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McRae MP, Srinivasan Rajsri K, Ross Kerr A, Vigneswaran N, Redding SW, Janal M, Kang SK, Palomo L, Christodoulides NJ, Singh M, Johnston J, McDevitt JT. A cytomics-on-a-chip platform and diagnostic model stratifies risk for oral lichenoid conditions. Oral Surg Oral Med Oral Pathol Oral Radiol 2024; 138:88-98. [PMID: 38755071 DOI: 10.1016/j.oooo.2024.04.003] [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: 11/01/2023] [Revised: 02/21/2024] [Accepted: 04/09/2024] [Indexed: 05/18/2024]
Abstract
OBJECTIVE A small fraction of oral lichenoid conditions (OLC) have potential for malignant transformation. Distinguishing OLCs from other oral potentially malignant disorders (OPMDs) can help prevent unnecessary concern or testing, but accurate identification by nonexpert clinicians is challenging due to overlapping clinical features. In this study, the authors developed a 'cytomics-on-a-chip' tool and integrated predictive model for aiding the identification of OLCs. STUDY DESIGN All study subjects underwent both scalpel biopsy for histopathology and brush cytology. A predictive model and OLC Index comprising clinical, demographic, and cytologic features was generated to discriminate between subjects with lichenoid (OLC+) (N = 94) and nonlichenoid (OLC-) (N = 237) histologic features in a population with OPMDs. RESULTS The OLC Index discriminated OLC+ and OLC- subjects with area under the curve (AUC) of 0.76. Diagnostic accuracy of the OLC Index was not significantly different from expert clinician impressions, with AUC of 0.81 (P = .0704). Percent agreement was comparable across all raters, with 83.4% between expert clinicians and histopathology, 78.3% between OLC Index and expert clinician, and 77.3% between OLC Index and histopathology. CONCLUSIONS The cytomics-on-a-chip tool and integrated diagnostic model have the potential to facilitate both the triage and diagnosis of patients presenting with OPMDs and OLCs.
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Affiliation(s)
- Michael P McRae
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, USA
| | - Kritika Srinivasan Rajsri
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, USA; Department of Pathology, Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY, USA
| | - A Ross Kerr
- Department of Oral and Maxillofacial Pathology, Radiology & Medicine, New York University College of Dentistry, New York, NY, USA
| | - Nadarajah Vigneswaran
- Department of Diagnostic and Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Spencer W Redding
- Department of Comprehensive Dentistry and Mays Cancer Center, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Malvin Janal
- Department of Epidemiology and Health Promotion, New York University College of Dentistry, New York, NY, USA
| | - Stella K Kang
- Departments of Radiology and Population Health, New York University School of Medicine, New York, NY, USA
| | - Leena Palomo
- Ashman Department of Periodontology and Implant Dentistry, New York University College of Dentistry, New York, NY, USA
| | - Nicolaos J Christodoulides
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, USA
| | - Meena Singh
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, USA
| | - Jeffery Johnston
- Research & Data Institute, Delta Dental of Michigan, OH, and IN, USA
| | - John T McDevitt
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, USA; Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, NY, USA.
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Bs A, P A, As SG, A P, J VP. Analysis of differentially expressed genes in dysplastic oral keratinocyte cell line and their role in the development of HNSCC. JOURNAL OF STOMATOLOGY, ORAL AND MAXILLOFACIAL SURGERY 2024:101928. [PMID: 38815724 DOI: 10.1016/j.jormas.2024.101928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/19/2024] [Accepted: 05/27/2024] [Indexed: 06/01/2024]
Abstract
Dysplasia is the presence of abnormal types of cells in a tissue precipitated by over or diminished expression of certain genes. These cells act as a precursor to cancer. Dysplastic oral keratinocyte (DOK) cell lines have an aneuploid complex karyotype. They provide an opportunity to study the action of specific carcinogens on malignant transformations. This study aimed to identify the differentially expressed genes in dysplastic cells and their possible association with head and neck squamous cell carcinoma (HNSCC). These genes can be developed as diagnostic, prognostic, or therapeutic leads.The list of genes related to oral keratinocyte dysplasia and head and neck cancer was accessed from the GEO (Gene Expression Omnibus) database. Gene expression profiling was done between dysplastic oral keratinocytes and normal human oral keratinocytes. Gene expression and Kaplan Meier survival analysis were performed using the UALCAN database to assess the correlations between dysregulated genes identified in dysplastic keratinocytes and primary tumors of HNSCC. The GEO omnibus dataset identified numerous differentially expressed genes of which the top 10 up and downregulated genes in dysplastic oral keratinocytes were curated for further analysis. The expression profile of these genes was assessed using the HNSCC dataset (TCGA, Firehose Legacy). Among all the genes assessed, only one gene, the OLR1 gene encoding oxidized low-density lipoprotein, was found to be overexpressed in both the groups viz., dysplastic keratinocytes and HNSCC cases with a strong correlation with the survival status of patients. There was significant correlation between the gene expression pattern observed in dysplastic keratinocytes and the primary tumor of the HNSCC group, with an exotic gene that was seldom discussed in association with cancer, viz., OLR1. Exploration into other top-ranking differentially expressed genes in dysplastic cases would aid in identifying the candidate gene associated with both phenotypes.
