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Vaddi A, Tadinada A, Lurie A, Deymier A. Evaluation of near-infrared Raman spectroscopy in the differentiation of cortical bone, trabecular bone, and Bio-Oss bone graft: an ex-vivo study. Oral Surg Oral Med Oral Pathol Oral Radiol 2023; 136:632-639. [PMID: 37394288 DOI: 10.1016/j.oooo.2023.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 05/18/2023] [Accepted: 05/20/2023] [Indexed: 07/04/2023]
Abstract
OBJECTIVE We evaluated the ability of near-infrared Raman spectroscopy (near-IR RS) to differentiate among cortical bone, trabecular bone, and Bio-Oss, a bovinebone-based graft material. STUDY DESIGN We obtained a thinly sliced section of the mandible to collect cortical and trabecular bone samples and placed compacted Bio-Oss bone graft into a partially edentulous mandible in a dry human skull to obtain a comparable Bio-Oss sample. We performed near-IR RS of the 3 samples and evaluated the resultant Raman spectra to evaluate their differences. RESULTS We identified 3 sets of spectroscopic markers that differentiated Bio-Oss from human bone. The first consisted of significant shifts in the location of the 960 cm-1 phosphate (PO43-) peak and a reduction in its width, suggesting that Bio-Oss is more crystalline than bone. The second was the reduced carbonate content of Bio-Oss compared to bone, as determined from the 1070 cm-1/960 cm-1 peak area ratio. The final marker was the lack of collagen-associated peaks in Bio-Oss compared to cortical and trabecular bone. CONCLUSIONS Near-IR RS can reliably differentiate human cortical and trabecular bone from Bio-Oss via 3 sets of spectral markers associated with mineral crystallinity, carbonate content, and collagen content that differ significantly between them. Integrating this modality into dental practice may assist in implant treatment planning.
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Affiliation(s)
- Anusha Vaddi
- Section of Oral and Maxillofacial Radiology, Division of Oral and Maxillofacial Diagnostic Sciences, UConn School of Dental Medicine, UConn Health, Farmington, CT, USA.
| | - Aditya Tadinada
- Section of Oral and Maxillofacial Radiology, Division of Oral and Maxillofacial Diagnostic Sciences, UConn School of Dental Medicine, UConn Health, Farmington, CT, USA
| | - Alan Lurie
- Section of Oral and Maxillofacial Radiology, Division of Oral and Maxillofacial Diagnostic Sciences, UConn School of Dental Medicine, UConn Health, Farmington, CT, USA
| | - Alix Deymier
- Department of Biomedical Engineering, UConn School of Dental Medicine, UConn Health, Farmington, CT, USA
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2
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Steybe D, Poxleitner P, Metzger MC, Rothweiler R, Beck J, Straehle J, Vach K, Weber A, Enderle-Ammour K, Werner M, Schmelzeisen R, Bronsert P. Stimulated Raman histology for histological evaluation of oral squamous cell carcinoma. Clin Oral Investig 2023; 27:4705-4713. [PMID: 37349642 PMCID: PMC10415463 DOI: 10.1007/s00784-023-05098-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 05/25/2023] [Indexed: 06/24/2023]
Abstract
OBJECTIVES To investigate whether in patients undergoing surgery for oral squamous cell carcinoma, stimulated Raman histology (SRH), in comparison with H&E-stained frozen sections, can provide accurate diagnoses regarding neoplastic tissue and sub-classification of non-neoplastic tissues. MATERIALS AND METHODS SRH, a technology based on Raman scattering, was applied to generate digital histopathologic images of 80 tissue samples obtained from 8 oral squamous cell carcinoma (OSCC) patients. Conventional H&E-stained frozen sections were then obtained from all 80 samples. All images/sections (SRH and H&E) were analyzed for squamous cell carcinoma, normal mucosa, connective tissue, muscle tissue, adipose tissue, salivary gland tissue, lymphatic tissue, and inflammatory cells. Agreement between SRH and H&E was evaluated by calculating Cohen's kappa. Accuracy of SRH compared to H&E was quantified by calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) as well as area under the receiver operating characteristic curve (AUC). RESULTS Thirty-six of 80 samples were classified as OSCC by H&E-based diagnosis. Regarding the differentiation between neoplastic and non-neoplastic tissue, high agreement between H&E and SRH (kappa: 0.880) and high accuracy of SRH (sensitivity: 100%; specificity: 90.91%; PPV: 90.00%, NPV: 100%; AUC: 0.954) were demonstrated. For sub-classification of non-neoplastic tissues, SRH performance was dependent on the type of tissue, with high agreement and accuracy for normal mucosa, muscle tissue, and salivary glands. CONCLUSION SRH provides high accuracy in discriminating neoplastic and non-neoplastic tissues. Regarding sub-classification of non-neoplastic tissues in OSCC patients, accuracy varies depending on the type of tissue examined. CLINICAL RELEVANCE This study demonstrates the potential of SRH for intraoperative imaging of fresh, unprocessed tissue specimens from OSCC patients without the need for sectioning or staining.
