent sensitivity, low power consumption, perfect wavenumber, intensity calibration, and 7 cm-1 resolution within the 400-4000 cm-1 range using a built-in reference. High performance and versatility are demonstrated in use cases including quantification of methanol in beverages, in-vivo Raman measurements of human skin, fermentation monitoring, chemical Raman mapping at sub-micrometer resolution, quantitative SERS mapping of the anti-cancer drug methotrexate and in-vitro bacteria identification. We foresee that the miniaturization will allow realization of super-compact Raman spectrometers for integration in smartphones and medical devices, democratizing Raman technology.
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Affiliation(s)
- Oleksii Ilchenko
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark.
- Lightnovo ApS, Birkerød, Denmark.
| | - Yurii Pilhun
- Lightnovo ApS, Birkerød, Denmark
- Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
| | - Andrii Kutsyk
- Lightnovo ApS, Birkerød, Denmark
- Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
- Technical University of Denmark, Department of Energy Conversion and Storage, Kgs. Lyngby, Denmark
| | - Denys Slobodianiuk
- Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
- Institute of Magnetism, Kyiv, Ukraine
| | - Yaman Goksel
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | - Elodie Dumont
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | - Lukas Vaut
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | - Chiara Mazzoni
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | - Lidia Morelli
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | | | | | | | - Tomas Rindzevicius
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
| | - Thomas Emil Andersen
- Department of Clinical Microbiology, Odense University Hospital and Research Unit of Clinical Microbiology, University of Southern Denmark, Odense, Denmark
| | | | | | | | | | - Merete Hædersdal
- Department of Dermatology, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Copenhagen University, Copenhagen, Denmark
| | - Anja Boisen
- Technical University of Denmark, Department of Health Technology, Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics, Kgs. Lyngby, Denmark
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Nieuwoudt M, Jarrett P, Matthews H, Locke M, Bonesi M, Burnett B, Holtkamp H, Aguergaray C, Mautner I, Minnee T, Simpson MC. Portable System for In-Clinic Differentiation of Skin Cancers from Benign Skin Lesions and Inflammatory Dermatoses. JID INNOVATIONS 2024; 4:100238. [PMID: 38274304 PMCID: PMC10808988 DOI: 10.1016/j.xjidi.2023.100238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 09/04/2023] [Accepted: 09/06/2023] [Indexed: 01/27/2024] Open
Abstract
The exquisite sensitivity of Raman spectroscopy for detecting biomolecular changes in skin cancer has previously been explored; however, this mostly required analysis of excised tissue samples using bulky, immobile laboratory instrumentation. In this study, the technique was translated for clinical use with a portable Raman system and customized fiber optic probe and applied to differentiation of skin cancers from benign lesions and inflammatory dermatoses. The aim was to provide an easy-to-use, easy-to-manage assessment tool for clinicians to use in their daily patient examination routine to perform rapid Raman measurements of skin lesions in vivo. Using this system, >867 spectra were measured in vivo from 330 patients with a wide variety of different benign skin lesions (n = 603), inflammatory dermatoses (n = 140), and skin cancers (n = 124). Ethnicities represented were 70% European; 16% Asian; 6% Māori; 5% Pacific people; and 4% Middle East, Latin American, and African. Accurate differentiation of skin cancers from benign lesions and inflammatory dermatoses was achieved using partial least squares discriminant analysis, with area under curve for the receiver operator curves for external validation sets ranging from 0.916 to 0.958. This study shows evidence for robust clinical translation of Raman spectroscopy for rapid, accurate diagnosis of skin cancer.
