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Libor L, Pécsy B, Szűcs E, Lantos J, Bakos A, Lázár G, Furák J. Effect of transbronchial or intravenous administration of indocyanine green on resection margins during near-infrared-guided segmentectomy: a review. Front Surg 2024; 11:1430100. [PMID: 39011052 PMCID: PMC11246956 DOI: 10.3389/fsurg.2024.1430100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 06/17/2024] [Indexed: 07/17/2024] Open
Abstract
For early-stage non-small cell lung cancer, surgical resection remains the best treatment option. Currently, sublobar resection, including segmentectomy, is recommended in these cases, as it provides a better quality of life with the same oncological outcomes; however, is requires adequate resection margins. Accurate preoperative planning and proper identification of the intersegmental planes during thoracic surgery are crucial for ensuring precise surgical management and adequate resection margins. Three dimensional computed tomography reconstruction and near-infrared-guided intersegmental plane identification can greatly facilitate the surgical procedures. Three-dimensional computed tomography reconstruction can simulate both the resection and resection margins. Indocyanine green is one of the most frequently used and affordable fluorophores. There are two ways to identify the intersegmental planes using indocyanine green: intravenous and transbronchial administration. Intravenous application is simple; however, its effectiveness may be affected by underlying lung disease, and it requires the isolation of segmental structures before administration. Transbronchial use requires appropriate bronchoscopic skills and preoperative planning; however, it also allows for delineation deep in the parenchyma and can be used for complex segmentectomies. Both methods can be used to ensure adequate resection margins and, therefore, achieve the correct oncological radicality of the surgical procedure. Here, we summarise these applications and provide an overview of their different possibilities.
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Affiliation(s)
- László Libor
- Department of Surgery, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Balázs Pécsy
- Department of Surgery, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Evelin Szűcs
- Department of Surgery, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - Judit Lantos
- Department of Neurology, Bács-Kiskun County Hospital, Kecskemet, Hungary
| | - Annamária Bakos
- Department of Nuclear Medicine, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - György Lázár
- Department of Surgery, Faculty of Medicine, University of Szeged, Szeged, Hungary
| | - József Furák
- Department of Surgery, Faculty of Medicine, University of Szeged, Szeged, Hungary
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2
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Korte B, Mathios D. Innovation in Non-Invasive Diagnosis and Disease Monitoring for Meningiomas. Int J Mol Sci 2024; 25:4195. [PMID: 38673779 PMCID: PMC11050588 DOI: 10.3390/ijms25084195] [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: 02/19/2024] [Revised: 03/26/2024] [Accepted: 04/07/2024] [Indexed: 04/28/2024] Open
Abstract
Meningiomas are tumors of the central nervous system that vary in their presentation, ranging from benign and slow-growing to highly aggressive. The standard method for diagnosing and classifying meningiomas involves invasive surgery and can fail to provide accurate prognostic information. Liquid biopsy methods, which exploit circulating tumor biomarkers such as DNA, extracellular vesicles, micro-RNA, proteins, and more, offer a non-invasive and dynamic approach for tumor classification, prognostication, and evaluating treatment response. Currently, a clinically approved liquid biopsy test for meningiomas does not exist. This review provides a discussion of current research and the challenges of implementing liquid biopsy techniques for advancing meningioma patient care.
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Affiliation(s)
- Brianna Korte
- Department of Neurosurgery, Washington University Medical Campus, St. Louis, MO 63110, USA
| | - Dimitrios Mathios
- Department of Neurosurgery, Washington University Medical Campus, St. Louis, MO 63110, USA
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3
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Bellantuono L, Tommasi R, Pantaleo E, Verri M, Amoroso N, Crucitti P, Di Gioacchino M, Longo F, Monaco A, Naciu AM, Palermo A, Taffon C, Tangaro S, Crescenzi A, Sodo A, Bellotti R. An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis. Sci Rep 2023; 13:16590. [PMID: 37789191 PMCID: PMC10547772 DOI: 10.1038/s41598-023-43856-7] [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: 06/30/2023] [Accepted: 09/29/2023] [Indexed: 10/05/2023] Open
Abstract
Raman spectroscopy shows great potential as a diagnostic tool for thyroid cancer due to its ability to detect biochemical changes during cancer development. This technique is particularly valuable because it is non-invasive and label/dye-free. Compared to molecular tests, Raman spectroscopy analyses can more effectively discriminate malignant features, thus reducing unnecessary surgeries. However, one major hurdle to using Raman spectroscopy as a diagnostic tool is the identification of significant patterns and peaks. In this study, we propose a Machine Learning procedure to discriminate healthy/benign versus malignant nodules that produces interpretable results. We collect Raman spectra obtained from histological samples, select a set of peaks with a data-driven and label independent approach and train the algorithms with the relative prominence of the peaks in the selected set. The performance of the considered models, quantified by area under the Receiver Operating Characteristic curve, exceeds 0.9. To enhance the interpretability of the results, we employ eXplainable Artificial Intelligence and compute the contribution of each feature to the prediction of each sample.
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Affiliation(s)
- Loredana Bellantuono
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
| | - Raffaele Tommasi
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Martina Verri
- Unit of Endocrine Organs and Neuromuscolar Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
- Dipartimento di Scienze, Università degli Studi Roma Tre, 00146, Roma, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Pierfilippo Crucitti
- Unit of Thoracic Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | | | - Filippo Longo
- Unit of Thoracic Surgery, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Anda Mihaela Naciu
- Unit of Metabolic Bone and Thyroid Diseases, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Andrea Palermo
- Unit of Metabolic Bone and Thyroid Diseases, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Chiara Taffon
- Unit of Endocrine Organs and Neuromuscolar Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Anna Crescenzi
- Unit of Endocrine Organs and Neuromuscolar Pathology, Fondazione Policlinico Universitario Campus Bio-Medico, 00128, Rome, Italy
| | - Armida Sodo
- Dipartimento di Scienze, Università degli Studi Roma Tre, 00146, Roma, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
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4
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Xu M, Chen Z, Zheng J, Zhao Q, Yuan Z. Artificial Intelligence-Aided Optical Imaging for Cancer Theranostics. Semin Cancer Biol 2023:S1044-579X(23)00094-9. [PMID: 37302519 DOI: 10.1016/j.semcancer.2023.06.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 06/08/2023] [Accepted: 06/08/2023] [Indexed: 06/13/2023]
Abstract
The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-making for individualized cancer medicine. In particular, optical imaging methods are able to visualize both the structural and functional information of tumors tissues with high contrast, low cost, and noninvasive property. However, no systematic work has been performed to inspect the recent advances on AI-aided optical imaging for cancer theranostics. In this review, we demonstrated how AI can guide optical imaging methods to improve the accuracy on tumor detection, automated analysis and prediction of its histopathological section, its monitoring during treatment, and its prognosis by using computer vision, deep learning and natural language processing. By contrast, the optical imaging techniques involved mainly consisted of various tomography and microscopy imaging methods such as optical endoscopy imaging, optical coherence tomography, photoacoustic imaging, diffuse optical tomography, optical microscopy imaging, Raman imaging, and fluorescent imaging. Meanwhile, existing problems, possible challenges and future prospects for AI-aided optical imaging protocol for cancer theranostics were also discussed. It is expected that the present work can open a new avenue for precision oncology by using AI and optical imaging tools.