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Affiliation(s)
- Aardra Bs
- Department of Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 77, India
| | - Anitha P
- Centre for Cellular and Molecular Research, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 77, India
| | - Smiline Girija As
- Department of Microbiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 77, India
| | - Paramasivam A
- Centre for Cellular and Molecular Research, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 77, India
| | - Vijayashree Priyadharsini J
- Centre for Cellular and Molecular Research, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 77, India.
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Ghosh SK, Man Y, Fraiwan A, Waters C, McKenzie C, Lu C, Pfau D, Kawsar H, Bhaskaran N, Pandiyan P, Jin G, Briggs F, Zender CC, Rezaee R, Panagakos F, Thuener JE, Wasman J, Tang A, Qari H, Wise-Draper T, McCormick TS, Madabhushi A, Gurkan UA, Weinberg A. Beta-defensin index: A functional biomarker for oral cancer detection. Cell Rep Med 2024; 5:101447. [PMID: 38442713 PMCID: PMC10983043 DOI: 10.1016/j.xcrm.2024.101447] [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: 06/28/2023] [Revised: 11/14/2023] [Accepted: 02/06/2024] [Indexed: 03/07/2024]
Abstract
There is an unmet clinical need for a non-invasive and cost-effective test for oral squamous cell carcinoma (OSCC) that informs clinicians when a biopsy is warranted. Human beta-defensin 3 (hBD-3), an epithelial cell-derived anti-microbial peptide, is pro-tumorigenic and overexpressed in early-stage OSCC compared to hBD-2. We validate this expression dichotomy in carcinoma in situ and OSCC lesions using immunofluorescence microscopy and flow cytometry. The proportion of hBD-3/hBD-2 levels in non-invasively collected lesional cells compared to contralateral normal cells, obtained by ELISA, generates the beta-defensin index (BDI). Proof-of-principle and blinded discovery studies demonstrate that BDI discriminates OSCC from benign lesions. A multi-center validation study shows sensitivity and specificity values of 98.2% (95% confidence interval [CI] 90.3-99.9) and 82.6% (95% CI 68.6-92.2), respectively. A proof-of-principle study shows that BDI is adaptable to a point-of-care assay using microfluidics. We propose that BDI may fulfill a major unmet need in low-socioeconomic countries where pathology services are lacking.
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Affiliation(s)
- Santosh K Ghosh
- Biological Sciences, Case School of Dental Medicine, Cleveland, OH, USA; Case Western Reserve University (CWRU), Cleveland, OH, USA.
| | - Yuncheng Man
- Department of Mechanical and Aerospace Engineering, CWRU, Cleveland, OH, USA
| | - Arwa Fraiwan
- Department of Mechanical and Aerospace Engineering, CWRU, Cleveland, OH, USA
| | | | - Crist McKenzie
- Division of Hematology/Oncology, University of Cincinnati Cancer Center, Cincinnati, OH, USA
| | - Cheng Lu
- Center for Computational Imaging & Personalized Diagnostics, CWRU, Cleveland, OH, USA
| | - David Pfau
- School of Medicine, CWRU, Cleveland, OH, USA
| | - Hameem Kawsar
- Biological Sciences, Case School of Dental Medicine, Cleveland, OH, USA; Case Western Reserve University (CWRU), Cleveland, OH, USA
| | - Natarajan Bhaskaran
- Biological Sciences, Case School of Dental Medicine, Cleveland, OH, USA; Case Western Reserve University (CWRU), Cleveland, OH, USA
| | - Pushpa Pandiyan
- Biological Sciences, Case School of Dental Medicine, Cleveland, OH, USA; Case Western Reserve University (CWRU), Cleveland, OH, USA
| | - Ge Jin
- Biological Sciences, Case School of Dental Medicine, Cleveland, OH, USA; Case Western Reserve University (CWRU), Cleveland, OH, USA
| | - Farren Briggs
- Department of Population and Quantitative Health Sciences, CWRU, Cleveland, OH, USA
| | - Chad C Zender
- Department of Otolaryngology, University Hospital of Cleveland, Cleveland, OH, USA
| | - Rod Rezaee
- Department of Otolaryngology, University Hospital of Cleveland, Cleveland, OH, USA
| | - Fotinos Panagakos
- West Virginia University (WVU) School of Dentistry, Morgantown, WV, USA
| | - Jason E Thuener
- Department of Otolaryngology, University Hospital of Cleveland, Cleveland, OH, USA
| | - Jay Wasman
- Department of Pathology, University Hospital of Cleveland, Cleveland, OH, USA
| | - Alice Tang
- Otolaryngology, Head & Neck Surgery, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Hiba Qari
- Department of Diagnostic Sciences, WVU School of Dentistry, Morgantown, WV, USA
| | - Trisha Wise-Draper
- Division of Hematology/Oncology, University of Cincinnati Cancer Center, Cincinnati, OH, USA
| | | | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Umut A Gurkan
- Department of Mechanical and Aerospace Engineering, CWRU, Cleveland, OH, USA
| | - Aaron Weinberg
- Biological Sciences, Case School of Dental Medicine, Cleveland, OH, USA; Case Western Reserve University (CWRU), Cleveland, OH, USA.
