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Al Jedani S, Lima C, Smith CI, Gunning PJ, Shaw RJ, Barrett SD, Triantafyllou A, Risk JM, Goodacre R, Weightman P. An optical photothermal infrared investigation of lymph nodal metastases of oral squamous cell carcinoma. Sci Rep 2024; 14:16050. [PMID: 38992088 PMCID: PMC11239877 DOI: 10.1038/s41598-024-66977-z] [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: 01/29/2024] [Accepted: 07/05/2024] [Indexed: 07/13/2024] Open
Abstract
In this study, optical photothermal infrared (O-PTIR) spectroscopy combined with machine learning algorithms were used to evaluate 46 tissue cores of surgically resected cervical lymph nodes, some of which harboured oral squamous cell carcinoma nodal metastasis. The ratios obtained between O-PTIR chemical images at 1252 cm-1 and 1285 cm-1 were able to reveal morphological details from tissue samples that are comparable to the information achieved by a pathologist's interpretation of optical microscopy of haematoxylin and eosin (H&E) stained samples. Additionally, when used as input data for a hybrid convolutional neural network (CNN) and random forest (RF) analyses, these yielded sensitivities, specificities and precision of 98.6 ± 0.3%, 92 ± 4% and 94 ± 5%, respectively, and an area under receiver operator characteristic (AUC) of 94 ± 2%. Our findings show the potential of O-PTIR technology as a tool to study cancer on tissue samples.
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Affiliation(s)
- Safaa Al Jedani
- Department of Physics, Oliver Lodge Laboratory, University of Liverpool, Liverpool, L69 7ZE, UK
- Department of Physics, University of Jeddah, Jeddah, Saudi Arabia
| | - Cassio Lima
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Caroline I Smith
- Department of Physics, Oliver Lodge Laboratory, University of Liverpool, Liverpool, L69 7ZE, UK
| | - Philip J Gunning
- Department of Molecular and Clinical Cancer Medicine, Liverpool Head and Neck Centre, University of Liverpool, Liverpool, L7 8TX, UK
| | - Richard J Shaw
- Department of Molecular and Clinical Cancer Medicine, Liverpool Head and Neck Centre, University of Liverpool, Liverpool, L7 8TX, UK
- Head and Neck Surgery, Liverpool University Foundation NHS Trust, Aintree Hospital, Liverpool, L9 7AL, UK
| | - Steve D Barrett
- Department of Physics, Oliver Lodge Laboratory, University of Liverpool, Liverpool, L69 7ZE, UK
| | - Asterios Triantafyllou
- Department of Cellular Pathology, Liverpool Clinical Laboratories, University of Liverpool, Liverpool, L7 8YE, UK
| | - Janet M Risk
- Department of Molecular and Clinical Cancer Medicine, Liverpool Head and Neck Centre, University of Liverpool, Liverpool, L7 8TX, UK
| | - Royston Goodacre
- Centre for Metabolomics Research, Department of Biochemistry, Cell and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Peter Weightman
- Department of Physics, Oliver Lodge Laboratory, University of Liverpool, Liverpool, L69 7ZE, UK.
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Uppal S, Kumar Shrivastava P, Khan A, Sharma A, Kumar Shrivastav A. Machine learning methods in predicting the risk of malignant transformation of oral potentially malignant disorders: A systematic review. Int J Med Inform 2024; 186:105421. [PMID: 38552265 DOI: 10.1016/j.ijmedinf.2024.105421] [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: 11/28/2023] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Oral Potentially Malignant Disorders (OPMDs) refer to a heterogenous group of clinical presentations with heightened rate of malignant transformation. Identification of risk levels in OPMDs is crucial to determine the need for active intervention in high-risk patients and routine follow-up in low-risk ones. Machine learning models has shown tremendous potential in several areas of dentistry that strongly suggest its application to estimate rate of malignant transformation of precancerous lesions. METHODS A comprehensive literature search was performed on Pubmed/MEDLINE, Web of Science, Scopus, Embase, Cochrane Library database to identify articles including machine learning models and algorithms to predict malignant transformation in OPMDs. Relevant bibliographic data, study characteristics, and outcomes were extracted for eligible studies. Quality of the included studies was assessed through the IJMEDI checklist. RESULTS Fifteen articles were found suitable for the review as per the PECOS criteria. Amongst all studies, highest sensitivity (100%) was recorded for U-net architecture, Peaks Random forest model, and Partial least squares discriminant analysis (PLSDA). Highest specificity (100%) was noted for PLSDA. Range of overall accuracy in risk prediction was between 95.4% and 74%. CONCLUSION Machine learning proved to be a viable tool in risk prediction, demonstrating heightened sensitivity, automation, and improved accuracy for predicting transformation of OPMDs. It presents an effective approach for incorporating multiple variables to monitor the progression of OPMDs and predict their malignant potential. However, its sensitivity to dataset characteristics necessitates the optimization of input parameters to maximize the efficiency of the classifiers.
