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Tamošiūnas M, Čiževskis O, Viškere D, Melderis M, Rubins U, Cugmas B. Multimodal Approach of Optical Coherence Tomography and Raman Spectroscopy Can Improve Differentiating Benign and Malignant Skin Tumors in Animal Patients. Cancers (Basel) 2022; 14:2820. [PMID: 35740486 DOI: 10.3390/cancers14122820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 05/30/2022] [Accepted: 06/06/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Skin and subcutaneous tumors are among the most frequent neoplasms in dogs and cats. We studied 51 samples of canine and feline skin, lipomas, soft tissue sarcomas, and mast cell tumors using a multimodal approach based on optical coherence tomography and Raman spectroscopy. A supervised machine learning algorithm detected malignant tumors with the sensitivity and specificity of 94% and 98%, respectively. The proposed multimodal algorithm is a novel approach in veterinary oncology that can outperform the existing clinical methods such as the fine-needle aspiration method. Abstract As in humans, cancer is one of the leading causes of companion animal mortality. Up to 30% of all canine and feline neoplasms appear on the skin or directly under it. There are only a few available studies that have investigated pet tumors by biophotonics techniques. In this study, we acquired 1115 optical coherence tomography (OCT) images of canine and feline skin, lipomas, soft tissue sarcomas, and mast cell tumors ex vivo, which were subsequently used for automated machine vision analysis. The OCT images were analyzed using a scanning window with a size of 53 × 53 μm. The distributions of the standard deviation, mean, range, and coefficient of variation values were acquired for each image. These distributions were characterized by their mean, standard deviation, and median values, resulting in 12 parameters in total. Additionally, 1002 Raman spectral measurements were made on the same samples, and features were generated by integrating the intensity of the most prominent peaks. Linear discriminant analysis (LDA) was used for sample classification, and sensitivities/specificities were acquired by leave-one-out cross-validation. Three datasets were analyzed—OCT, Raman, and combined. The combined OCT and Raman data enabled the best sample differentiation with the sensitivities of 0.968, 1, and 0.939 and specificities of 0.956, 1, and 0.977 for skin, lipomas, and malignant tumors, respectively. Based on these results, we concluded that the proposed multimodal approach, combining Raman and OCT data, can accurately distinguish between malignant and benign tissues.
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Hanna K, Krzoska E, Shaaban AM, Muirhead D, Abu-Eid R, Speirs V. Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects. Br J Cancer 2022; 126:1125-39. [PMID: 34893761 DOI: 10.1038/s41416-021-01659-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 11/11/2021] [Accepted: 11/25/2021] [Indexed: 12/26/2022] Open
Abstract
Despite significant improvements in the way breast cancer is managed and treated, it continues to persist as a leading cause of death worldwide. If detected and diagnosed early, when tumours are small and localised, there is a considerably higher chance of survival. However, current methods for detection and diagnosis lack the required sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages. Thus, there is a need to develop more rapid and reliable methods, capable of detecting disease earlier, for improved disease management and patient outcome. Raman spectroscopy is a non-destructive analytical technique that can rapidly provide highly specific information on the biochemical composition and molecular structure of samples. In cancer, it has the capacity to probe very early biochemical changes that accompany malignant transformation, even prior to the onset of morphological changes, to produce a fingerprint of disease. This review explores the application of Raman spectroscopy in breast cancer, including discussion on its capabilities in analysing both ex-vivo tissue and liquid biopsy samples, and its potential in vivo applications. The review also addresses current challenges and potential future uses of this technology in cancer research and translational clinical application.
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Oliva-Teles L, Pinto R, Vilarinho R, Carvalho AP, Moreira JA, Guimarães L. Environmental diagnosis with Raman Spectroscopy applied to diatoms. Biosens Bioelectron 2022; 198:113800. [PMID: 34838373 DOI: 10.1016/j.bios.2021.113800] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 10/04/2021] [Accepted: 11/12/2021] [Indexed: 12/30/2022]
Abstract
Freshwater quality has been changing due to the ever greater use of water resources and the contamination load resulting from human activities. Management of these systems, thus, requires constant diagnose of water quality with fast and efficient methodologies. The conventional methods adopted are, however, time-consuming, often very expensive, and require specialised expertise. Raman spectroscopy (RS) is a simple, fast and label-free technique that can be applied to environmental diagnosis using diatoms. Here, we developed a diagnostic method based on Raman spectroscopy applied to freshwater diatoms. For this, Raman spectra were recorded from diatoms of three lakes of a natural city park. The data acquired was analysed by chemometrics methods to describe the data (Partial Least Squares Regression), infer relationships in the dataset (Cluster Analysis) and produce classification models (Artificial Neural Network). The classification models developed diagnosed the lakes with excellent accuracy (89%) without requiring taxonomic information about the diatom species recorded. This study provides a proof-of-concept for the application of diatom Raman spectroscopy to diagnosing water quality, laying an important foundation for future environmental studies aiming at assessing freshwater systems, to be replicated at larger scales and to varied geographic settings.
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Affiliation(s)
- Luís Oliva-Teles
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros Do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal; Department of Biology, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal.
| | - Raquel Pinto
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros Do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal; Department of Biology, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal
| | - Rui Vilarinho
- IFIMUP, Department of Physics and Astronomy, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal
| | - António Paulo Carvalho
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros Do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal; Department of Biology, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal
| | - J Agostinho Moreira
- IFIMUP, Department of Physics and Astronomy, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal
| | - Laura Guimarães
- CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros Do Porto de Leixões, Avenida General Norton de Matos, s/n, 4450-208, Matosinhos, Portugal; Department of Biology, Faculty of Sciences of the University of Porto, Rua Do Campo Alegre, s/n, 4169-007, Porto, Portugal
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