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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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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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