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Affiliation(s)
- Mengze Xu
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai, China; Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Zhiyi Chen
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
| | - Junxiao Zheng
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Zhen Yuan
- Cancer Center, Faculty of Health Sciences, University of Macau, Macau SAR, China; Centre for Cognitive and Brain Sciences, University of Macau, Macau SAR, China.
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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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6
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Chen X, Wu X, Chen C, Luo C, Shi Y, Li Z, Lv X, Chen C, Su J, Wu L. Raman spectroscopy combined with a support vector machine algorithm as a diagnostic technique for primary Sjögren's syndrome. Sci Rep 2023; 13:5137. [PMID: 36991016 PMCID: PMC10060214 DOI: 10.1038/s41598-023-29943-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 02/13/2023] [Indexed: 03/31/2023] Open
Abstract
The aim of this study was to explore the feasibility of Raman spectroscopy combined with computer algorithms in the diagnosis of primary Sjögren syndrome (pSS). In this study, Raman spectra of 60 serum samples were acquired from 30 patients with pSS and 30 healthy controls (HCs). The means and standard deviations of the raw spectra of patients with pSS and HCs were calculated. Spectral features were assigned based on the literature. Principal component analysis (PCA) was used to extract the spectral features. Then, a particle swarm optimization (PSO)-support vector machine (SVM) was selected as the method of parameter optimization to rapidly classify patients with pSS and HCs. In this study, the SVM algorithm was used as the classification model, and the radial basis kernel function was selected as the kernel function. In addition, the PSO algorithm was used to establish a model for the parameter optimization method. The training set and test set were randomly divided at a ratio of 7:3. After PCA dimension reduction, the specificity, sensitivity and accuracy of the PSO-SVM model were obtained, and the results were 88.89%, 100% and 94.44%, respectively. This study showed that the combination of Raman spectroscopy and a support vector machine algorithm could be used as an effective pSS diagnosis method with broad application value.
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Affiliation(s)
- Xiaomei Chen
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Xue Wu
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Chen Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Cainan Luo
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Yamei Shi
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Zhengfang Li
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China
| | - Xiaoyi Lv
- College of Software, Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, Xinjiang, China
| | - Cheng Chen
- College of Software, Xinjiang University, Urumqi, Xinjiang, China
| | - Jinmei Su
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China.
- Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Lijun Wu
- Department of Rheumatology and Immunology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, China.
- Xinjiang Clinical Research Center for Rheumatoid Arthritis, Urumqi, Xinjiang, China.
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Zhang L, Zhou Y, Wu B, Zhang S, Zhu K, Liu CH, Yu X, Alfano RR. A Handheld Visible Resonance Raman Analyzer Used in Intraoperative Detection of Human Glioma. Cancers (Basel) 2023; 15:cancers15061752. [PMID: 36980638 PMCID: PMC10046110 DOI: 10.3390/cancers15061752] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/08/2023] [Accepted: 03/10/2023] [Indexed: 03/17/2023] Open
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.
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Affiliation(s)
- Liang Zhang
- Department of Neurosurgery, Medical School of Nankai University, Tianjin 300071, China
- Department of Neurosurgery, PLA General Hospital, Beijing 100853, China
| | - Yan Zhou
- Department of Neurosurgery, Air Force Medical Center, Beijing 100142, China
- Correspondence: (Y.Z.); (X.Y.)
| | - Binlin Wu
- Physics Department and CSCU Center for Nanotechnology, Southern Connecticut State University, New Haven, CT 06515, USA
| | | | - Ke Zhu
- Institute of Physics, Chinese Academy of Sciences (CAS), Beijing 100190, China
| | - Cheng-Hui Liu
- Institute for Ultrafast Spectroscopy and Lasers, Department of Physics, The City College of the City University of New York, New York, NY 10031, USA
| | - Xinguang Yu
- Department of Neurosurgery, Medical School of Nankai University, Tianjin 300071, China
- Department of Neurosurgery, PLA General Hospital, Beijing 100853, China
- Correspondence: (Y.Z.); (X.Y.)
| | - Robert R. Alfano
- Institute for Ultrafast Spectroscopy and Lasers, Department of Physics, The City College of the City University of New York, New York, NY 10031, USA
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8
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Quesnel A, Coles N, Angione C, Dey P, Polvikoski TM, Outeiro TF, Islam M, Khundakar AA, Filippou PS. Glycosylation spectral signatures for glioma grade discrimination using Raman spectroscopy. BMC Cancer 2023; 23:174. [PMID: 36809974 PMCID: PMC9942363 DOI: 10.1186/s12885-023-10588-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 01/12/2023] [Accepted: 01/27/2023] [Indexed: 02/23/2023] Open
Abstract
BACKGROUND Gliomas are the most common brain tumours with the high-grade glioblastoma representing the most aggressive and lethal form. Currently, there is a lack of specific glioma biomarkers that would aid tumour subtyping and minimally invasive early diagnosis. Aberrant glycosylation is an important post-translational modification in cancer and is implicated in glioma progression. Raman spectroscopy (RS), a vibrational spectroscopic label-free technique, has already shown promise in cancer diagnostics. METHODS RS was combined with machine learning to discriminate glioma grades. Raman spectral signatures of glycosylation patterns were used in serum samples and fixed tissue biopsy samples, as well as in single cells and spheroids. RESULTS Glioma grades in fixed tissue patient samples and serum were discriminated with high accuracy. Discrimination between higher malignant glioma grades (III and IV) was achieved with high accuracy in tissue, serum, and cellular models using single cells and spheroids. Biomolecular changes were assigned to alterations in glycosylation corroborated by analysing glycan standards and other changes such as carotenoid antioxidant content. CONCLUSION RS combined with machine learning could pave the way for more objective and less invasive grading of glioma patients, serving as a useful tool to facilitate glioma diagnosis and delineate biomolecular glioma progression changes.
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Affiliation(s)
- Agathe Quesnel
- School of Health & Life Sciences, Teesside University, TS1 3BX, Middlesbrough, UK
- National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK
| | - Nathan Coles
- School of Health & Life Sciences, Teesside University, TS1 3BX, Middlesbrough, UK
- National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK
| | - Claudio Angione
- National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK
- School of Computing, Engineering & Digital Technologies, Teesside University, Darlington, UK
- Centre for Digital Innovation, Teesside University, Darlington, UK
| | - Priyanka Dey
- School of Health & Life Sciences, Teesside University, TS1 3BX, Middlesbrough, UK
- National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK
- School of Pharmacy and Biomedical Sciences, University of Portsmouth, PO1 2UP, Portsmouth, UK
| | - Tuomo M Polvikoski
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Tiago F Outeiro
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
- Department of Experimental Neurodegeneration, Center for Biostructural Imaging of Neurodegeneration, University Medical Center, Göttingen, Germany
- Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Göttingen, Germany
| | - Meez Islam
- School of Health & Life Sciences, Teesside University, TS1 3BX, Middlesbrough, UK
- National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK
| | - Ahmad A Khundakar
- School of Health & Life Sciences, Teesside University, TS1 3BX, Middlesbrough, UK
- National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Panagiota S Filippou
- School of Health & Life Sciences, Teesside University, TS1 3BX, Middlesbrough, UK.
- National Horizons Centre, Teesside University, 38 John Dixon Ln, DL1 1HG, Darlington, UK.