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Affiliation(s)
- Simran Uppal
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | | | - Atiya Khan
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Aditi Sharma
- Faculty of Dentistry, Jamia Millia Islamia, New Delhi, India.
| | - Ayush Kumar Shrivastav
- Computer Science and Engineering, Centre for Development of Advanced Computing, Noida, Uttar Pradesh, India.
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Alajaji SA, Khoury ZH, Jessri M, Sciubba JJ, Sultan AS. An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research. Head Neck Pathol 2024; 18:38. [PMID: 38727841 PMCID: PMC11087425 DOI: 10.1007/s12105-024-01643-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 03/30/2024] [Indexed: 05/13/2024]
Abstract
INTRODUCTION Oral epithelial dysplasia (OED) is a precancerous histopathological finding which is considered the most important prognostic indicator for determining the risk of malignant transformation into oral squamous cell carcinoma (OSCC). The gold standard for diagnosis and grading of OED is through histopathological examination, which is subject to inter- and intra-observer variability, impacting accurate diagnosis and prognosis. The aim of this review article is to examine the current advances in digital pathology for artificial intelligence (AI) applications used for OED diagnosis. MATERIALS AND METHODS We included studies that used AI for diagnosis, grading, or prognosis of OED on histopathology images or intraoral clinical images. Studies utilizing imaging modalities other than routine light microscopy (e.g., scanning electron microscopy), or immunohistochemistry-stained histology slides, or immunofluorescence were excluded from the study. Studies not focusing on oral dysplasia grading and diagnosis, e.g., to discriminate OSCC from normal epithelial tissue were also excluded. RESULTS A total of 24 studies were included in this review. Nineteen studies utilized deep learning (DL) convolutional neural networks for histopathological OED analysis, and 4 used machine learning (ML) models. Studies were summarized by AI method, main study outcomes, predictive value for malignant transformation, strengths, and limitations. CONCLUSION ML/DL studies for OED grading and prediction of malignant transformation are emerging as promising adjunctive tools in the field of digital pathology. These adjunctive objective tools can ultimately aid the pathologist in more accurate diagnosis and prognosis prediction. However, further supportive studies that focus on generalization, explainable decisions, and prognosis prediction are needed.
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Affiliation(s)
- Shahd A Alajaji
- Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, 650 W. Baltimore Street, 7 Floor, Baltimore, MD, 21201, USA
- Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh, Saudi Arabia
- Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, MD, USA
| | - Zaid H Khoury
- Department of Oral Diagnostic Sciences and Research, Meharry Medical College School of Dentistry, Nashville, TN, USA
| | - Maryam Jessri
- Oral Medicine and Pathology Department, School of Dentistry, University of Queensland, Herston, QLD, Australia
- Oral Medicine Department, Metro North Hospital and Health Services, Queensland Health, Brisbane, QLD, Australia
| | - James J Sciubba
- Department of Otolaryngology, Head & Neck Surgery, The Johns Hopkins University, Baltimore, MD, USA
| | - Ahmed S Sultan
- Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, 650 W. Baltimore Street, 7 Floor, Baltimore, MD, 21201, USA.
- Division of Artificial Intelligence Research, University of Maryland School of Dentistry, Baltimore, MD, USA.
- University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center, Baltimore, MD, USA.