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Affiliation(s)
- David Steybe
- Department of Oral and Maxillofacial Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Center for Advanced Surgical Tissue Analysis (CAST), University of Freiburg, Freiburg, Germany.
- Department of Oral and Maxillofacial Surgery and Facial Plastic Surgery, LMU University Hospital, LMU Munich, Lindwurmstrasse 2a, 80337, Munich, Germany.
| | - Philipp Poxleitner
- Department of Oral and Maxillofacial Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Oral and Maxillofacial Surgery and Facial Plastic Surgery, LMU University Hospital, LMU Munich, Lindwurmstrasse 2a, 80337, Munich, Germany
| | - Marc C Metzger
- Department of Oral and Maxillofacial Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - René Rothweiler
- Department of Oral and Maxillofacial Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jürgen Beck
- Center for Advanced Surgical Tissue Analysis (CAST), University of Freiburg, Freiburg, Germany
- Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jakob Straehle
- Center for Advanced Surgical Tissue Analysis (CAST), University of Freiburg, Freiburg, Germany
- Department of Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kirstin Vach
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Andreas Weber
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kathrin Enderle-Ammour
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Martin Werner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center, University of Freiburg, Freiburg, Germany
| | - Rainer Schmelzeisen
- Department of Oral and Maxillofacial Surgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Center for Advanced Surgical Tissue Analysis (CAST), University of Freiburg, Freiburg, Germany
| | - Peter Bronsert
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Tumorbank Comprehensive Cancer Center Freiburg, Medical Center, University of Freiburg, Freiburg, Germany
- Core Facility for Histopathology and Digital Pathology, Medical Center, University of Freiburg, Freiburg, Germany
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3
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Sharma M, Li YC, Manjunatha SN, Tsai CL, Lin RM, Huang SF, Chang LB. Identification of Healthy Tissue from Malignant Tissue in Surgical Margin Using Raman Spectroscopy in Oral Cancer Surgeries. Biomedicines 2023; 11:1984. [PMID: 37509623 PMCID: PMC10377260 DOI: 10.3390/biomedicines11071984] [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: 06/07/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Accurate identification of tissue types in surgical margins is essential for ensuring the complete removal of cancerous cells and minimizing the risk of recurrence. The objective of this study was to explore the clinical utility of Raman spectroscopy for the detection of oral squamous cell carcinoma (OSCC) in both tumor and healthy tissues obtained from surgical resection specimens during surgery. This study enrolled a total of 64 patients diagnosed with OSCC. Among the participants, approximately 50% of the cases were classified as the most advanced stage, referred to as T4. Raman experiments were conducted on cryopreserved tissue samples collected from patients diagnosed with OSCC. Prominent spectral regions containing key oral biomarkers were analyzed using the partial least squares-support vector machine (PLS-SVM) method, which is a powerful multivariate analysis technique for discriminant analysis. This approach effectively differentiated OSCC tissue from non-OSCC tissue, achieving a sensitivity of 95.7% and a specificity of 93.3% with 94.7% accuracy. In the current study, Raman analysis of fresh tissue samples showed that OSCC tissues contained significantly higher levels of nucleic acids, proteins, and several amino acids compared to the adjacent healthy tissues. In addition to differentiating between OSCC and non-OSCC tissues, we have also explored the potential of Raman spectroscopy in classifying different stages of OSCC. Specifically, we have investigated the classification of T1, T2, T3, and T4 stages based on their Raman spectra. These findings emphasize the importance of considering both stage and subsite factors in the application of Raman spectroscopy for OSCC analysis. Future work will focus on expanding our tissue sample collection to better comprehend how different subsites influence the Raman spectra of OSCC at various stages, aiming to improve diagnostic accuracy and aid in identifying tumor-free margins during surgical interventions.
