1
|
Maryam S, Nogueira MS, Gautam R, Krishnamoorthy S, Venkata Sekar SK, Kho KW, Lu H, Ni Riordain R, Feeley L, Sheahan P, Burke R, Andersson-Engels S. Label-Free Optical Spectroscopy for Early Detection of Oral Cancer. Diagnostics (Basel) 2022; 12:diagnostics12122896. [PMID: 36552903 PMCID: PMC9776497 DOI: 10.3390/diagnostics12122896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/16/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022] Open
Abstract
Oral cancer is the 16th most common cancer worldwide. It commonly arises from painless white or red plaques within the oral cavity. Clinical outcome is highly related to the stage when diagnosed. However, early diagnosis is complex owing to the impracticality of biopsying every potentially premalignant intraoral lesion. Therefore, there is a need to develop a non-invasive cost-effective diagnostic technique to differentiate non-malignant and early-stage malignant lesions. Optical spectroscopy may provide an appropriate solution to facilitate early detection of these lesions. It has many advantages over traditional approaches including cost, speed, objectivity, sensitivity, painlessness, and ease-of use in clinical setting for real-time diagnosis. This review consists of a comprehensive overview of optical spectroscopy for oral cancer diagnosis, epidemiology, and recent improvements in this field for diagnostic purposes. It summarizes major developments in label-free optical spectroscopy, including Raman, fluorescence, and diffuse reflectance spectroscopy during recent years. Among the wide range of optical techniques available, we chose these three for this review because they have the ability to provide biochemical information and show great potential for real-time deep-tissue point-based in vivo analysis. This review also highlights the importance of saliva-based potential biomarkers for non-invasive early-stage diagnosis. It concludes with the discussion on the scope of development and future demands from a clinical point of view.
Collapse
Affiliation(s)
- Siddra Maryam
- Tyndall National Institute, University College Cork, T12 R229 Cork, Ireland
- Correspondence:
| | | | - Rekha Gautam
- Tyndall National Institute, University College Cork, T12 R229 Cork, Ireland
| | | | | | - Kiang Wei Kho
- Tyndall National Institute, University College Cork, T12 R229 Cork, Ireland
| | - Huihui Lu
- Tyndall National Institute, University College Cork, T12 R229 Cork, Ireland
| | - Richeal Ni Riordain
- ENTO Research Institute, University College Cork, T12 R229 Cork, Ireland
- Cork University Dental School and Hospital, Wilton, T12 E8YV Cork, Ireland
| | - Linda Feeley
- ENTO Research Institute, University College Cork, T12 R229 Cork, Ireland
- Cork University Hospital, T12 DC4A Cork, Ireland
| | - Patrick Sheahan
- ENTO Research Institute, University College Cork, T12 R229 Cork, Ireland
- South Infirmary Victoria University Hospital, T12 X23H Cork, Ireland
| | - Ray Burke
- Tyndall National Institute, University College Cork, T12 R229 Cork, Ireland
| | | |
Collapse
|
2
|
Liu Y, Bilodeau E, Pollack B, Batmanghelich K. Automated detection of premalignant oral lesions on whole slide images using convolutional neural networks. Oral Oncol 2022; 134:106109. [PMID: 36126604 DOI: 10.1016/j.oraloncology.2022.106109] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/12/2022] [Accepted: 08/29/2022] [Indexed: 11/30/2022]
Abstract
INTRODUCTION Oral epithelial dysplasia (OED) is a precursor lesion to oral squamous cell carcinoma, a disease with a reported overall survival rate of 56 percent across all stages. Accurate detection of OED is critical as progression to oral cancer can be impeded with complete excision of premalignant lesions. However, previous research has demonstrated that the task of grading of OED, even when performed by highly trained experts, is subject to high rates of reader variability and misdiagnosis. Thus, our study aims to develop a convolutional neural network (CNN) model that can identify regions suspicious for OED whole-slide pathology images. METHODS During model development, we optimized key training hyperparameters including loss function on 112 pathologist annotated cases between the training and validation sets. Then, we compared OED segmentation and classification metrics between two well-established CNN architectures for medical imaging, DeepLabv3+ and UNet++. To further assess generalizability, we assessed case-level performance of a held-out test set of 44 whole-slide images. RESULTS DeepLabv3+ outperformed UNet++ in overall accuracy, precision, and segmentation metrics in a 4-fold cross validation study. When applied to the held-out test set, our best performing DeepLabv3+ model achieved an overall accuracy and F1-Score of 93.3 percent and 90.9 percent, respectively. CONCLUSION The present study trained and implemented a CNN-based deep learning model for identification and segmentation of oral epithelial dysplasia (OED) with reasonable success. Computer assisted detection was shown to be feasible in detecting premalignant/precancerous oral lesions, laying groundwork for eventual clinical implementation.