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9
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Cao L, Zheng X, Han P, Ren L, Hu F, Li Z. Raman spectroscopy as a promising diagnostic method for rheumatoid arthritis. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:709-718. [PMID: 36598183 DOI: 10.1039/d2ay01904c] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Background: Diagnosis of rheumatoid arthritis (RA) basically relies on clinical symptoms and autoantibodies, especially anti-citrullinated protein antibodies (ACPAs) and rheumatoid factor (RF). However, the lack of autoantibodies is still a dilemma clinically in seronegative RA, especially in the early stage of the disease. This study aimed to provide a unique disease fingerprint with high diagnostic value to discriminate RA based on Raman spectroscopy. Methods: Raman spectroscopy provides a repertoire of biomolecules in serum from RA. Multivariate dimension-reducing methods and machine-learning algorithms were exploited to reveal the intrinsic differences and the potential discrimination power. The underlying differential biomolecules were retrieved by the assignment of Raman peaks. Moreover, the correlations between the spectral differences and RA patient's clinical and immunological manifestations were also analyzed. Results: RA patients exhibited unique Raman spectra characterized by biomolecular alterations during the disease progression. The discrimination power yielded 97.3% sensitivity and 94.8% specificity for RA diagnosis. In the recognition of ACPA-negative RA, the sensitivity and specificity also reached 95.6% and 92.8%, respectively. In particular, the differential Raman spectrum peaks of RA patients mainly represented lipids, amino acids, glycogen, and fatty acids. Further analysis showed that the different serum Raman spectra correlated with the clinical features of RA, including disease duration, RF, anticyclic citrullinated peptide antibodies (anti-CCPs), IgA, IgM, IgG, tender joint count, and swollen joint count (|rs| = 0.15-0.52, p < 0.05). Conclusions: Raman spectroscopy was revealed to be a promising diagnostic method for RA, especially for ACPA-negative patients.
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Affiliation(s)
- Lulu Cao
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
| | - Xi Zheng
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Peng Han
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
| | - Limin Ren
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
| | - Fanlei Hu
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, China
- Department of Integration of Chinese and Western Medicine, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Zhanguo Li
- Department of Rheumatology and Immunology, Peking University People's Hospital and Beijing Key Laboratory for Rheumatism Mechanism and Immune Diagnosis (BZ0135), Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, China
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10
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Zhao B, Zhai H, Shao H, Bi K, Zhu L. Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 229:107295. [PMID: 36706562 PMCID: PMC9711896 DOI: 10.1016/j.cmpb.2022.107295] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/10/2022] [Accepted: 11/29/2022] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics. METHODS Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset. RESULTS For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients. CONCLUSIONS Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.
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Affiliation(s)
- Bingqiang Zhao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Honglin Zhai
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China.
| | - Haiping Shao
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Kexin Bi
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
| | - Ling Zhu
- College of Chemistry & Chemical Engineering, Lanzhou University; South Tianshui Road 222, Lanzhou, Gansu 730000, PR China
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11
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Banerjee A, Halder A, Jadhav P, Sarkar A, Hole A, Shastri JS, Agrawal S, Chilakapati MK, Srivastava S. SARS-CoV-2 severity classification from plasma sample using confocal Raman spectroscopy. JOURNAL OF RAMAN SPECTROSCOPY : JRS 2023; 54:124-132. [PMID: 36713977 PMCID: PMC9874663 DOI: 10.1002/jrs.6461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 06/18/2023]
Abstract
The world is on the brink of facing coronavirus's (COVID-19) fourth wave as the mutant forms of viruses are escaping neutralizing antibodies in spite of being vaccinated. As we have already witnessed that it has encumbered our health system, with hospitals swamped with infected patients observed during the viral outbreak. Rapid triage of patients infected with SARS-CoV-2 is required during hospitalization to prioritize and provide the best point of care. Traditional diagnostics techniques such as RT-PCR and clinical parameters such as symptoms, comorbidities, sex and age are not enough to identify the severity of patients. Here, we investigated the potential of confocal Raman microspectroscopy as a powerful tool to generate an expeditious blood-based test for the classification of COVID-19 disease severity using 65 patients plasma samples from cohorts infected with SARS-CoV-2. We designed an easy manageable blood test where we used a small volume (8 μl) of inactivated whole plasma samples from infected patients without any extra solvent usage in plasma processing. Raman spectra of plasma samples were acquired and multivariate exploratory analysis PC-LDA (principal component based linear discriminant analysis) was used to build a model, which segregated the severe from the non-severe COVID-19 group with a sensitivity of 83.87%, specificity of 70.60% and classification efficiency of 76.92%. Among the bands expressed in both the cohorts, the study led to the identification of Raman fingerprint regions corresponding to lipids (1661, 1742), proteins amide I and amide III (1555, 1247), proteins (Phe) (1006, 1034), and nucleic acids (760) to be differentially expressed in severe COVID-19 patient's samples. In summary, the current study exhibits the potential of confocal Raman to generate simple, rapid, and less expensive blood tests to triage the severity of patients infected with SARS-CoV-2.
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Affiliation(s)
- Arghya Banerjee
- Department of Biosciences and BioengineeringIndian Institute of Technology BombayMumbaiIndia
| | - Ankit Halder
- Department of Biosciences and BioengineeringIndian Institute of Technology BombayMumbaiIndia
| | - Priyanka Jadhav
- Advanced Centre for Treatment Research and Education in Cancer (ACTREC)Tata Memorial Centre (TMC)Navi MumbaiIndia
- Homi Bhabha National InstituteTraining School Complex, Anushakti NagarMumbaiIndia
| | - Anushka Sarkar
- Department of Life SciencesPresidency University (Main Campus)KolkataIndia
| | - Arti Hole
- Advanced Centre for Treatment Research and Education in Cancer (ACTREC)Tata Memorial Centre (TMC)Navi MumbaiIndia
| | | | | | - Murali Krishna Chilakapati
- Advanced Centre for Treatment Research and Education in Cancer (ACTREC)Tata Memorial Centre (TMC)Navi MumbaiIndia
| | - Sanjeeva Srivastava
- Department of Biosciences and BioengineeringIndian Institute of Technology BombayMumbaiIndia
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12
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Ranasinghe JC, Wang Z, Huang S. Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications. BIOSENSORS 2022; 13:27. [PMID: 36671862 PMCID: PMC9855372 DOI: 10.3390/bios13010027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.
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13
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Rapid label-free detection of cholangiocarcinoma from human serum using Raman spectroscopy. PLoS One 2022; 17:e0275362. [PMID: 36227878 PMCID: PMC9562168 DOI: 10.1371/journal.pone.0275362] [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: 02/05/2022] [Accepted: 09/15/2022] [Indexed: 11/25/2022] Open
Abstract
Cholangiocarcinoma (CCA) is highly prevalent in the northeastern region of Thailand. Current diagnostic methods for CCA are often expensive, time-consuming, and require medical professionals. Thus, there is a need for a simple and low-cost CCA screening method. This work developed a rapid label-free technique by Raman spectroscopy combined with the multivariate statistical methods of principal component analysis and linear discriminant analysis (PCA-LDA), aiming to analyze and classify between CCA (n = 30) and healthy (n = 30) serum specimens. The model's classification performance was validated using k-fold cross validation (k = 5). Serum levels of cholesterol (548, 700 cm-1), tryptophan (878 cm-1), and amide III (1248,1265 cm-1) were found to be statistically significantly higher in the CCA patients, whereas serum beta-carotene (1158, 1524 cm-1) levels were significantly lower. The peak heights of these identified Raman marker bands were input into an LDA model, achieving a cross-validated diagnostic sensitivity and specificity of 71.33% and 90.00% in distinguishing the CCA from healthy specimens. The PCA-LDA technique provided a higher cross-validated sensitivity and specificity of 86.67% and 96.67%. To conclude, this work demonstrated the feasibility of using Raman spectroscopy combined with PCA-LDA as a helpful tool for cholangiocarcinoma serum-based screening.