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Affiliation(s)
- Michel Nieuwoudt
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
- The Photon Factory, University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand
| | - Paul Jarrett
- Department of Dermatology, Middlemore Hospital, Auckland, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Hannah Matthews
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
- The Photon Factory, University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand
| | - Michelle Locke
- Department of Plastic Surgery, Middlemore Hospital, Auckland, New Zealand
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Marco Bonesi
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
- The Photon Factory, University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand
- Department of Physics, University of Auckland, Auckland, New Zealand
| | - Brydon Burnett
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
- The Photon Factory, University of Auckland, Auckland, New Zealand
| | - Hannah Holtkamp
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
- The Photon Factory, University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
| | - Claude Aguergaray
- The Photon Factory, University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand
- Department of Physics, University of Auckland, Auckland, New Zealand
| | - Ira Mautner
- The Photon Factory, University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand
- Department of Physics, University of Auckland, Auckland, New Zealand
| | - Thom Minnee
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
- The Photon Factory, University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand
| | - M. Cather Simpson
- School of Chemical Sciences, University of Auckland, Auckland, New Zealand
- The Photon Factory, University of Auckland, Auckland, New Zealand
- The Dodd Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
- The MacDiarmid Institute for Advanced Materials and Nanotechnology, Wellington, New Zealand
- Department of Physics, University of Auckland, Auckland, New Zealand
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10
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Rimskaya E, Shelygina S, Timurzieva A, Saraeva I, Perevedentseva E, Melnik N, Kudrin K, Reshetov D, Kudryashov S. Multispectral Raman Differentiation of Malignant Skin Neoplasms In Vitro: Search for Specific Biomarkers and Optimal Wavelengths. Int J Mol Sci 2023; 24:14748. [PMID: 37834196 PMCID: PMC10572672 DOI: 10.3390/ijms241914748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/20/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
Confocal scanning Raman and photoluminescence (PL) microspectroscopy is a structure-sensitive optical method that allows the non-invasive analysis of biomarkers in the skin tissue. We used it to perform in vitro diagnostics of different malignant skin neoplasms at several excitation wavelengths (532, 785 and 1064 nm). Distinct spectral differences were noticed in the Raman spectra of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), compared with healthy skin. Our analysis of Raman/PL spectra at the different excitation wavelengths enabled us to propose two novel wavelength-independent spectral criteria (intensity ratios for 1302 cm-1 and 1445 cm-1 bands, 1745 cm-1 and 1445 cm-1 bands), related to the different vibrational "fingerprints" of cell membrane lipids as biomarkers, which was confirmed by the multivariate curve resolution (MCR) technique. These criteria allowed us to differentiate healthy skin from BCC and SCC with sensitivity and specificity higher than 95%, demonstrating high clinical importance in the differential diagnostics of skin tumors.
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Affiliation(s)
- Elena Rimskaya
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (S.S.); (A.T.); (I.S.); (E.P.); (N.M.); (K.K.)
| | - Svetlana Shelygina
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (S.S.); (A.T.); (I.S.); (E.P.); (N.M.); (K.K.)
| | - Alina Timurzieva
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (S.S.); (A.T.); (I.S.); (E.P.); (N.M.); (K.K.)
- Semashko National Research Institute of Public Health, 105064 Moscow, Russia
| | - Irina Saraeva
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (S.S.); (A.T.); (I.S.); (E.P.); (N.M.); (K.K.)
| | - Elena Perevedentseva
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (S.S.); (A.T.); (I.S.); (E.P.); (N.M.); (K.K.)
| | - Nikolay Melnik
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (S.S.); (A.T.); (I.S.); (E.P.); (N.M.); (K.K.)
| | - Konstantin Kudrin
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (S.S.); (A.T.); (I.S.); (E.P.); (N.M.); (K.K.)
- Department of Oncology, Radiotherapy and Reconstructive Surgery, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
| | - Dmitry Reshetov
- Department of Oncology and Radiation Therapy, Evdokimov Moscow State University of Medicine and Dentistry, 127473 Moscow, Russia;
| | - Sergey Kudryashov
- Lebedev Physical Institute, 119991 Moscow, Russia; (E.R.); (S.S.); (A.T.); (I.S.); (E.P.); (N.M.); (K.K.)
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11
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Zhang S, Qi Y, Tan SPH, Bi R, Olivo M. Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review. BIOSENSORS 2023; 13:bios13050557. [PMID: 37232918 DOI: 10.3390/bios13050557] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.
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Affiliation(s)
- Shuyan Zhang
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Yi Qi
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Sonia Peng Hwee Tan
- Department of Biomedical Engineering, National University of Singapore (NUS), 4 Engineering Drive 3 Block 4, #04-08, Singapore 117583, Singapore
| | - Renzhe Bi
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
| | - Malini Olivo
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos #07-01, Singapore 138634, Singapore
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12
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Khristoforova YA, Bratchenko LA, Skuratova MA, Lebedeva EA, Lebedev PA, Bratchenko IA. Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis. JOURNAL OF BIOPHOTONICS 2023:e202300016. [PMID: 36999197 DOI: 10.1002/jbio.202300016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/09/2023] [Accepted: 03/28/2023] [Indexed: 06/19/2023]
Abstract
This work aims at studying Raman spectroscopy in combination with chemometrics as an alternative fast noninvasive method to detect chronic heart failure (CHF) cases. Optical analysis is focused on the changes in the spectral features associated with the biochemical composition changes of skin tissues. A portable spectroscopy setup with the 785 nm excitation wavelength was used to record skin Raman features. In this in vivo study, 127 patients and 57 healthy volunteers were involved in measuring skin spectral features by Raman spectroscopy. The spectral data were analyzed with a projection on the latent structures and discriminant analysis. 202 skin spectra of patients with CHF and 90 skin spectra of healthy volunteers were classified with 0.888 ROC AUC for the 10-fold cross validated algorithm. To identify CHF cases, the performance of the proposed classifier was verified by means of a new test set that is equal to 0.917 ROC AUC.