Collapse
Affiliation(s)
- Yingci Liu
- University of Pittsburgh, Department of Biomedical Informatics, 5607, Baum Boulevard, Pittsburgh, PA 15206, USA; Rutgers School of Dental Medicine, 110, Bergen St, Newark, NJ 07101, USA.
| | - Elizabeth Bilodeau
- University of Pittsburgh School of Dental Medicine, 3501 Terrace St., Pittsburgh, PA 15206, USA
| | - Brian Pollack
- University of Pittsburgh, Department of Biomedical Informatics, 5607, Baum Boulevard, Pittsburgh, PA 15206, USA
| | - Kayhan Batmanghelich
- University of Pittsburgh, Department of Biomedical Informatics, 5607, Baum Boulevard, Pittsburgh, PA 15206, USA
| |
Collapse
|
3
|
Ingham J, Smith CI, Ellis BG, Whitley CA, Triantafyllou A, Gunning PJ, Barrett SD, Gardener P, Shaw RJ, Risk JM, Weightman P. Prediction of malignant transformation in oral epithelial dysplasia using machine learning. IOP SCINOTES 2022; 3:034001. [PMID: 36277682 PMCID: PMC9580266 DOI: 10.1088/2633-1357/ac95e2] [Citation(s) in RCA: 3] [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: 06/17/2022] [Revised: 09/14/2022] [Accepted: 09/28/2022] [Indexed: 11/12/2022] Open
Abstract
A machine learning algorithm (MLA) has been applied to a Fourier transform infrared spectroscopy (FTIR) dataset previously analysed with a principal component analysis (PCA) linear discriminant analysis (LDA) model. This comparison has confirmed the robustness of FTIR as a prognostic tool for oral epithelial dysplasia (OED). The MLA is able to predict malignancy with a sensitivity of 84 ± 3% and a specificity of 79 ± 3%. It provides key wavenumbers that will be important for the development of devices that can be used for improved prognosis of OED.
Collapse
Affiliation(s)
- James Ingham
- Department of Physics, University of Liverpool, L69 7ZE, United Kingdom
| | - Caroline I Smith
- Department of Physics, University of Liverpool, L69 7ZE, United Kingdom
| | - Barnaby G Ellis
- Department of Physics, University of Liverpool, L69 7ZE, United Kingdom
| | - Conor A Whitley
- Department of Physics, University of Liverpool, L69 7ZE, United Kingdom
| | - Asterios Triantafyllou
- Department of Pathology, Liverpool Clinical Laboratories, University of Liverpool, L69 3GA, United Kingdom
| | - Philip J Gunning
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L3 9TA, United Kingdom
| | - Steve D Barrett
- Department of Physics, University of Liverpool, L69 7ZE, United Kingdom
| | - Peter Gardener
- Manchester Institute of Biotechnology, University of Manchester, M1 7DN, United Kingdom
| | - Richard J Shaw
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L3 9TA, United Kingdom
- Regional Maxillofacial Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, L9 7AL, United Kingdom
| | - Janet M Risk
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, L3 9TA, United Kingdom
| | - Peter Weightman
- Department of Physics, University of Liverpool, L69 7ZE, United Kingdom
| |
Collapse
|
4
|
Ellis BG, Whitley CA, Triantafyllou A, Gunning PJ, Smith CI, Barrett SD, Gardner P, Shaw RJ, Weightman P, Risk JM. Prediction of malignant transformation in oral epithelial dysplasia using infrared absorbance spectra. PLoS One 2022; 17:e0266043. [PMID: 35333891 PMCID: PMC8956195 DOI: 10.1371/journal.pone.0266043] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/14/2022] [Indexed: 11/23/2022] Open
Abstract
Oral epithelial dysplasia (OED) is a histopathologically-defined, potentially premalignant condition of the oral cavity. The rate of transformation to frank carcinoma is relatively low (12% within 2 years) and prediction based on histopathological grade is unreliable, leading to both over- and under-treatment. Alternative approaches include infrared (IR) spectroscopy, which is able to classify cancerous and non-cancerous tissue in a number of cancers, including oral. The aim of this study was to explore the capability of FTIR (Fourier-transform IR) microscopy and machine learning as a means of predicting malignant transformation of OED. Supervised, retrospective analysis of longitudinally-collected OED biopsy samples from 17 patients with high risk OED lesions: 10 lesions transformed and 7 did not over a follow-up period of more than 3 years. FTIR spectra were collected from routine, unstained histopathological sections and machine learning used to predict malignant transformation, irrespective of OED classification. PCA-LDA (principal component analysis followed by linear discriminant analysis) provided evidence that the subsequent transforming status of these 17 lesions could be predicted from FTIR data with a sensitivity of 79 ± 5% and a specificity of 76 ± 5%. Six key wavenumbers were identified as most important in this classification. Although this pilot study used a small cohort, the strict inclusion criteria and classification based on known outcome, rather than OED grade, make this a novel study in the field of FTIR in oral cancer and support the clinical potential of this technology in the surveillance of OED.