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Affiliation(s)
- Mukta Sharma
- Department of Electronic Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Ying-Chang Li
- Department of Ph.D. Program, Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung 411030, Taiwan
| | - S N Manjunatha
- Department of Electronic Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Chia-Lung Tsai
- Department of Electronic Engineering, Chang Gung University, Taoyuan 33302, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou 333, Taiwan
| | - Ray-Ming Lin
- Department of Electronic Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Shiang-Fu Huang
- Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou 333, Taiwan
- Department of Public Health, Chang Gung University, Taoyuan 33302, Taiwan
| | - Liann-Be Chang
- Department of Electronic Engineering, Chang Gung University, Taoyuan 33302, Taiwan
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4
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Nogueira MS, Barreto AL, Furukawa M, Rovai ES, Bastos A, Bertoncello G, Carvalho LFDCESD. FTIR spectroscopy as a point of care diagnostic tool for diabetes and periodontitis: A saliva analysis approach. Photodiagnosis Photodyn Ther 2022; 40:103036. [PMID: 35917904 DOI: 10.1016/j.pdpdt.2022.103036] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 07/12/2022] [Accepted: 07/25/2022] [Indexed: 12/14/2022]
Abstract
Diabetes mellitus (DM) is a general term for heterogeneous metabolic disorders whose main characteristic is chronic hyperglycemia. Considering that conventional diagnostic methods are currently unable of early DM detection and the number of diabetic patients has been increasing worldwide, there is a clinical need for novel diagnostic approaches. Fourier-transform infrared (FT-IR) spectroscopy technique an alternative non-invasive diagnostic method for real-time evaluation of biofluids such as saliva. This study aims evaluate the feasibility of diagnosing diabetes and periodontitis through saliva samples based on their FT-IR spectra. Our first collection and spectral analysis of samples was a pilot study and comprised a total of 23 patients, 2 healthy, 9 with diabetes and 12 with diabetes and periodontitis. By using weighted KNN as classifier, we have obtained an area under the Receiver Operating Characteristic curve of 0.92 and 0.95 when considering the diabetes or diabetes + periodontitis groups as positive groups, respectively.
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Affiliation(s)
- Marcelo Saito Nogueira
- Tyndall National Institute, Cork, Ireland; Department of Physics, University College Cork, Cork, Ireland.
| | | | | | | | - Alice Bastos
- Centro Universitário Braz Cubas, Mogi das Cruzes, Brazil
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5
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Insights into Biochemical Sources and Diffuse Reflectance Spectral Features for Colorectal Cancer Detection and Localization. Cancers (Basel) 2022; 14:cancers14225715. [PMID: 36428806 PMCID: PMC9688116 DOI: 10.3390/cancers14225715] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/07/2022] [Accepted: 11/09/2022] [Indexed: 11/23/2022] Open
Abstract
Colorectal cancer (CRC) is the third most common and second most deadly type of cancer worldwide. Early detection not only reduces mortality but also improves patient prognosis by allowing the use of minimally invasive techniques to remove cancer while avoiding major surgery. Expanding the use of microsurgical techniques requires accurate diagnosis and delineation of the tumor margins in order to allow complete excision of cancer. We have used diffuse reflectance spectroscopy (DRS) to identify the main optical CRC biomarkers and to optimize parameters for the integration of such technologies into medical devices. A total number of 2889 diffuse reflectance spectra were collected in ex vivo specimens from 47 patients. Short source-detector distance (SDD) and long-SDD fiber-optic probes were employed to measure tissue layers from 0.5 to 1 mm and from 0.5 to 1.9 mm deep, respectively. The most important biomolecules contributing to differentiating DRS between tissue types were oxy- and deoxy-hemoglobin (Hb and HbO2), followed by water and lipid. Accurate tissue classification and potential DRS device miniaturization using Hb, HbO2, lipid and water data were achieved particularly well within the wavelength ranges 350-590 nm and 600-1230 nm for the short-SDD probe, and 380-400 nm, 420-610 nm, and 650-950 nm for the long-SDD probe.