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Affiliation(s)
- Yulia A Khristoforova
- Department of Laser and Biotechnical Systems, Samara National Research University, Samara, Russia
| | - Lyudmila A Bratchenko
- Department of Laser and Biotechnical Systems, Samara National Research University, Samara, Russia
| | - Maria A Skuratova
- Cardiology Department, City Clinical Hospital № 1 named after N. I. Pirogov, Samara, Russia
| | - Elena A Lebedeva
- Cardiology Department, City Clinical Hospital № 1 named after N. I. Pirogov, Samara, Russia
| | - Petr A Lebedev
- Therapy chair of Postgraduate Department, Samara State Medical University, Samara, Russia
| | - Ivan A Bratchenko
- Department of Laser and Biotechnical Systems, Samara National Research University, Samara, Russia
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13
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Bratchenko LA, Moryatov AA, Bratchenko IA. Raman-based optical biopsy shortens the clinical pathway of the patient: Example of pigmented skin neoplasm diagnosis. PHOTODERMATOLOGY, PHOTOIMMUNOLOGY & PHOTOMEDICINE 2023; 39:169-171. [PMID: 36583286 DOI: 10.1111/phpp.12862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 11/30/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022]
Affiliation(s)
- Lyudmila A Bratchenko
- Department of Laser and Biotechnical Systems, Samara National Research University, Samara, Russia
| | | | - Ivan A Bratchenko
- Department of Laser and Biotechnical Systems, Samara National Research University, Samara, Russia
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14
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Bratchenko IA, Bratchenko LA. Comment on "Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes". JOURNAL OF BIOPHOTONICS 2023; 16:e202200272. [PMID: 36306108 DOI: 10.1002/jbio.202200272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
This paper comments recent findings about Raman spectroscopy application for in vivo noninvasive diabetes detection, published in the Journal of Biophotonics by E. Guevara et al. (J. Biophotonics 2022, 15, e202200055). The proposed results may be not entirely correct due to possible overestimation of classification models and absence of additional information regarding age of tested volunteers.
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Affiliation(s)
- Ivan A Bratchenko
- Laser and Biotechnical Systems Department, Samara National Research University, Samara, Russia
| | - Lyudmila A Bratchenko
- Laser and Biotechnical Systems Department, Samara National Research University, Samara, Russia
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15
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Matveeva I, Bratchenko I, Khristoforova Y, Bratchenko L, Moryatov A, Kozlov S, Kaganov O, Zakharov V. Multivariate Curve Resolution Alternating Least Squares Analysis of In Vivo Skin Raman Spectra. SENSORS (BASEL, SWITZERLAND) 2022; 22:9588. [PMID: 36559957 PMCID: PMC9785721 DOI: 10.3390/s22249588] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/02/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
In recent years, Raman spectroscopy has been used to study biological tissues. However, the analysis of experimental Raman spectra is still challenging, since the Raman spectra of most biological tissue components overlap significantly and it is difficult to separate individual components. New methods of analysis are needed that would allow for the decomposition of Raman spectra into components and the evaluation of their contribution. The aim of our work is to study the possibilities of the multivariate curve resolution alternating least squares (MCR-ALS) method for the analysis of skin tissues in vivo. We investigated the Raman spectra of human skin recorded using a portable conventional Raman spectroscopy setup. The MCR-ALS analysis was performed for the Raman spectra of normal skin, keratosis, basal cell carcinoma, malignant melanoma, and pigmented nevus. We obtained spectral profiles corresponding to the contribution of the optical system and skin components: melanin, proteins, lipids, water, etc. The obtained results show that the multivariate curve resolution alternating least squares analysis can provide new information on the biochemical profiles of skin tissues. Such information may be used in medical diagnostics to analyze Raman spectra with a low signal-to-noise ratio, as well as in various fields of science and industry for preprocessing Raman spectra to remove parasitic components.