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Whitley CA, Ellis BG, Triantafyllou A, Gunning PJ, Gardner P, Barrett SD, Shaw RJ, Smith CI, Weightman P, Risk JM. Prediction of prognosis in oral squamous cell carcinoma using infrared microspectroscopy. Cancer Med 2024; 13:e7094. [PMID: 38468595 DOI: 10.1002/cam4.7094] [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: 10/17/2023] [Revised: 11/14/2023] [Accepted: 02/28/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Estimation of prognosis of oral squamous cell carcinoma (OSCC) is inaccurate prior to surgery, only being effected following subsequent pathological analysis of the primary tumour and excised lymph nodes. Consequently, a proportion of patients are overtreated, with an increase in morbidity, or undertreated, with inadequate margins and risk of recurrence. We hypothesise that it is possible to accurately characterise clinical outcomes from infrared spectra arising from diagnostic biopsies. In this first step, we correlate survival with IR spectra derived from the primary tumour. METHODS Infrared spectra were collected from tumour tissue from 29 patients with OSCC and subject to classification modelling. RESULTS The model had a median AUROC of 0.89 with regard to prognosis, a median specificity of 0.83, and a hazard ratio of 6.29 in univariate Cox proportional hazard modelling. CONCLUSION The data suggest that FTIR spectra may be a useful early biomarker of prognosis in OSCC.
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Affiliation(s)
- Conor A Whitley
- Department of Physics, University of Liverpool, Liverpool, UK
| | - Barnaby G Ellis
- Department of Physics, University of Liverpool, Liverpool, UK
| | - Asterios Triantafyllou
- Department of Pathology, Liverpool Clinical Laboratories, University of Liverpool, Liverpool, UK
| | - Philip J Gunning
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Peter Gardner
- Department of Chemical Engineering, The University of Manchester, Manchester, UK
| | - Steve D Barrett
- Department of Physics, University of Liverpool, Liverpool, UK
| | - Richard J Shaw
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
- Regional Maxillofacial Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | | | - Peter Weightman
- Department of Physics, University of Liverpool, Liverpool, UK
| | - Janet M Risk
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
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Adeoye J, Su YX. Leveraging artificial intelligence for perioperative cancer risk assessment of oral potentially malignant disorders. Int J Surg 2024; 110:1677-1686. [PMID: 38051932 PMCID: PMC10942172 DOI: 10.1097/js9.0000000000000979] [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: 08/12/2023] [Accepted: 11/21/2023] [Indexed: 12/07/2023]
Abstract
Oral potentially malignant disorders (OPMDs) are mucosal conditions with an inherent disposition to develop oral squamous cell carcinoma. Surgical management is the most preferred strategy to prevent malignant transformation in OPMDs, and surgical approaches to treatment include conventional scalpel excision, laser surgery, cryotherapy, and photodynamic therapy. However, in reality, since all patients with OPMDs will not develop oral squamous cell carcinoma in their lifetime, there is a need to stratify patients according to their risk of malignant transformation to streamline surgical intervention for patients with the highest risks. Artificial intelligence (AI) has the potential to integrate disparate factors influencing malignant transformation for robust, precise, and personalized cancer risk stratification of OPMD patients than current methods to determine the need for surgical resection, excision, or re-excision. Therefore, this article overviews existing AI models and tools, presents a clinical implementation pathway, and discusses necessary refinements to aid the clinical application of AI-based platforms for cancer risk stratification of OPMDs in surgical practice.