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6
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Faur C, Falamas A, Chirila M, Roman R, Rotaru H, Moldovan M, Albu S, Baciut M, Robu I, Hedesiu M. Raman spectroscopy in oral cavity and oropharyngeal cancer: a systematic review. Int J Oral Maxillofac Surg 2022; 51:1373-1381. [DOI: 10.1016/j.ijom.2022.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 02/22/2022] [Accepted: 02/25/2022] [Indexed: 12/24/2022]
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7
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Cialla-May D, Krafft C, Rösch P, Deckert-Gaudig T, Frosch T, Jahn IJ, Pahlow S, Stiebing C, Meyer-Zedler T, Bocklitz T, Schie I, Deckert V, Popp J. Raman Spectroscopy and Imaging in Bioanalytics. Anal Chem 2021; 94:86-119. [PMID: 34920669 DOI: 10.1021/acs.analchem.1c03235] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Dana Cialla-May
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Christoph Krafft
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Petra Rösch
- Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Tanja Deckert-Gaudig
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Torsten Frosch
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Izabella J Jahn
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Susanne Pahlow
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
| | - Clara Stiebing
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
| | - Tobias Meyer-Zedler
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Thomas Bocklitz
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Iwan Schie
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Ernst-Abbe-Hochschule Jena, University of Applied Sciences, Department of Biomedical Engineering and Biotechnology, Carl-Zeiss-Promenade 2, 07745 Jena, Germany
| | - Volker Deckert
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
| | - Jürgen Popp
- Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance - Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany.,Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany.,InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
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8
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Nogueira MS, Leal LB, Marcarini WD, Pimentel RL, Muller M, Vassallo PF, Campos LCG, Dos Santos L, Luiz WB, Mill JG, Barauna VG, de Carvalho LFDCES. Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning. Sci Rep 2021; 11:15409. [PMID: 34635702 PMCID: PMC8505540 DOI: 10.1038/s41598-021-93511-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 06/10/2021] [Indexed: 12/13/2022] Open
Abstract
Early diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.
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Affiliation(s)
- Marcelo Saito Nogueira
- Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, Cork, T12R5CP, Ireland.
| | - Leonardo Barbosa Leal
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Wena Dantas Marcarini
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil.,Faculdade Vale do Cricaré, São Matheus, Brazil
| | - Raquel Lemos Pimentel
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Matheus Muller
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | | | | | - Leonardo Dos Santos
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Wilson Barros Luiz
- Department of Biological Science, Santa Cruz State University, Ilhéus, BA, Brazil
| | - José Geraldo Mill
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Valerio Garrone Barauna
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
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9
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Nogueira MS, Leal LB, Marcarini WD, Pimentel RL, Muller M, Vassallo PF, Campos LCG, Dos Santos L, Luiz WB, Mill JG, Barauna VG, de Carvalho LFDCES. Rapid diagnosis of COVID-19 using FT-IR ATR spectroscopy and machine learning. Sci Rep 2021. [PMID: 34635702 DOI: 10.1038/s41598-021-93511-] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2023] Open
Abstract
Early diagnosis of COVID-19 in suspected patients is essential for contagion control and damage reduction strategies. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in oropharyngeal swab suspension fluid to predict COVID-19 positive samples. The study included samples of 243 patients from two Brazilian States. Samples were transported by using different viral transport mediums (liquid 1 or 2). Clinical COVID-19 diagnosis was performed by the RT-PCR. We built a classification model based on partial least squares (PLS) associated with cosine k-nearest neighbours (KNN). Our analysis led to 84% and 87% sensitivity, 66% and 64% specificity, and 76.9% and 78.4% accuracy for samples of liquids 1 and 2, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective solution for high-throughput screening of suspect patients for COVID-19 in health care centres and emergency departments.
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Affiliation(s)
- Marcelo Saito Nogueira
- Tyndall National Institute, University College Cork, Lee Maltings Complex, Dyke Parade, Cork, T12R5CP, Ireland.