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Affiliation(s)
- Irina Matveeva
- Department of Laser and Biotechnical Systems, Samara University, Samara 443086, Russia
| | - Ivan Bratchenko
- Department of Laser and Biotechnical Systems, Samara University, Samara 443086, Russia
| | - Yulia Khristoforova
- Department of Laser and Biotechnical Systems, Samara University, Samara 443086, Russia
| | - Lyudmila Bratchenko
- Department of Laser and Biotechnical Systems, Samara University, Samara 443086, Russia
| | - Alexander Moryatov
- Department of Oncology, Samara State Medical University, Samara 443099, Russia
- Department of Visual Localization Tumors, Samara Regional Clinical Oncology Dispensary, Samara 443031, Russia
| | - Sergey Kozlov
- Department of Oncology, Samara State Medical University, Samara 443099, Russia
- Department of Visual Localization Tumors, Samara Regional Clinical Oncology Dispensary, Samara 443031, Russia
| | - Oleg Kaganov
- Department of Oncology, Samara State Medical University, Samara 443099, Russia
- Department of Visual Localization Tumors, Samara Regional Clinical Oncology Dispensary, Samara 443031, Russia
| | - Valery Zakharov
- Department of Laser and Biotechnical Systems, Samara University, Samara 443086, Russia
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16
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Khristoforova Y, Bratchenko I, Bratchenko L, Moryatov A, Kozlov S, Kaganov O, Zakharov V. Combination of Optical Biopsy with Patient Data for Improvement of Skin Tumor Identification. Diagnostics (Basel) 2022; 12:diagnostics12102503. [PMID: 36292192 PMCID: PMC9600416 DOI: 10.3390/diagnostics12102503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 11/29/2022] Open
Abstract
In this study, patient data were combined with Raman and autofluorescence spectral parameters for more accurate identification of skin tumors. The spectral and patient data of skin tumors were classified by projection on latent structures and discriminant analysis. The importance of patient risk factors was determined using statistical improvement of ROC AUCs when spectral parameters were combined with risk factors. Gender, age and tumor localization were found significant for classification of malignant versus benign neoplasms, resulting in improvement of ROC AUCs from 0.610 to 0.818 (p < 0.05). To distinguish melanoma versus pigmented skin tumors, the same factors significantly improved ROC AUCs from 0.709 to 0.810 (p < 0.05) when analyzed together according to the spectral data, but insignificantly (p > 0.05) when analyzed individually. For classification of melanoma versus seborrheic keratosis, no statistical improvement of ROC AUC was observed when the patient data were added to the spectral data. In all three classification models, additional risk factors such as occupational hazards, family history, sun exposure, size, and personal history did not statistically improve the ROC AUCs. In summary, combined analysis of spectral and patient data can be significant for certain diagnostic tasks: patient data demonstrated the distribution of skin tumor incidence in different demographic groups, whereas tumors within each group were distinguished using the spectral differences.
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Affiliation(s)
- Yulia Khristoforova
- Laser and Biotechnical Systems Department, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
- Correspondence:
| | - Ivan Bratchenko
- Laser and Biotechnical Systems Department, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
| | - Lyudmila Bratchenko
- Laser and Biotechnical Systems Department, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
| | - Alexander Moryatov
- Department of Oncology, Samara State Medical University, 89 Chapaevskaya Str., 443099 Samara, Russia
| | - Sergey Kozlov
- Department of Oncology, Samara State Medical University, 89 Chapaevskaya Str., 443099 Samara, Russia
| | - Oleg Kaganov
- Department of Oncology, Samara State Medical University, 89 Chapaevskaya Str., 443099 Samara, Russia
| | - Valery Zakharov
- Laser and Biotechnical Systems Department, Samara National Research University, 34 Moskovskoe Shosse, 443086 Samara, Russia
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17
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Wilkinson EL, Ashton L, Kerns JG, Allinson SL, Mort RL. Fingerprinting of skin cells by live cell Raman spectroscopy reveals melanoma cell heterogeneity and cell-type-specific responses to UVR. Exp Dermatol 2022; 31:1543-1553. [PMID: 35700136 PMCID: PMC9796253 DOI: 10.1111/exd.14625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/26/2022] [Accepted: 06/09/2022] [Indexed: 01/01/2023]
Abstract
Raman spectroscopy is an emerging dermatological technique with the potential to discriminate biochemically between cell types in a label-free and non-invasive manner. Here, we use live single-cell Raman spectroscopy and principal component analysis (PCA) to fingerprint mouse melanoblasts, melanocytes, keratinocytes and melanoma cells. We show the differences in their spectra are attributable to biomarkers in the melanin biosynthesis pathway and that melanoma cells are a heterogeneous population that sit on a trajectory between undifferentiated melanoblasts and differentiated melanocytes. We demonstrate the utility of Raman spectroscopy as a highly sensitive tool to probe the melanin biosynthesis pathway and its immediate response to ultraviolet (UV) irradiation revealing previously undescribed opposing responses to UVA and UVB irradiation in melanocytes. Finally, we identify melanocyte-specific accumulation of β-carotene correlated with a stabilisation of the UVR response in lipids and proteins consistent with a β-carotene-mediated photoprotective mechanism. In summary, our data show that Raman spectroscopy can be used to determine the differentiation status of cells of the melanocyte lineage and describe the immediate and temporal biochemical changes associated with UV exposure which differ depending on cell type, differentiation status and competence to synthesise melanin. Our work uniquely applies Raman spectroscopy to discriminate between cell types by biological function and differentiation status while they are growing in culture. In doing so, we demonstrate for the first time its utility as a tool with which to probe the melanin biosynthesis pathway.