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14
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Banerjee A, Halder A, Jadhav P, Bankar R, Pattarkine J, Hole A, Shah A, Goel A, Murali Krishna C, Srivastava S. Metabolomics Profiling of Pituitary Adenomas by Raman Spectroscopy, Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy, and Mass Spectrometry of Serum Samples. Anal Chem 2022; 94:11898-11907. [PMID: 35980087 DOI: 10.1021/acs.analchem.2c02487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
To date, no studies are available in which pituitary adenomas (PAs) have been studied using techniques like confocal Raman spectroscopy, attenuated total reflection-Fourier transform infrared (FT-IR), and liquid chromatography-tandem mass spectrometry (LC-MS/MS) in the same serum samples. To understand the metabolomics fingerprint, Raman spectra of 16 acromegaly, 19 Cushing's, and 33 nonfunctional PA (NFPA) and ATR-FTIR spectral acquisition of 16 acromegaly, 18 Cushing's, and 22 NFPA patient's serum samples were acquired. Next, Principal component-based linear discriminant analysis (PC-LDA) models were developed, Raman spectral analysis classified acromegaly with an accuracy of 79.17%, sensitivity of 75%, and specificity of 81.25%, Cushing's with an accuracy of 66.67%, sensitivity of 100%, and specificity of 52.63%, and NFPA with an accuracy of 73.17%, sensitivity of 75%, and specificity of 72.73%. ATR-FTIR spectral analysis classified acromegaly with an accuracy of 95.83%, sensitivity of 100%, and specificity of 93.75%, Cushing's with an accuracy of 65.38%, sensitivity of 87.5%, and specificity of 55.56%, and NFPA with an accuracy of 70%, sensitivity of 87.5%, and specificity of 43.75%. In either of the cases, healthy individual cohorts were clearly segregated from the disease cohort, which identified differential regulated regions of nucleic acids, lipids, amides, phosphates, and polysaccharide/C-C residue α helix regions. Furthermore, LC-MS/MS-based analysis of sera samples resulted in the identification of various sphingosine, lipids, acylcarnitines, amino acids, ethanolamine, choline, and their derivatives that differentially regulated in each tumor cohort. We believe cues obtained from the study may be used to generate the metabolite-based test to diagnose PAs from serum in addition to conventional techniques and also to understand disease biology for better disease management, point of care, and improving quality of life in PA patients.
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Affiliation(s)
- Arghya Banerjee
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Ankit Halder
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Priyanka Jadhav
- Advanced Centre for Treatment Research and Education in Cancer (ACTREC), Tata Memorial Centre (TMC). Sector-22, Kharghar, Navi Mumbai 410210, India
| | - Renuka Bankar
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Janhavi Pattarkine
- Department of Biotechnology, Dr. D.Y. Patil Arts, Commerce and Science College, Pimpri, Pune 411018, India
| | - Arti Hole
- Advanced Centre for Treatment Research and Education in Cancer (ACTREC), Tata Memorial Centre (TMC). Sector-22, Kharghar, Navi Mumbai 410210, India
| | - Abhidha Shah
- Department of Neurosurgery, King Edward Memorial Hospital and Seth G. S. Medical College, Dr E Borges Road, Acharya Donde Marg, Parel, Opposite Tata & Wadia Hospital, Mumbai 400012, India
| | - Atul Goel
- Department of Neurosurgery, King Edward Memorial Hospital and Seth G. S. Medical College, Dr E Borges Road, Acharya Donde Marg, Parel, Opposite Tata & Wadia Hospital, Mumbai 400012, India
| | - C Murali Krishna
- Advanced Centre for Treatment Research and Education in Cancer (ACTREC), Tata Memorial Centre (TMC). Sector-22, Kharghar, Navi Mumbai 410210, India
| | - Sanjeeva Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
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15
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Blake N, Gaifulina R, Griffin LD, Bell IM, Thomas GMH. Machine Learning of Raman Spectroscopy Data for Classifying Cancers: A Review of the Recent Literature. Diagnostics (Basel) 2022; 12:diagnostics12061491. [PMID: 35741300 PMCID: PMC9222091 DOI: 10.3390/diagnostics12061491] [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: 04/29/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/16/2022] Open
Abstract
Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models.
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Affiliation(s)
- Nathan Blake
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK; (N.B.); (R.G.)
| | - Riana Gaifulina
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK; (N.B.); (R.G.)
| | - Lewis D. Griffin
- Department of Computer Science, University College London, London WC1E 6BT, UK;
| | - Ian M. Bell
- Spectroscopy Products Division, Renishaw plc, Wotton-under-Edge GL12 8JR, UK;
| | - Geraint M. H. Thomas
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK; (N.B.); (R.G.)
- Correspondence: ; Tel.: +44-20-3549-5456
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16
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Lilo T, Morais CLM, Ashton KM, Davis C, Dawson TP, Martin FL, Alder J, Roberts G, Ray A, Gurusinghe N. Raman hyperspectral imaging coupled to three-dimensional discriminant analysis: Classification of meningiomas brain tumour grades. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2022; 273:121018. [PMID: 35189493 DOI: 10.1016/j.saa.2022.121018] [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: 10/17/2021] [Revised: 02/04/2022] [Accepted: 02/06/2022] [Indexed: 06/14/2023]
Abstract
Meningiomas remains a clinical dilemma. They are the commonest "benign" types of brain tumours and, although being typically benign, they are divided into three WHO grades categories (I, II and III) which are associated with the tumour growth rate and likelihood of recurrence. Recurrence depends on extend of surgery as well as histopathological diagnosis. There is a marked variation amongst surgeons in the follow-up arrangements for their patients even within the same unit which has a significant clinical, and financial implication. Knowing the tumour grade rapidly is an important factor to predict surgical outcomes and adequate patient treatment. Clinical follow up sometimes is haphazard and not based on clear evidence. Spectrochemical techniques are a powerful tool for cancer diagnostics. Raman hyperspectral imaging is able to generate spatially-distributed spectrochemical signatures with great sensitivity. Using this technique, 95 brain tissue samples (66 meningiomas WHO grade I, 24 meningiomas WHO grade II and 5 meningiomas that reoccurred) were analysed in order to discriminate grade I and grade II samples. Newly-developed three-dimensional discriminant analysis algorithms were used to process the hyperspectral imaging data in a 3D fashion. Three-dimensional principal component analysis quadratic discriminant analysis (3D-PCA-QDA) was able to distinguish grade I and grade II meningioma samples with 96% test accuracy (100% sensitivity and 95% specificity). This technique is here shown to be a high-throughput, reagent-free, non-destructive, and can give accurate predictive information regarding the meningioma tumour grade, hence, having enormous clinical potential with regards to being developed for intra-operative real-time assessment of disease.