| | - Leonardo Barbosa Leal
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Wena Dantas Marcarini
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
- Faculdade Vale do Cricaré, São Matheus, Brazil
| | - Raquel Lemos Pimentel
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Matheus Muller
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | | | | | - Leonardo Dos Santos
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Wilson Barros Luiz
- Department of Biological Science, Santa Cruz State University, Ilhéus, BA, Brazil
| | - José Geraldo Mill
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
| | - Valerio Garrone Barauna
- Department of Physiological Sciences, Federal University of Espírito Santo (UFES), Vitória, Brazil
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10
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Magsumova OA, Polkanova VA, Timchenko EV, Volova LT. [Raman spectroscopy and its application in different areas of medicine]. STOMATOLOGII︠A︡ 2021; 100:137-142. [PMID: 34357743 DOI: 10.17116/stomat2021100041137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The aim of the review is to learn about the areas of application of the Raman spectroscopy in medicine, particularly in dentistry. The method is widely used in biology, medicine, pharmacy, forensic science, gemology, food industry and other industries. The main advantages of Raman spectroscopy are no need for sample preparation and small amounts of the object of study, as well as the ability to contactlessly obtain unique information about the conformation and microenvironment of living cell molecules. The disadvantages are high costs of the equipment that are compensated with the long-term use by having no costs for additional reagents. The combinatorial scattering is used in dermatology, as it is a high-accuracy automated method of visualization and diagnostics of both benign growths as pigmented nevus, seborrheic keratosis, and malignant neoplasms as melanoma and basal cell carcinoma. This method is an analytical tool for diagnosing various diseases, making the direct measurements in hard and liquid media easier, does not require special treatment of samples and is not sensitive to absorption bands. The Raman spectroscopy use in dentistry allows diagnosing and comparative analysis of the changes of hard tissues of teeth and mucous membrane of the mouth, which improves security and rationalization of treatment and further prevention of complications before and after making different operations.
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Affiliation(s)
| | | | | | - L T Volova
- Samara State Medical University, Samara, Russia
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11
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Raman microspectroscopy and Raman imaging reveal biomarkers specific for thoracic aortic aneurysms. CELL REPORTS MEDICINE 2021; 2:100261. [PMID: 34095874 PMCID: PMC8149374 DOI: 10.1016/j.xcrm.2021.100261] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 01/29/2021] [Accepted: 04/06/2021] [Indexed: 01/30/2023]
Abstract
Aortic rupture and dissection are life-threatening complications of ascending thoracic aortic aneurysms (aTAAs), and risk assessment has been largely based on the monitoring of lumen size enlargement. Temporal changes in the extracellular matrix (ECM), which has a critical impact on aortic remodeling, are not routinely evaluated, and cardiovascular biomarkers do not exist to predict aTAA formation. Here, Raman microspectroscopy and Raman imaging are used to identify spectral biomarkers specific for aTAAs in mice and humans by multivariate data analysis (MVA). Multivariate curve resolution-alternating least-squares (MCR-ALS) combined with Lasso regression reveals elastic fiber-derived (Ce1) and collagen fiber-derived (Cc6) components that are significantly increased in aTAA lesions of murine and human aortic tissues. In particular, Cc6 detects changes in amino acid residues, including phenylalanine, tyrosine, tryptophan, cysteine, aspartate, and glutamate. Ce1 and Cc6 may serve as diagnostic Raman biomarkers that detect alterations of amino acids derived from aneurysm lesions. Label-free Raman imaging of human/murine ascending thoracic aortic aneurysm (aTAA) Multivariate analysis of Raman spectra allows detection of aTAA molecular features Identification of spectral biomarkers for aTAA in elastic and collagen fibers Alterations in amino acid spectra correlate with aTAA formation
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Nogueira MS, Brugnera Junior A, Bagnato VS, Panhóca VH. Evaluation of the Whitening Effectiveness of Violet Illumination Alone or Combined with Hydrogen Peroxide Gel. Photobiomodul Photomed Laser Surg 2021; 39:395-402. [PMID: 33439765 DOI: 10.1089/photob.2020.4846] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Objective: To evaluate the effectiveness of the whitening and washing steps of a treatment using violet illumination (VI) alone or combined with hydrogen peroxide gel. In addition, we evaluated the color change after cleaning the tooth with and without mineral oil. Methods: First, 16 bovine teeth were extracted and stored in 5% thymol solution. Next, the teeth were collected and cleaned. Then, the teeth were stained with instant coffee solution for 24 h. The teeth were divided in four groups: control, VI without 35% hydrogen peroxide gel (VI), VI with 35% hydrogen peroxide gel (VI + gel), and VI without 35% hydrogen peroxide gel and cleaned with mineral oil before washing (VI + oil). Results: The whitening treatment VI + gel was able to completely restore the teeth whiteness and make the teeth 31.2% less yellow than prior coffee staining. The VI + oil treatment led to about 3.7 times the whiteness and yellowness changes observed in the VI treatment and restored 51% of the whiteness lost by staining. Conclusions: The VI + gel treatment can be recommended against coffee stains and should be further investigated for other types of tooth stains. In addition, cleaning the tooth surface with mineral oil could be an alternative to increase the performance of whitening treatments.