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Affiliation(s)
- Emma L. Wilkinson
- Division of Biomedical and Life Sciences, Faculty of Health and MedicineLancaster UniversityLancasterUK
| | - Lorna Ashton
- Department of ChemistryLancaster UniversityLancasterUK
| | - Jemma G. Kerns
- Lancaster Medical School, Faculty of Health and MedicineLancaster UniversityLancasterUK
| | - Sarah L. Allinson
- Division of Biomedical and Life Sciences, Faculty of Health and MedicineLancaster UniversityLancasterUK
| | - Richard L. Mort
- Division of Biomedical and Life Sciences, Faculty of Health and MedicineLancaster UniversityLancasterUK
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18
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Bratchenko IA, Bratchenko LA. Comment on "Quantification of glycated hemoglobin and glucose in vivo using Raman spectroscopy and artificial neural networks". Lasers Med Sci 2022; 37:3753-3754. [PMID: 36167863 DOI: 10.1007/s10103-022-03650-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 10/14/2022]
Affiliation(s)
- Ivan A Bratchenko
- Laser and Biotechnical Systems Department, Samara National Research University, Moskovskoe shosse 34, Samara, 443086, Russia.
| | - Lyudmila A Bratchenko
- Laser and Biotechnical Systems Department, Samara National Research University, Moskovskoe shosse 34, Samara, 443086, Russia
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19
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La Salvia M, Torti E, Leon R, Fabelo H, Ortega S, Balea-Fernandez F, Martinez-Vega B, Castaño I, Almeida P, Carretero G, Hernandez JA, Callico GM, Leporati F. Neural Networks-Based On-Site Dermatologic Diagnosis through Hyperspectral Epidermal Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:7139. [PMID: 36236240 PMCID: PMC9571453 DOI: 10.3390/s22197139] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/15/2022] [Accepted: 09/18/2022] [Indexed: 06/16/2023]
Abstract
Cancer originates from the uncontrolled growth of healthy cells into a mass. Chromophores, such as hemoglobin and melanin, characterize skin spectral properties, allowing the classification of lesions into different etiologies. Hyperspectral imaging systems gather skin-reflected and transmitted light into several wavelength ranges of the electromagnetic spectrum, enabling potential skin-lesion differentiation through machine learning algorithms. Challenged by data availability and tiny inter and intra-tumoral variability, here we introduce a pipeline based on deep neural networks to diagnose hyperspectral skin cancer images, targeting a handheld device equipped with a low-power graphical processing unit for routine clinical testing. Enhanced by data augmentation, transfer learning, and hyperparameter tuning, the proposed architectures aim to meet and improve the well-known dermatologist-level detection performances concerning both benign-malignant and multiclass classification tasks, being able to diagnose hyperspectral data considering real-time constraints. Experiments show 87% sensitivity and 88% specificity for benign-malignant classification and specificity above 80% for the multiclass scenario. AUC measurements suggest classification performance improvement above 90% with adequate thresholding. Concerning binary segmentation, we measured skin DICE and IOU higher than 90%. We estimated 1.21 s, at most, consuming 5 Watts to segment the epidermal lesions with the U-Net++ architecture, meeting the imposed time limit. Hence, we can diagnose hyperspectral epidermal data assuming real-time constraints.