Collapse
Affiliation(s)
- Barnaby G. Ellis
- Department of Physics, University of Liverpool, Liverpool, United Kingdom
| | - Conor A. Whitley
- Department of Physics, University of Liverpool, Liverpool, United Kingdom
| | - Asterios Triantafyllou
- Department of Pathology, Liverpool Clinical Laboratories, University of Liverpool, Liverpool, United Kingdom
| | - Philip J. Gunning
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| | - Caroline I. Smith
- Department of Physics, University of Liverpool, Liverpool, United Kingdom
| | - Steve D. Barrett
- Department of Physics, University of Liverpool, Liverpool, United Kingdom
| | - Peter Gardner
- Manchester Institute of Biotechnology, University of Manchester, Manchester, United Kingdom
| | - Richard J. Shaw
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
- Regional Maxillofacial Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, United Kingdom
- * E-mail:
| | - Peter Weightman
- Department of Physics, University of Liverpool, Liverpool, United Kingdom
| | - Janet M. Risk
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, United Kingdom
| |
Collapse
|
5
|
Matthies L, Gebrekidan MT, Tegtmeyer JF, Oetter N, Rohde M, Vollkommer T, Smeets R, Wilczak W, Stelzle F, Gosau M, Braeuer AS, Knipfer C. Optical diagnosis of oral cavity lesions by label-free Raman spectroscopy. BIOMEDICAL OPTICS EXPRESS 2021; 12:836-851. [PMID: 33680545 PMCID: PMC7901324 DOI: 10.1364/boe.409456] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 11/21/2020] [Accepted: 12/08/2020] [Indexed: 06/12/2023]
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers and frequently preceded by non-malignant lesions. Using Shifted-Excitation Raman Difference Spectroscopy (SERDS), principal component and linear discriminant analysis in native tissue specimens, 9500 raw Raman spectra of OSCC, 4300 of non-malignant lesions and 4200 of physiological mucosa were evaluated. Non-malignant lesions were distinguished from physiological mucosa with a classification accuracy of 95.3% (95.4% sensitivity, 95.2% specificity, area under the curve (AUC) 0.99). Discriminating OSCC from non-malignant lesions showed an accuracy of 88.4% (93.7% sensitivity, 76.7% specificity, AUC 0.93). OSCC was identified against physiological mucosa with an accuracy of 89.8% (93.7% sensitivity, 81.0% specificity, AUC 0.90). These findings underline the potential of SERDS for the diagnosis of oral cavity lesions.