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Affiliation(s)
| | - Aldo Brugnera Junior
- Physics Institute of São Carlos, University of Sao Paulo, São Carlos, Brazil.,Department of Dental Science, Faculty of Medicine, Emdola, University in Liège, Liège, Belgium
| | | | - Vitor Hugo Panhóca
- São Carlos Institute of Physics, University of São Paulo, São Carlos, Brazil
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Nogueira MS, Maryam S, Amissah M, Lu H, Lynch N, Killeen S, O’Riordain M, Andersson-Engels S. Evaluation of wavelength ranges and tissue depth probed by diffuse reflectance spectroscopy for colorectal cancer detection. Sci Rep 2021; 11:798. [PMID: 33436684 PMCID: PMC7804163 DOI: 10.1038/s41598-020-79517-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 12/04/2020] [Indexed: 01/29/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common type of cancer worldwide and the second most deadly. Recent research efforts have focused on developing non-invasive techniques for CRC detection. In this study, we evaluated the diagnostic capabilities of diffuse reflectance spectroscopy (DRS) for CRC detection by building 6 classification models based on support vector machines (SVMs). Our dataset consists of 2889 diffuse reflectance spectra collected from freshly excised ex vivo tissues of 47 patients over wavelengths ranging from 350 and 1919 nm with source-detector distances of 630-µm and 2500-µm to probe different depths. Quadratic SVMs were used and performance was evaluated using twofold cross-validation on 10 iterations of randomized training and test sets. We achieved (93.5 ± 2.4)% sensitivity, (94.0 ± 1.7)% specificity AUC by probing the superficial colorectal tissue and (96.1 ± 1.8)% sensitivity, (95.7 ± 0.6)% specificity AUC by sampling deeper tissue layers. To the best of our knowledge, this is the first DRS study to investigate the potential of probing deeper tissue layers using larger SDD probes for CRC detection in the luminal wall. The data analysis showed that using a broader spectrum and longer near-infrared wavelengths can improve the diagnostic accuracy of CRC as well as probing deeper tissue layers.
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Affiliation(s)
- Marcelo Saito Nogueira
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
| | - Siddra Maryam
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
| | - Michael Amissah
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
| | - Huihui Lu
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland
| | - Noel Lynch
- grid.411785.e0000 0004 0575 9497Department of Surgery, Mercy University Hospital, Cork, Ireland
| | - Shane Killeen
- grid.411785.e0000 0004 0575 9497Department of Surgery, Mercy University Hospital, Cork, Ireland
| | - Micheal O’Riordain
- grid.411785.e0000 0004 0575 9497Department of Surgery, Mercy University Hospital, Cork, Ireland
| | - Stefan Andersson-Engels
- grid.7872.a0000000123318773Tyndall National Institute, Lee Maltings, Dyke Parade, Cork, Ireland ,grid.7872.a0000000123318773Department of Physics, University College Cork, College Road, Cork, Ireland
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Jeng MJ, Sharma M, Sharma L, Huang SF, Chang LB, Wu SL, Chow L. Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection. Cancers (Basel) 2020; 12:E3364. [PMID: 33202869 PMCID: PMC7696965 DOI: 10.3390/cancers12113364] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/27/2020] [Accepted: 11/10/2020] [Indexed: 12/15/2022] Open
Abstract
In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA-LDA and PCA-QDA models. The PCA-LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA-LDA and PCA-QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.
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Affiliation(s)
- Ming-Jer Jeng
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan; (M.-J.J.); (M.S.)
- Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou 244, Taiwan
| | - Mukta Sharma
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan; (M.-J.J.); (M.S.)
| | - Lokesh Sharma
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 333, Taiwan; (L.S.); (S.-L.W.)
| | - Shiang-Fu Huang
- Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou 244, Taiwan
- Department of Public Health, Chang Gung University, Taoyuan 333, Taiwan
- Graduate Institute of Clinical Medical Sciences, Chang Gung University, Taoyuan 333, Taiwan
| | - Liann-Be Chang
- Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou 244, Taiwan
- Green Technology Research Center, Chang Gung University, Guishan, Taoyuan 333, Taiwan
| | - Shih-Lin Wu
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 333, Taiwan; (L.S.); (S.-L.W.)