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Affiliation(s)
- Marco La Salvia
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
| | - Emanuele Torti
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
| | - Raquel Leon
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain
| | - Himar Fabelo
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain
| | - Samuel Ortega
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain
- Norwegian Institute of Food, Fisheries and Aquaculture Research (Nofima), 6122 Tromsø, Norway
| | - Francisco Balea-Fernandez
- Department of Psychology, Sociology and Social Work, University of Las Palmas de Gran Canaria, 35001 Las Palmas de Gran Canaria, Spain
| | - Beatriz Martinez-Vega
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain
| | - Irene Castaño
- Department of Dermatology, Hospital Universitario de Gran Canaria Doctor Negrín, Barranco de la Ballena, s/n, 35010 Las Palmas de Gran Canaria, Spain
| | - Pablo Almeida
- Department of Dermatology, Complejo Hospitalario Universitario Insular-Materno Infantil, Avenida Maritima del Sur, s/n, 35016 Las Palmas de Gran Canaria, Spain
| | - Gregorio Carretero
- Department of Dermatology, Hospital Universitario de Gran Canaria Doctor Negrín, Barranco de la Ballena, s/n, 35010 Las Palmas de Gran Canaria, Spain
| | - Javier A. Hernandez
- Department of Dermatology, Complejo Hospitalario Universitario Insular-Materno Infantil, Avenida Maritima del Sur, s/n, 35016 Las Palmas de Gran Canaria, Spain
| | - Gustavo M. Callico
- Research Institute for Applied Microelectronics (IUMA), University of Las Palmas de Gran Canaria (ULPGC), 35001 Las Palmas de Gran Canaria, Spain
| | - Francesco Leporati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
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20
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Lunter D, Klang V, Kocsis D, Varga-Medveczky Z, Berkó S, Erdő F. Novel aspects of Raman spectroscopy in skin research. Exp Dermatol 2022; 31:1311-1329. [PMID: 35837832 PMCID: PMC9545633 DOI: 10.1111/exd.14645] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/07/2022] [Accepted: 07/12/2022] [Indexed: 11/27/2022]
Abstract
The analytical technology of Raman spectroscopy has an almost 100‐year history. During this period, many modifications and developments happened in the method like discovery of laser, improvements in optical elements and sensitivity of spectrometer and also more advanced light detection systems. Many types of the innovative techniques appeared (e.g. Transmittance Raman spectroscopy, Coherent Raman Scattering microscopy, Surface‐Enhanced Raman scattering and Confocal Raman spectroscopy/microscopy). This review article gives a short description about these different Raman techniques and their possible applications. Then, a short statistical part is coming about the appearance of Raman spectroscopy in the scientific literature from the beginnings to these days. The third part of the paper shows the main application options of the technique (especially confocal Raman spectroscopy) in skin research, including skin composition analysis, drug penetration monitoring and analysis, diagnostic utilizations in dermatology and cosmeto‐scientific applications. At the end, the possible role of artificial intelligence in Raman data analysis and the regulatory aspect of these techniques in dermatology are briefly summarized. For the future of Raman Spectroscopy, increasing clinical relevance and in vivo applications can be predicted with spreading of non‐destructive methods and appearance with the most advanced instruments with rapid analysis time.
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Affiliation(s)
- Dominique Lunter
- University of Tübingen, Department of Pharmaceutical Technology, Institute of Pharmacy and Biochemistry, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Victoria Klang
- University of Vienna, Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology and Biopharmaceutics, Faculty of Life Sciences, Vienna, Austria
| | - Dorottya Kocsis
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Zsófia Varga-Medveczky
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Szilvia Berkó
- University of Szeged, Faculty of Pharmacy, Institute of Pharmaceutical Technology and Regulatory Affairs, Szeged, Hungary
| | - Franciska Erdő
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary.,University of Tours EA 6295 Nanomédicaments et Nanosondes, Tours, France
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21
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Bratchenko IA, Bratchenko LA, Khristoforova YA, Moryatov AA, Kozlov SV, Zakharov VP. Classification of skin cancer using convolutional neural networks analysis of Raman spectra. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106755. [PMID: 35349907 DOI: 10.1016/j.cmpb.2022.106755] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/21/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Skin cancer is the most common malignancy in whites accounting for about one third of all cancers diagnosed per year. Portable Raman spectroscopy setups for skin cancer "optical biopsy" are utilized to detect tumors based on their spectral features caused by the comparative presence of different chemical components. However, low signal-to-noise ratio in such systems may prevent accurate tumors classification. Thus, there is a challenge to develop methods for efficient skin tumors classification. METHODS We compare the performance of convolutional neural networks and the projection on latent structures with discriminant analysis for discriminating skin cancer using the analysis of Raman spectra with a high autofluorescence background stimulated by a 785 nm laser. We have registered the spectra of 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable Raman setup and created classification models both for convolutional neural networks and projection on latent structures approaches. To check the classification models stability, a 10-fold cross-validation was performed for all created models. To avoid models overfitting, the data was divided into a training set (80% of spectral dataset) and a test set (20% of spectral dataset). RESULTS The results for different classification tasks demonstrate that the convolutional neural networks significantly (p<0.01) outperforms the projection on latent structures. For the convolutional neural networks implementation we obtained ROC AUCs of 0.96 (0.94 - 0.97; 95% CI), 0.90 (0.85-0.94; 95% CI), and 0.92 (0.87 - 0.97; 95% CI) for classifying a) malignant vs benign tumors, b) melanomas vs pigmented tumors and c) melanomas vs seborrheic keratosis respectively. CONCLUSIONS The performance of the convolutional neural networks classification of skin tumors based on Raman spectra analysis is higher or comparable to the accuracy provided by trained dermatologists. The increased accuracy with the convolutional neural networks implementation is due to a more precise accounting of low intensity Raman bands in the intense autofluorescence background. The achieved high performance of skin tumors classifications with convolutional neural networks analysis opens a possibility for wide implementation of Raman setups in clinical setting.