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Affiliation(s)
| | - Yu-Xiong Su
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, People’s Republic of China
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Ge M, Wang Y, Wu T, Li H, Yang C, Wang Z, Mu N, Chen T, Xu D, Feng H, Yao J. Raman spectroscopic diagnosis of blast-induced traumatic brain injury in rats combined with machine learning. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 304:123419. [PMID: 37738762 DOI: 10.1016/j.saa.2023.123419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/12/2023] [Accepted: 09/14/2023] [Indexed: 09/24/2023]
Abstract
Blast-induced traumatic brain injury (bTBI) is a kind of nervous system disease, which results in a major health and economic problem to society. However, the rapid and label-free detection method with high sensitivity is still in great demand for the diagnosis of bTBI, especially for mild bTBI. In this paper, we report a new strategy for bTBI diagnosis through hippocampus and hypothalamus tissues based on Raman spectroscopy. The spectral characteristics of hippocampus and hypothalamus tissues of experimental bTBI in rats have been investigated for mild and moderate degrees at 3 h, 6 h, 24 h, 48 h, 72 h after blast exposure. The results show that the Raman spectra of mild and moderate bTBIs in 300-1700 cm-1 and 2800-3000 cm-1 regions exhibit significant differences at different time points compared with the control group. The main reason is the content change of proteins and lipids in hippocampus and hypothalamus tissues after bTBI. Moreover, four machine learning algorithms are used to automatically identify mild and moderate bTBIs at different time points (a total of 11 groups). The highest diagnostic accuracies are up to 95.3% and 88.5% based on Raman spectra of hippocampus and hypothalamus tissue, respectively. In addition, the classification performance of linear discriminant analysis classifier has been improved after data fusion. It is suggested that there has great potential as an alternative method for high-sensitive, rapid, label-free, economical diagnosis of bTBI.
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Affiliation(s)
- Meilan Ge
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Yuye Wang
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300072, China.
| | - Tong Wu
- School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
| | - Haibin Li
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Chuanyan Yang
- Department of Neurosurgery and Key Laboratory of Neurotrauma, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - Zelong Wang
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Ning Mu
- Department of Neurosurgery and Key Laboratory of Neurotrauma, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - Tunan Chen
- Department of Neurosurgery and Key Laboratory of Neurotrauma, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - Degang Xu
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Hua Feng
- Department of Neurosurgery and Key Laboratory of Neurotrauma, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - Jianquan Yao
- School of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300072, China
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Ellis BG, Ingham J, Whitley CA, Al Jedani S, Gunning PJ, Gardner P, Shaw RJ, Barrett SD, Triantafyllou A, Risk JM, Smith CI, Weightman P. Metric-based analysis of FTIR data to discriminate tissue types in oral cancer. Analyst 2023; 148:1948-1953. [PMID: 37067098 PMCID: PMC10152457 DOI: 10.1039/d3an00258f] [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: 02/16/2023] [Accepted: 04/08/2023] [Indexed: 04/18/2023]
Abstract
A machine learning algorithm (MLA) has predicted the prognosis of oral potentially malignant lesions and discriminated between lymph node tissue and metastatic oral squamous cell carcinoma (OSCC). The MLA analyses metrics, which are ratios of Fourier transform infrared absorbances, and identifies key wavenumbers that can be associated with molecular biomarkers. The wider efficacy of the MLA is now shown in the more complex primary OSCC tumour setting, where it is able to identify seven types of tissue. Three epithelial and four non-epithelial tissue types were discriminated from each other with sensitivities between 82% and 96% and specificities between 90% and 99%. The wavenumbers involved in the five best discriminating metrics for each tissue type were tightly grouped, indicating that small changes in the spectral profiles of the different tissue types are important. The number of samples used in this study was small, but the information will provide a basis for further, larger investigations.
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Affiliation(s)
- Barnaby G Ellis
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - James Ingham
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Conor A Whitley
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Safaa Al Jedani
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Philip J Gunning
- Liverpool Head and Neck Centre, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, L7 8TX, UK
| | - Peter Gardner
- Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK
| | - Richard J Shaw
- Liverpool Head and Neck Centre, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, L7 8TX, UK
- Head and Neck Surgery, Liverpool University Foundation NHS Trust, Aintree Hospital, Liverpool, L9 7AL, UK
| | - Steve D Barrett
- Department of Physics, University of Liverpool, L69 7ZE, UK.
| | - Asterios Triantafyllou
- Department of Cellular Pathology, Liverpool Clinical Laboratories, University of Liverpool, Liverpool, L7 8YE, UK
| | - Janet M Risk
- Liverpool Head and Neck Centre, Department of Molecular and Clinical Cancer Medicine, University of Liverpool, L7 8TX, UK
| | | | - Peter Weightman
- Department of Physics, University of Liverpool, L69 7ZE, UK.
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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.
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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
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