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Affiliation(s)
- Taha Lilo
- Department of Neurosurgery, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, UK; School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK.
| | - Camilo L M Morais
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK
| | - Katherine M Ashton
- Department of Neuropathology, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, UK
| | - Charles Davis
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK
| | - Timothy P Dawson
- Department of Neuropathology, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, UK
| | | | - Jane Alder
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston PR1 2HE, UK
| | - Gareth Roberts
- Department of Neurosurgery, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, UK
| | - Arup Ray
- Department of Neurosurgery, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, UK
| | - Nihal Gurusinghe
- Department of Neurosurgery, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, UK
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17
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Lilo T, Morais CL, Shenton C, Ray A, Gurusinghe N. Revising Fourier-transform infrared (FT-IR) and Raman spectroscopy towards brain cancer detection. Photodiagnosis Photodyn Ther 2022; 38:102785. [DOI: 10.1016/j.pdpdt.2022.102785] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 02/15/2022] [Accepted: 02/25/2022] [Indexed: 12/11/2022]
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18
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Das D, Banerjee A, Jena AB, Duttaroy AK, Pathak S. Essentiality, relevance, and efficacy of adjuvant/combinational therapy in the management of thyroid dysfunctions. Biomed Pharmacother 2022; 146:112613. [PMID: 35062076 DOI: 10.1016/j.biopha.2022.112613] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/14/2021] [Accepted: 01/02/2022] [Indexed: 11/02/2022] Open
Abstract
Thyroid dysfunction is the most prevalent endocrine disorder worldwide having an epidemiology of 11% in Indians, 4.6% in the United Kingdom, and 2% in the United States of America among the overall population. The common thyroid disorders include hypothyroidism, hyperthyroidism, Hashimoto's thyroiditis, and thyroid cancer. This review briefly elaborates the molecular regulation and mechanism of thyroid hormone, and its associated thyroid disorders. The thyroid hormones regulate critical biochemical functions in brain development and function. Hypothyroidism is mainly associated with dysregulation of cytokines, increased ROS production, and altered signal transduction in major regions of the brain. In addition, it is associated with reduced antioxidant capacity and increased oxidative stress in humans. Though 70% of thyroid disorders are caused by heredity, environmental factors have a significant influence in developing autoimmune thyroid disorders in people who are predisposed to them. This drives us to understand the relationship between environmental factors and thyroid dysregulated disorders. The treatment option for the thyroid disorder includes antithyroid medications, receiving radioactive iodine therapy, or surgery at a critical stage. However, antithyroid drugs are not typically used long-term in thyroid disease due to the high recurrence rate. Adjuvant treatment of antioxidants can produce better outcomes with anti-thyroid drug treatment. Thus, Adjuvant therapy has been proven as an effective strategy for managing thyroid dysfunction, herbal remedies can be used to treat thyroid dysfunction in the future, which in turn can reduce the prevalence of thyroid disorders.
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Affiliation(s)
- Diptimayee Das
- Faculty of Allied Health Sciences, Chettinad Academy of Research and Education (CARE), Chettinad Hospital and Research Institute (CHRI), Chennai 603103, India
| | - Antara Banerjee
- Department of Medical Biotechnology, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education (CARE), Chettinad Hospital and Research Institute (CHRI), Chennai, India
| | | | - Asim K Duttaroy
- Department of Nutrition, Institute of Medical Sciences, Faculty of Medicine, University of Oslo, Norway.
| | - Surajit Pathak
- Department of Medical Biotechnology, Faculty of Allied Health Sciences, Chettinad Academy of Research and Education (CARE), Chettinad Hospital and Research Institute (CHRI), Chennai, India.
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19
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Intraoperative discrimination of native meningioma and dura mater by Raman spectroscopy. Sci Rep 2021; 11:23583. [PMID: 34880346 PMCID: PMC8654829 DOI: 10.1038/s41598-021-02977-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 11/25/2021] [Indexed: 01/10/2023] Open
Abstract
Meningiomas are among the most frequent tumors of the central nervous system. For a total resection, shown to decrease recurrences, it is paramount to reliably discriminate tumor tissue from normal dura mater intraoperatively. Raman spectroscopy (RS) is a non-destructive, label-free method for vibrational analysis of biochemical molecules. On the microscopic level, RS was already used to differentiate meningioma from dura mater. In this study we test its suitability for intraoperative macroscopic meningioma diagnostics. RS is applied to surgical specimen of intracranial meningiomas. The main purpose is the differentiation of tumor from normal dura mater, in order to potentially accelerate the diagnostic workflow. The collected meningioma and dura mater samples (n = 223 tissue samples from a total of 59 patients) are analyzed under untreated conditions using a new partially robotized RS acquisition system. Spectra (n = 1273) are combined with the according histopathological analysis for each sample. Based on this, a classifier is trained via machine learning. Our trained classifier separates meningioma and dura mater with a sensitivity of 96.06 \documentclass[12pt]{minimal}
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\begin{document}$$\pm $$\end{document}± 0.02% for internal fivefold cross validation and 100% and 93.97% if validated with an external test set. RS is an efficient method to discriminate meningioma from healthy dura mater in fresh tissue samples without additional processing or histopathological imaging. It is a quick and reliable complementary diagnostic tool to the neuropathological workflow and has potential for guided surgery. RS offers a safe way to examine unfixed surgical specimens in a perioperative setting.
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20
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Li X, Chen H, Zhang S, Yang H, Gao S, Xu H, Wang L, Xu R, Zhou F, Hu J, Zhao J, Zeng H. Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection. JOURNAL OF BIOPHOTONICS 2021; 14:e202100010. [PMID: 34092038 DOI: 10.1002/jbio.202100010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 04/28/2021] [Accepted: 06/02/2021] [Indexed: 06/12/2023]
Abstract
We herein report a novel, reliable and inexpensive method for detecting esophageal cancer using blood plasma resonance Raman spectroscopy combined with multivariate analysis methods. The blood plasma samples were divided into late stage cancer group (n = 164), early stage cancer group (n = 35) and normal group (n = 135) based on clinical pathological diagnosis. Using a specially designed quartz capillary tube as sample holder, we obtained higher quality resonance Raman spectra of blood plasma than existing method. The study demonstrated that the carotenoids levels in blood plasma were reduced in esophageal cancer patients. The area under the receiver operating characteristic curve (and 95% confidence interval) calculated by wavenumber selection and principal component analysis combined with linear discriminant analysis (PC-LDA) algorithm were 0.894 (0.858-0.929), 0.901 (0.841-0.960) and 0.871 (0.799-0.942) for differentiating late cancer from normal, late cancer from early cancer, and early cancer from normal respectively. The contribution from the two carotenoids wavenumber regions of 1155 and 1515 cm-1 were more than 84.2%. The results show that the plasma carotenoids could be a potential biomarker for screening esophageal cancer using resonance Raman spectroscopy combined with wavenumber selection and PC-LDA algorithms.