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Affiliation(s)
- Ivan A Bratchenko
- Department of Laser and Biotechnical Systems, Samara University, 34 Moskovskoe Shosse, Samara, 443086, Russian Federation.
| | - Lyudmila A Bratchenko
- Department of Laser and Biotechnical Systems, Samara University, 34 Moskovskoe Shosse, Samara, 443086, Russian Federation
| | - Yulia A Khristoforova
- Department of Laser and Biotechnical Systems, Samara University, 34 Moskovskoe Shosse, Samara, 443086, Russian Federation
| | - Alexander A Moryatov
- Department of Oncology, Samara State Medical University, 159 Tashkentskaya Street, Samara, 443095, Russian Federation; Department of Visual Localization Tumors, Samara Regional Clinical Oncology Dispensary, 50 Solnechnaya Street, Samara, 443095, Russian Federation
| | - Sergey V Kozlov
- Department of Oncology, Samara State Medical University, 159 Tashkentskaya Street, Samara, 443095, Russian Federation; Department of Visual Localization Tumors, Samara Regional Clinical Oncology Dispensary, 50 Solnechnaya Street, Samara, 443095, Russian Federation
| | - Valery P Zakharov
- Department of Laser and Biotechnical Systems, Samara University, 34 Moskovskoe Shosse, Samara, 443086, Russian Federation
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22
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Bratchenko IA, Bratchenko LA. Comment on “Finding reduced Raman spectroscopy fingerprint of skin samples for melanoma diagnosis through machine learning”. Artif Intell Med 2022; 125:102252. [DOI: 10.1016/j.artmed.2022.102252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/29/2022] [Indexed: 11/30/2022]
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23
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Sloan-Dennison S, Laing S, Graham D, Faulds K. From Raman to SESORRS: moving deeper into cancer detection and treatment monitoring. Chem Commun (Camb) 2021; 57:12436-12451. [PMID: 34734952 PMCID: PMC8609625 DOI: 10.1039/d1cc04805h] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Raman spectroscopy is a non-invasive technique that allows specific chemical information to be obtained from various types of sample. The detailed molecular information that is present in Raman spectra permits monitoring of biochemical changes that occur in diseases, such as cancer, and can be used for the early detection and diagnosis of the disease, for monitoring treatment, and to distinguish between cancerous and non-cancerous biological samples. Several techniques have been developed to enhance the capabilities of Raman spectroscopy by improving detection sensitivity, reducing imaging times and increasing the potential applicability for in vivo analysis. The different Raman techniques each have their own advantages that can accommodate the alternative detection formats, allowing the techniques to be applied in several ways for the detection and diagnosis of cancer. This feature article discusses the various forms of Raman spectroscopy, how they have been applied for cancer detection, and the adaptation of the techniques towards their use for in vivo cancer detection and in clinical diagnostics. Despite the advances in Raman spectroscopy, the clinical application of the technique is still limited and certain challenges must be overcome to enable clinical translation. We provide an outlook on the future of the techniques in this area and what we believe is required to allow the potential of Raman spectroscopy to be achieved for clinical cancer diagnostics.
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Affiliation(s)
- Sian Sloan-Dennison
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George Street, Glasgow, G1 1RD, UK.
| | - Stacey Laing
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George Street, Glasgow, G1 1RD, UK.
| | - Duncan Graham
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George Street, Glasgow, G1 1RD, UK.
| | - Karen Faulds
- Department of Pure and Applied Chemistry, Technology and Innovation Centre, University of Strathclyde, 99 George Street, Glasgow, G1 1RD, UK.