- Department of Cardiology, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Lee Chow
- Department of Physics, University of Central Florida, Orlando, FL 32816, USA;
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15
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Behl I, Calado G, Vishwakarma A, Flint S, Galvin S, Healy CM, Leite Pimentel M, Malkin A, Byrne HJ, Lyng FM. Raman microspectroscopic study for the detection of oral field cancerisation using brush biopsy samples. JOURNAL OF BIOPHOTONICS 2020; 13:e202000131. [PMID: 32602241 DOI: 10.1002/jbio.202000131] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 05/30/2020] [Accepted: 06/10/2020] [Indexed: 06/11/2023]
Abstract
Field cancerisation (FC) is potentially an underlying cause of poor treatment outcomes of oral squamous cell carcinoma (OSCC). To explore the phenomenon using Raman microspectroscopy, brush biopsies from the buccal mucosa, tongue, gingiva and alveolus of healthy donors (n = 40) and from potentially malignant lesions (PML) of Dysplasia Clinic patients (n = 40) were examined. Contralateral normal samples (n = 38) were also collected from the patients. Raman spectra were acquired from the nucleus and cytoplasm of each cell, and subjected to partial least squares-discriminant analysis (PLS-DA). High discriminatory accuracy for donor and PML samples was achieved for both cytopalmic and nuclear data sets. Notably, contralateral normal (patient) samples were also accurately discriminated from donor samples and contralateral normal samples from patients with multiple lesions showed a similar spectral profile to PML samples, strongly indicating a FC effect. These findings support the potential of Raman microspectroscopy as a screening tool for PML using oral exfoliated cells.
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Affiliation(s)
- Isha Behl
- Centre for Radiation and Environmental Science, FOCAS Research Institute, Technological University Dublin, Dublin, Ireland
- School of Physics, Technological University Dublin, Dublin, Ireland
| | - Genecy Calado
- Centre for Radiation and Environmental Science, FOCAS Research Institute, Technological University Dublin, Dublin, Ireland
- School of Physics, Technological University Dublin, Dublin, Ireland
| | - Anika Vishwakarma
- Centre for Radiation and Environmental Science, FOCAS Research Institute, Technological University Dublin, Dublin, Ireland
- School of Physics, Technological University Dublin, Dublin, Ireland
| | - Stephen Flint
- Oral Medicine Unit, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Sheila Galvin
- Oral Medicine Unit, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Claire M Healy
- Oral Medicine Unit, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Marina Leite Pimentel
- Division of Restorative Dentistry and Periodontology, Dublin Dental University Hospital, Trinity College Dublin, Dublin, Ireland
| | - Alison Malkin
- School of Biological Sciences, Technological University Dublin, Dublin, Ireland
| | - Hugh J Byrne
- FOCAS Research Institute, Technological University Dublin, Dublin, Ireland
| | - Fiona M Lyng
- Centre for Radiation and Environmental Science, FOCAS Research Institute, Technological University Dublin, Dublin, Ireland
- School of Physics, Technological University Dublin, Dublin, Ireland
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Jeng MJ, Sharma M, Sharma L, Chao TY, Huang SF, Chang LB, Wu SL, Chow L. Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection. J Clin Med 2019; 8:E1313. [PMID: 31461884 PMCID: PMC6780219 DOI: 10.3390/jcm8091313] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 08/17/2019] [Accepted: 08/22/2019] [Indexed: 12/11/2022] Open
Abstract
Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer.
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Affiliation(s)
- Ming-Jer Jeng
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
- Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou 244, Taiwan
| | - Mukta Sharma
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
| | - Lokesh Sharma
- AI Innovation Research Center, Chang Gung University, Taoyuan 333, Taiwan
| | - Ting-Yu Chao
- Department of Electronic Engineering, Chang Gung University, Taoyuan 333, Taiwan
| | - Shiang-Fu Huang
- Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou 244, Taiwan.
- Department of Public Health, Chang Gung University, Taoyuan 333, Taiwan.
| | - Liann-Be Chang
- Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou 244, Taiwan.
- Green Technology Research Center, Chang Gung University, Taoyuan 333, Taiwan.
| | - Shih-Lin Wu
- AI Innovation Research Center, Chang Gung University, Taoyuan 333, Taiwan
- Department of Cardiology, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 333, Taiwan
| | - Lee Chow
- Department of Physics, University of Central Florida, Orlando, FL 32816, USA
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