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Affiliation(s)
- Xianchang Li
- Anyang Tumor Hospital, The 4th Affiliated Hospital of Henan University of Science and Technology, Anyang, China
- State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, School of Chemical and Environmental Engineering, Anyang Institute of Technology, Anyang, China
| | - Hongjun Chen
- Anyang Tumor Hospital, The 4th Affiliated Hospital of Henan University of Science and Technology, Anyang, China
| | - Shiding Zhang
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, School of Chemical and Environmental Engineering, Anyang Institute of Technology, Anyang, China
| | - Haijun Yang
- Anyang Tumor Hospital, The 4th Affiliated Hospital of Henan University of Science and Technology, Anyang, China
| | - Shanshan Gao
- Henan Joint International Research Laboratory of Nanocomposite Sensing Materials, School of Chemical and Environmental Engineering, Anyang Institute of Technology, Anyang, China
| | - Haisheng Xu
- Anyang Tumor Hospital, The 4th Affiliated Hospital of Henan University of Science and Technology, Anyang, China
| | - Lidong Wang
- State Key Laboratory of Esophageal Cancer Prevention and Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ruiping Xu
- Anyang Tumor Hospital, The 4th Affiliated Hospital of Henan University of Science and Technology, Anyang, China
| | - Fuyou Zhou
- Anyang Tumor Hospital, The 4th Affiliated Hospital of Henan University of Science and Technology, Anyang, China
| | - Jiming Hu
- Key Laboratory of Analytical Chemistry for Biology and Medicine (Ministry of Education), College of Chemistry and Molecular Sciences, Wuhan University, Wuhan, China
| | - Jianhua Zhao
- Department of Dermatology and Skin Science, University of British Columbia, Vancouver, British Columbia, Canada
- Imaging Unit - Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, British Columbia, Canada
| | - Haishan Zeng
- Department of Dermatology and Skin Science, University of British Columbia, Vancouver, British Columbia, Canada
- Imaging Unit - Department of Integrative Oncology, BC Cancer Research Centre, Vancouver, British Columbia, Canada
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21
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Identifying functioning and nonfunctioning adrenal tumors based on blood serum surface-enhanced Raman spectroscopy. Anal Bioanal Chem 2021; 413:4289-4299. [PMID: 33963880 DOI: 10.1007/s00216-021-03381-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/06/2021] [Accepted: 04/28/2021] [Indexed: 12/26/2022]
Abstract
Adrenal tumors are common tumors in urology and they can be further divided into functioning and nonfunctioning tumors according to whether there is uncommon endocrine function. In clinical practice, the early identification and accurate assessment of adrenal tumors are essential for the guidance of subsequent treatment. However, a nonfunctioning adrenal tumor often lacks obvious clinical symptoms, making it difficult to be timely and precisely diagnosed by conventional examinations. Therefore, a rapid and accurate method for identifying the functioning and nonfunctioning adrenal tumors is urgently required to achieve precise treatment of adrenal tumors. In this study, surface-enhanced Raman spectroscopy was investigated as a diagnostic tool to identify the blood serum samples from healthy volunteers as well as the patients with functioning and nonfunctioning adrenal tumors. Based on the SERS peak analysis, abnormal glycolysis, DNA/RNA, and amino acid metabolites were found to be potential biomarkers for identifying patients with adrenal tumors, while metabolites related to disordered protein catabolism and excessive hormone secretion were expected to further differentiate functioning adrenal tumors from nonfunctioning adrenal tumors. In addition, principal component analysis followed by support vector machine (PCA-SVM) was further applied on those serum SERS measurements, and the classification accuracies of 96.8% and 84.5% were achieved for differentiating healthy group versus adrenal tumor group and functioning adrenal tumor group versus nonfunctioning adrenal tumor group, respectively. The results have demonstrated the prodigious potential of precise adrenal tumor diagnosis by using the blood serum surface-enhanced Raman spectroscopy technique.
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22
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Yin G, Li L, Lu S, Yin Y, Su Y, Zeng Y, Luo M, Ma M, Zhou H, Orlandini L, Yao D, Liu G, Lang J. An efficient primary screening of COVID-19 by serum Raman spectroscopy. JOURNAL OF RAMAN SPECTROSCOPY : JRS 2021; 52:949-958. [PMID: 33821082 PMCID: PMC8014023 DOI: 10.1002/jrs.6080] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 01/17/2021] [Accepted: 02/10/2021] [Indexed: 05/02/2023]
Abstract
The outbreak of COVID-19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID-19 detection is the real-time polymerase chain reaction (RT-PCR)-based technique; however, it also has certain limitations, such as sample-dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID-19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID-19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support-vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID-19 patients and 5 symptomatic COVID-19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups-confirmed COVID-19, suspected, and healthy individuals-the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID-19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85-0.88), and the accuracy between the COVID-19 and the healthy controls is 0.90 (95% CI: 0.89-0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67-0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum-level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID-19 screening.
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Affiliation(s)
- Gang Yin
- Department of Radiation OncologySichuan Cancer Hospital & InstituteChengduChina
- Physical Engineering LaboratoryRadiation Oncology Key Laboratory of Sichuan ProvinceChengduChina
| | - Lintao Li
- Department of Radiation OncologySichuan Cancer Hospital & InstituteChengduChina
- Physical Engineering LaboratoryRadiation Oncology Key Laboratory of Sichuan ProvinceChengduChina
| | - Shun Lu
- Department of Radiation OncologySichuan Cancer Hospital & InstituteChengduChina
- Physical Engineering LaboratoryRadiation Oncology Key Laboratory of Sichuan ProvinceChengduChina
| | - Yu Yin
- Sichuan Institute for Brain Science and Brain‐Inspired Intelligence, MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Yuanzhang Su
- School of Foreign LanguagesUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Yilan Zeng
- Clinical LaboratoryThe Public Health Clinical Center of ChengduChengduChina
| | - Mei Luo
- Clinical LaboratoryThe Public Health Clinical Center of ChengduChengduChina
| | - Maohua Ma
- Clinical LaboratoryThe Public Health Clinical Center of ChengduChengduChina
| | - Hongyan Zhou
- Department of Radiation OncologySichuan Cancer Hospital & InstituteChengduChina
- Physical Engineering LaboratoryRadiation Oncology Key Laboratory of Sichuan ProvinceChengduChina
| | - Lucia Orlandini
- Department of Radiation OncologySichuan Cancer Hospital & InstituteChengduChina
- Physical Engineering LaboratoryRadiation Oncology Key Laboratory of Sichuan ProvinceChengduChina
| | - Dezhong Yao
- Sichuan Institute for Brain Science and Brain‐Inspired Intelligence, MOE Key Lab for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Gang Liu
- Department of Clinical LaboratoryThe First Affiliated Hospital of Chengdu Medical CollegeChengduChina
| | - Jinyi Lang
- Department of Radiation OncologySichuan Cancer Hospital & InstituteChengduChina
- Physical Engineering LaboratoryRadiation Oncology Key Laboratory of Sichuan ProvinceChengduChina
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Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors. Cancers (Basel) 2021; 13:cancers13061407. [PMID: 33808766 PMCID: PMC8003579 DOI: 10.3390/cancers13061407] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
Abstract
Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis-Support Vector Machine (PCA-SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9-92.5% CA, 80-95% sensitivity and 80-90% specificity. AUC scores in the range of 0.82-0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.