Collapse
Affiliation(s)
- Levi Matthies
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
- These authors contributed equally
| | - Medhanie T. Gebrekidan
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Straße 6, D-91054 Erlangen, Germany
- Technische Universität Bergakademie Freiberg (TUBAF), Institute of Thermal-, Environmental- and Resources‘ Process Engineering (ITUN), Leipziger Straße 28, D-09599 Freiberg, Germany
- These authors contributed equally
| | - Jasper F. Tegtmeyer
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
| | - Nicolai Oetter
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Straße 6, D-91054 Erlangen, Germany
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Department of Oral and Maxillofacial Surgery, Glückstraße 11, D-91054 Erlangen, Germany
| | - Maximilian Rohde
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Department of Oral and Maxillofacial Surgery, Glückstraße 11, D-91054 Erlangen, Germany
| | - Tobias Vollkommer
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
| | - Ralf Smeets
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
| | - Waldemar Wilczak
- University Medical Center Hamburg-Eppendorf (UKE), Institute of Pathology, Martinistraße 52, D-20246 Hamburg, Germany
| | - Florian Stelzle
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen Graduate School in Advanced Optical Technologies (SAOT), Paul-Gordan-Straße 6, D-91054 Erlangen, Germany
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Department of Oral and Maxillofacial Surgery, Glückstraße 11, D-91054 Erlangen, Germany
| | - Martin Gosau
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
| | - Andreas S. Braeuer
- Technische Universität Bergakademie Freiberg (TUBAF), Institute of Thermal-, Environmental- and Resources‘ Process Engineering (ITUN), Leipziger Straße 28, D-09599 Freiberg, Germany
| | - Christian Knipfer
- University Medical Center Hamburg-Eppendorf (UKE), Department of Oral and Maxillofacial Surgery, Martinistraße 52, D-20246 Hamburg, Germany
| |
Collapse
|
6
|
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.
Collapse
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.
| |
Collapse
|
7
|
Ghosh A, Raha S, Dey S, Chatterjee K, Roy Chowdhury A, Barui A. Chemometric analysis of integrated FTIR and Raman spectra obtained by non-invasive exfoliative cytology for the screening of oral cancer. Analyst 2019; 144:1309-1325. [DOI: 10.1039/c8an02092b] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
FTIR spectroscopy and Raman spectroscopy of biological analytes are increasingly explored as screening tools for early detection of cancer.
Collapse
Affiliation(s)
- Aritri Ghosh
- Centre for Healthcare Science and Technology
- Indian Institute of Engineering Science and Technology
- Howrah 711103
- India
| | - Sreyan Raha
- Department of Physics
- Bose Institute
- Kolkata-700009
- India
| | - Susmita Dey
- Centre for Healthcare Science and Technology
- Indian Institute of Engineering Science and Technology
- Howrah 711103
- India
| | - Kabita Chatterjee
- Department of Oral and Maxillofacial Pathology
- Buddha Institute of Dental Sciences
- Patna 800020
- India
| | - Amit Roy Chowdhury
- Department of Aerospace and Applied Mechanics
- Indian Institute of Engineering Science and Technology
- Howrah 711103
- India
| | - Ananya Barui
- Centre for Healthcare Science and Technology
- Indian Institute of Engineering Science and Technology
- Howrah 711103
- India
| |
Collapse
|
8
|
Xue L, Yan B, Li Y, Tan Y, Luo X, Wang M. Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for tumor stages detection and histologic grades classification of oral squamous cell carcinoma. Int J Nanomedicine 2018; 13:4977-4986. [PMID: 30214201 PMCID: PMC6124473 DOI: 10.2147/ijn.s167996] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Background Tumor stages detection and histologic grades classification are essential for the diagnosis and prognosis of oral squamous cell carcinoma (OSCC). In this research, we apply surface-enhanced Raman spectroscopy (SERS) of blood serum to detect the tumor stages and histologic classification of OSCC. Methods According to TNM classification and World Health Organization histologic grading system, the blood serum samples were collected from a total of 135 OSCC patients in the different tumor stages and histologic grades. Then the SERS spectra of serum samples from OSCC patients were diagnosed and classified into different groups using principal component analysis (PCA) and linear discriminant analysis (LDA) based on the tumor sizes, lymph node metastasis and histologic grades. Results The SERS spectra of blood serum samples have shown the distinct changes and differences compared with each other, which were assigned to the biomolecule alterations (nucleic acids, proteins, lipids, and so on) in blood serums. And all accuracies of detection and classification reached above 85%. Conclusion This study demonstrated that the SERS based on blood serum test had an enormous potential to carry out the preoperative assessment and prediction of the OSCC patients in different tumor stages and histologic classification.
Collapse
Affiliation(s)
- Lili Xue
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China,
| | - Bing Yan
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Yi Li
- Department of Head and Neck Oncology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Yingyun Tan
- Department of Implant, Stomatological Hospital of Chongqing Medical University, Chongqing, China
| | - Xianyang Luo
- Department of Otolaryngology Head and Neck Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, China
| | - Min Wang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China,
| |
Collapse
|