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24
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Jung JM, Cho JY, Lee WJ, Chang SE, Lee MW, Won CH. Emerging Minimally Invasive Technologies for the Detection of Skin Cancer. J Pers Med 2021; 11:951. [PMID: 34683091 PMCID: PMC8538732 DOI: 10.3390/jpm11100951] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 09/23/2021] [Accepted: 09/23/2021] [Indexed: 12/23/2022] Open
Abstract
With the increasing incidence of skin cancer, many noninvasive technologies to detect its presence have been developed. This review focuses on reflectance confocal microscopy (RCM), optical coherence tomography (OCT), high-frequency ultrasound (HFUS), electrical impedance spectroscopy (EIS), pigmented lesion assay (PLA), and Raman spectroscopy (RS) and discusses the basic principle, clinical applications, advantages, and disadvantages of each technology. RCM provides high cellular resolution and has high sensitivity and specificity for the diagnosis of skin cancer. OCT provides lower resolution than RCM, although its evaluable depth is deeper than that of RCM. RCM and OCT may be useful in reducing the number of unnecessary biopsies, evaluating the tumor margin, and monitoring treatment response. HFUS can be mainly used to delineate tumor depths or margins and monitor the treatment response. EIS provides high sensitivity but low specificity for the diagnosis of skin malignancies. PLA, which is based on the genetic information of lesions, is applicable for the detection of melanoma with high sensitivity and moderate-to-high specificity. RS showed high accuracy for the diagnosis of skin cancer, although more clinical studies are required. Advances in these technologies for the diagnosis of skin cancer can lead to the realization of optimized and individualized treatments.
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Affiliation(s)
- Joon Min Jung
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea; (J.M.J.); (W.J.L.); (S.E.C.); (M.W.L.)
| | - Ji Young Cho
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea;
| | - Woo Jin Lee
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea; (J.M.J.); (W.J.L.); (S.E.C.); (M.W.L.)
| | - Sung Eun Chang
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea; (J.M.J.); (W.J.L.); (S.E.C.); (M.W.L.)
| | - Mi Woo Lee
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea; (J.M.J.); (W.J.L.); (S.E.C.); (M.W.L.)
| | - Chong Hyun Won
- Department of Dermatology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea; (J.M.J.); (W.J.L.); (S.E.C.); (M.W.L.)
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25
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Quantitative Multispectral Imaging Differentiates Melanoma from Seborrheic Keratosis. Diagnostics (Basel) 2021; 11:diagnostics11081315. [PMID: 34441250 PMCID: PMC8392390 DOI: 10.3390/diagnostics11081315] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/13/2021] [Accepted: 07/15/2021] [Indexed: 12/27/2022] Open
Abstract
Melanoma is a melanocytic tumor that is responsible for the most skin cancer-related deaths. By contrast, seborrheic keratosis (SK) is a very common benign lesion with a clinical picture that may resemble melanoma. We used a multispectral imaging device to distinguish these two entities, with the use of autofluorescence imaging with 405 nm and diffuse reflectance imaging with 525 and 660 narrow-band LED illumination. We analyzed intensity descriptors of the acquired images. These included ratios of intensity values of different channels, standard deviation and minimum/maximum values of intensity of the lesions. The pattern of the lesions was also assessed with the use of particle analysis. We found significantly higher intensity values in SKs compared with melanoma, especially with the use of the autofluorescence channel. Moreover, we found a significantly higher number of particles with high fluorescence in SKs. We created a parameter, the SK index, using these values to differentiate melanoma from SK with a sensitivity of 91.9% and specificity of 57.0%. In conclusion, this imaging technique is potentially applicable to distinguish melanoma from SK based on the analysis of various quantitative parameters. For this application, multispectral imaging could be used as a screening tool by general physicians and non-experts in the everyday practice.
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26
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Abstract
Raman spectroscopy has shown great potential in detecting nonmelanoma skin cancer accurately and quickly; however, little direct evidence exists on the sensitivity of measurements to the underlying anatomy. Here, we aimed to correlate Raman measurements directly to the underlying tissue anatomy. We acquired Raman spectra of ex vivo skin tissue from 25 patients undergoing Mohs surgery with a fiber probe. We utilized a previously developed biophysical model to extract key biomarkers in the skin from the Raman spectra. We then examined the correlations between the biomarkers and the major skin structures (including the dermis, sebaceous glands, hair follicles, fat, and two types of nonmelanoma skin cancer—basal cell carcinoma (BCC) and squamous cell carcinoma (SCC)). SCC had a significantly different concentration of keratin, collagen, and nucleic acid than normal structures, while ceramide differentiated BCC from normal structures. Our findings identified the key proteins, lipids, and nucleic acids that discriminate nonmelanoma tumors and healthy skin using Raman spectroscopy. These markers may be promising surgical guidance tools for detecting tumors in resection margins.
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