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24
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Use of Raman spectroscopy to evaluate the biochemical composition of normal and tumoral human brain tissues for diagnosis. Lasers Med Sci 2020; 37:121-133. [PMID: 33159308 DOI: 10.1007/s10103-020-03173-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 10/23/2020] [Indexed: 10/23/2022]
Abstract
Raman spectroscopy was used to identify biochemical differences in normal brain tissue (cerebellum and meninges) compared to tumors (glioblastoma, medulloblastoma, schwannoma, and meningioma) through biochemical information obtained from the samples. A total of 263 spectra were obtained from fragments of the normal cerebellum (65), normal meninges (69), glioblastoma (28), schwannoma (8), medulloblastoma (19), and meningioma (74), which were collected using the dispersive Raman spectrometer (830 nm, near infrared, output power of 350 mW, 20 s exposure time to obtain the spectra), coupled to a Raman probe. A spectral model based on least squares fitting was developed to estimate the biochemical concentration of 16 biochemical compounds present in brain tissue, among those that most characterized brain tissue spectra, such as linolenic acid, triolein, cholesterol, sphingomyelin, phosphatidylcholine, β-carotene, collagen, phenylalanine, DNA, glucose, and blood. From the biochemical information, the classification of the spectra in the normal and tumor groups was conducted according to the type of brain tumor and corresponding normal tissue. The classification used in discrimination models were (a) the concentrations of the biochemical constituents of the brain, through linear discriminant analysis (LDA), and (b) the tissue spectra, through the discrimination by partial least squares (PLS-DA) regression. The models obtained 93.3% discrimination accuracy through the LDA between the normal and tumor groups of the cerebellum separated according to the concentration of biochemical constituents and 94.1% in the discrimination by PLS-DA using the whole spectrum. The results obtained demonstrated that the Raman technique is a promising tool to differentiate concentrations of biochemical compounds present in brain tissues, both normal and tumor. The concentrations estimated by the biochemical model and all the information contained in the Raman spectra were both able to classify the pathological groups.
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25
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Chen C, Yang L, Li H, Chen F, Chen C, Gao R, Lv XY, Tang J. Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure. Photodiagnosis Photodyn Ther 2020; 30:101792. [DOI: 10.1016/j.pdpdt.2020.101792] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 04/18/2020] [Accepted: 04/20/2020] [Indexed: 10/24/2022]
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26
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Chen C, Du G, Tong D, Lv G, Lv X, Si R, Tang J, Li H, Ma H, Mo J. Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction. JOURNAL OF BIOPHOTONICS 2020; 13:e201900099. [PMID: 31593625 DOI: 10.1002/jbio.201900099] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Revised: 09/19/2019] [Accepted: 09/22/2019] [Indexed: 06/10/2023]
Abstract
The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high-dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA-SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm-1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.
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Affiliation(s)
- Cheng Chen
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Guoli Du
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Dongni Tong
- The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Guodong Lv
- State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China
| | - Xiaoyi Lv
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Rumeng Si
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
| | - Jun Tang
- Physics and Chemistry Detecting Center, Xinjiang University, Urumqi, China
| | - Hongyi Li
- Quality of Products Supervision and Inspection Institute, Urumqi, China
| | - Hongbing Ma
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Jiaqing Mo
- College of Information Science and Engineering, Xinjiang University, Urumqi, China
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27
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Parachalil DR, McIntyre J, Byrne HJ. Potential of Raman spectroscopy for the analysis of plasma/serum in the liquid state: recent advances. Anal Bioanal Chem 2020; 412:1993-2007. [DOI: 10.1007/s00216-019-02349-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 10/17/2019] [Accepted: 12/11/2019] [Indexed: 12/18/2022]
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28
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Ralbovsky NM, Lednev IK. Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning. Chem Soc Rev 2020; 49:7428-7453. [DOI: 10.1039/d0cs01019g] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
This review summarizes recent progress made using Raman spectroscopy and machine learning for potential universal medical diagnostic applications.
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Affiliation(s)
| | - Igor K. Lednev
- Department of Chemistry
- University at Albany
- SUNY
- Albany
- USA
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29
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Lilo T, Morais CLM, Ashton KM, Pardilho A, Davis C, Dawson TP, Gurusinghe N, Martin FL. Spectrochemical differentiation of meningioma tumours based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Anal Bioanal Chem 2019; 412:1077-1086. [PMID: 31865413 PMCID: PMC7007428 DOI: 10.1007/s00216-019-02332-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 11/11/2019] [Accepted: 12/05/2019] [Indexed: 12/11/2022]
Abstract
Meningiomas are the commonest types of tumours in the central nervous system (CNS). It is a benign type of tumour divided into three WHO grades (I, II and III) associated with tumour growth rate and likelihood of recurrence, where surgical outcomes and patient treatments are dependent on the meningioma grade and histological subtype. The development of alternative approaches based on attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy could aid meningioma grade determination and its biospectrochemical profiling in an automated fashion. Herein, ATR-FTIR in combination with chemometric techniques is employed to distinguish grade I, grade II and grade I meningiomas that re-occurred. Ninety-nine patients were investigated in this study where their formalin-fixed paraffin-embedded (FFPE) brain tissue samples were analysed by ATR-FTIR spectroscopy. Subsequent classification was performed via principal component analysis plus linear discriminant analysis (PCA-LDA) and partial least squares plus discriminant analysis (PLS-DA). PLS-DA gave the best results where grade I and grade II meningiomas were discriminated with 79% accuracy, 80% sensitivity and 73% specificity, while grade I versus grade I recurrence and grade II versus grade I recurrence were discriminated with 94% accuracy (94% sensitivity and specificity) and 97% accuracy (97% sensitivity and 100% specificity), respectively. Several wavenumbers were identified as possible biomarkers towards tumour differentiation. The majority of these were associated with lipids, protein, DNA/RNA and carbohydrate alterations. These findings demonstrate the potential of ATR-FTIR spectroscopy towards meningioma grade discrimination as a fast, low-cost, non-destructive and sensitive tool for clinical settings. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy was used to discriminate meningioma WHO grade I, grade II and grade I recurrence tumours. ![]()
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Affiliation(s)
- Taha Lilo
- Department of Neurosurgery, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston, PR2 9HT, UK.,School of Pharmacy and Biomedical Sciences, UCLan, Preston, PR1 2HE, UK
| | - Camilo L M Morais
- School of Pharmacy and Biomedical Sciences, UCLan, Preston, PR1 2HE, UK
| | - Katherine M Ashton
- Department of Neuropathology, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston, PR2 9HT, UK
| | - Ana Pardilho
- Department of Neurosurgery, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston, PR2 9HT, UK
| | - Charles Davis
- School of Pharmacy and Biomedical Sciences, UCLan, Preston, PR1 2HE, UK
| | - Timothy P Dawson
- Department of Neuropathology, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston, PR2 9HT, UK
| | - Nihal Gurusinghe
- Department of Neurosurgery, Royal Preston Hospital, Lancashire Teaching Hospitals NHS Trust, Preston, PR2 9HT, UK
| | - Francis L Martin
- School of Pharmacy and Biomedical Sciences, UCLan, Preston, PR1 2HE, UK.
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30
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Luther E, Matus A, Eichberg DG, Shah AH, Ivan M. Stimulated Raman Histology for Intraoperative Guidance in the Resection of a Recurrent Atypical Spheno-orbital Meningioma: A Case Report and Review of Literature. Cureus 2019; 11:e5905. [PMID: 31777692 PMCID: PMC6853273 DOI: 10.7759/cureus.5905] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Meningiomas are the most common intracranial, extra-axial neoplasms and account for a significant proportion of all central nervous system (CNS) tumors. Regardless of the grade, treatment typically involves upfront surgical resection. However, in many instances, especially in meningiomas arising from the skull base, complete removal is often difficult given the close proximity to important anatomic structures. In this report, we discuss the use of stimulated Raman histology as a means to identify tissue boundaries during the resection of an extensive, recurrent, atypical spheno-orbital meningioma. We report a 75-year-old male with a history of a prior left frontotemporal craniotomy for a grade II meningioma three years prior, who presented with worsening left-sided visual loss and pronounced temporal bossing. Repeat magnetic resonance imaging (MRI) revealed a recurrent left spheno-orbital tumor suggestive of a meningioma extending into the middle cranial fossa, the lateral orbit, and the temporalis muscle. He underwent an extended orbito-pterional craniotomy, and intraoperative stimulated Raman histology aided in the identification of tumor margins within the orbit and the temporalis muscle in order to better preserve the normal orbital contents and muscle bulk of the infratemporal fossa. This case demonstrates the utility of stimulated Raman histology during the resection of invasive skull base tumors. The immediate intraoperative Raman histologic sections can clearly identify tissue boundaries and thus help preserve important anatomic structures. Continued development of this method can potentially improve the accuracy of intraoperative diagnoses and guide surgeons during tumor resections near eloquent anatomical regions or important normal structures.
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Affiliation(s)
- Evan Luther
- Neurological Surgery, University of Miami Miller School of Medicine, Miami, USA
| | - Alejandro Matus
- Neurological Surgery, Florida International University, Herbert Wertheim College of Medicine, Miami, USA
| | - Daniel G Eichberg
- Neurological Surgery, University of Miami Miller School of Medicine, Miami, USA
| | - Ashish H Shah
- Neurological Surgery, University of Miami Miller School of Medicine, Miami, USA
| | - Michael Ivan
- Neurological Surgery, University of Miami Miller School of Medicine, Miami, USA
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31
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Talari ACS, Rehman S, Rehman IU. Advancing cancer diagnostics with artificial intelligence and spectroscopy: identifying chemical changes associated with breast cancer. Expert Rev Mol Diagn 2019; 19:929-940. [PMID: 31461624 DOI: 10.1080/14737159.2019.1659727] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Background: Artificial intelligence (AI) and machine learning (ML) approaches in combination with Raman spectroscopy (RS) to obtain accurate medical diagnosis and decision-making is a way forward for understanding not only the chemical pathway to the progression of disease, but also for tailor-made personalized medicine. These processes remove unwanted affects in the spectra such as noise, fluorescence and normalization, and help in the optimization of spectral data by employing chemometrics. Methods: In this study, breast cancer tissues have been analyzed by RS in conjunction with principal component (PCA) and linear discriminate (LDA) analyses. Tissue microarray (TMA) breast biopsies were investigated using RS and chemometric methods and classified breast biopsies into luminal A, luminal B, HER2, and triple negative subtypes. Results: Supervised and unsupervised algorithms were applied on biopsy data to explore intra and inter data set biochemical changes associated with lipids, collagen, and nucleic acid content. LDA predicted specificity accuracy of luminal A, luminal B, HER2, and triple negative subtypes were 70%, 100%, 90%, and 96.7%, respectively. Conclusion: It is envisaged that a combination of RS with AI and ML may create a precise and accurate real-time methodology for cancer diagnosis and monitoring.
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Affiliation(s)
| | - Shazza Rehman
- Department of Medical Oncology, Airedale NHS Foundation Trust, Airedale General Hospital , Steeton , UK
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32
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Ralbovsky NM, Lednev IK. Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2019; 219:463-487. [PMID: 31075613 DOI: 10.1016/j.saa.2019.04.067] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 04/20/2019] [Accepted: 04/24/2019] [Indexed: 05/14/2023]
Abstract
Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.
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Affiliation(s)
- Nicole M Ralbovsky
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, NY 12222, USA.
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33
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Ito H, Uragami N, Miyazaki T, Yokoyama N, Inoue H. Raman spectroscopic evaluation of human serum using metal plate and 785- and 1064-nm excitation lasers. PLoS One 2019; 14:e0211986. [PMID: 30768643 PMCID: PMC6377121 DOI: 10.1371/journal.pone.0211986] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Accepted: 01/23/2019] [Indexed: 12/15/2022] Open
Abstract
In this study, we utilized a stainless steel (SUS304) plate for measuring the Raman scattering spectra of body fluid samples. Using this stainless steel plate, we recorded the Raman scattering spectra of 99.5% ethanol and human serum samples by performing irradiation with 785- and 1064-nm lasers. Raman scattering spectra with intensities equal to or greater than those reported previously were obtained. In addition, the Raman scattering spectra acquired using the 1064-nm laser were less influenced by autofluorescence than those obtained via use of the shorter-wavelength laser. Moreover, the shapes of the spectra did not show any dependence on integration time, and denaturation of the samples was minimal. Our method, based on 1064-nm laser and the stainless steel plate, provides performance equal to or better than the methods reported thus far for the measurement of Raman scattering spectra from liquid samples. This method can be employed to rapidly evaluate the components of serum in liquid form without using surface-enhanced Raman scattering.
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Affiliation(s)
- Hiroaki Ito
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Toyosu, Koto-ku, Tokyo, Japan
- * E-mail:
| | - Naoyuki Uragami
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Toyosu, Koto-ku, Tokyo, Japan
| | - Tomokazu Miyazaki
- Research Planning Department, JSR Corporation, Higashi-Sinbashi, Minato-ku, Tokyo, Japan
| | - Noboru Yokoyama
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Toyosu, Koto-ku, Tokyo, Japan
| | - Haruhiro Inoue
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Toyosu, Koto-ku, Tokyo, Japan
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34
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Morais CLM, Lilo T, Ashton KM, Davis C, Dawson TP, Gurusinghe N, Martin FL. Determination of meningioma brain tumour grades using Raman microspectroscopy imaging. Analyst 2019; 144:7024-7031. [DOI: 10.1039/c9an01551e] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Raman microspectroscopy imaging was used to distinguish 90 brain tissue samples into meningiomas Grade I and Grade II.
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Affiliation(s)
- Camilo L. M. Morais
- School of Pharmacy and Biomedical Sciences
- University of Central Lancashire
- Preston PR1 2HE
- UK
| | - Taha Lilo
- School of Pharmacy and Biomedical Sciences
- University of Central Lancashire
- Preston PR1 2HE
- UK
- Neurosurgery
| | - Katherine M. Ashton
- Neuropathology
- Royal Preston Hospital
- Lancashire Teaching Hospitals NHS Trust
- Preston PR2 9HT
- UK
| | - Charles Davis
- School of Pharmacy and Biomedical Sciences
- University of Central Lancashire
- Preston PR1 2HE
- UK
| | - Timothy P. Dawson
- Neuropathology
- Royal Preston Hospital
- Lancashire Teaching Hospitals NHS Trust
- Preston PR2 9HT
- UK
| | - Nihal Gurusinghe
- Neurosurgery
- Royal Preston Hospital
- Lancashire Teaching Hospitals NHS Trust
- Preston PR2 9HT
- UK
| | - Francis L. Martin
- School of Pharmacy and Biomedical Sciences
- University of Central Lancashire
- Preston PR1 2HE
- UK
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