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Srinivasan Rajsri K, K Durab S, A Varghese I, Vigneswaran N, T McDevitt J, Kerr AR. A brief review of cytology in dentistry. Br Dent J 2024; 236:329-336. [PMID: 38388613 DOI: 10.1038/s41415-024-7075-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/13/2023] [Accepted: 12/15/2023] [Indexed: 02/24/2024]
Abstract
Oral cytology is a non-invasive adjunctive diagnostic tool with a number of potential applications in the practice of dentistry. This brief review begins with a history of cytology in medicine and how cytology was initially applied in oral medicine. A description of the different technical aspects of oral cytology is provided, including the collection and processing of oral cytological samples, and the microscopic interpretation and reporting, along with their advantages and limitations. Applications for oral cytology are listed with a focus on the triage of patients presenting with oral potentially malignant disorders and oral mucosal infections. Furthermore, the utility of oral cytology roles across both expert (for example, secondary oral medicine or tertiary head and neck oncology services) and non-expert (for example, primary care general dental practice) clinical settings is explored. A detailed section covers the evidence-base for oral cytology as a diagnostic adjunctive technique in both the early detection and monitoring of patients with oral cancer and oral epithelial dysplasia. The review concludes with an exploration of future directions, including the integration of artificial intelligence for automated analysis and point of care 'smart diagnostics', thereby offering some insight into future opportunities for a wider application of oral cytology in dentistry.
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Affiliation(s)
- Kritika Srinivasan Rajsri
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, 10010, USA
| | - Safia K Durab
- Division of Oral and Maxillofacial Pathology, UT Health, The University of Texas Health Science Centre, Houston, Texas, 77054, USA
| | - Ida A Varghese
- Division of Oral and Maxillofacial Pathology, UT Health, The University of Texas Health Science Centre, Houston, Texas, 77054, USA
| | - Nadarajah Vigneswaran
- Division of Oral and Maxillofacial Pathology, UT Health, The University of Texas Health Science Centre, Houston, Texas, 77054, USA
| | - John T McDevitt
- Department of Molecular Pathobiology, New York University College of Dentistry, New York, 10010, USA
| | - A Ross Kerr
- Department of Oral and Maxillofacial Pathology, Radiology and Medicine, New York University College of Dentistry, New York,, 10010, USA.
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5
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Sunny SP, D. R. R, Hariharan A, Mukhia N, Gurudath S, G. K, Raghavan S, Kolur T, Shetty V, R. VB, Surolia A, T. S, Chandrashekhar P, R. N, Pandya HJ, Pillai V, N. PB, Kuriakose MA, Suresh A. CD44-SNA1 integrated cytopathology for delineation of high grade dysplastic and neoplastic oral lesions. PLoS One 2023; 18:e0291972. [PMID: 37747904 PMCID: PMC10519609 DOI: 10.1371/journal.pone.0291972] [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: 04/21/2023] [Accepted: 09/08/2023] [Indexed: 09/27/2023] Open
Abstract
The high prevalence of oral potentially-malignant disorders exhibits diverse severity and risk of malignant transformation, which mandates a Point-of-Care diagnostic tool. Low patient compliance for biopsies underscores the need for minimally-invasive diagnosis. Oral cytology, an apt method, is not clinically applicable due to a lack of definitive diagnostic criteria and subjective interpretation. The primary objective of this study was to identify and evaluate the efficacy of biomarkers for cytology-based delineation of high-risk oral lesions. A comprehensive systematic review and meta-analysis of biomarkers recognized a panel of markers (n: 10) delineating dysplastic oral lesions. In this observational cross sectional study, immunohistochemical validation (n: 131) identified a four-marker panel, CD44, Cyclin D1, SNA-1, and MAA, with the best sensitivity (>75%; AUC>0.75) in delineating benign, hyperplasia, and mild-dysplasia (Low Risk Lesions; LRL) from moderate-severe dysplasia (High Grade Dysplasia: HGD) along with cancer. Independent validation by cytology (n: 133) showed that expression of SNA-1 and CD44 significantly delineate HGD and cancer with high sensitivity (>83%). Multiplex validation in another cohort (n: 138), integrated with a machine learning model incorporating clinical parameters, further improved the sensitivity and specificity (>88%). Additionally, image automation with SNA-1 profiled data set also provided a high sensitivity (sensitivity: 86%). In the present study, cytology with a two-marker panel, detecting aberrant glycosylation and a glycoprotein, provided efficient risk stratification of oral lesions. Our study indicated that use of a two-biomarker panel (CD44/SNA-1) integrated with clinical parameters or SNA-1 with automated image analysis (Sensitivity >85%) or multiplexed two-marker panel analysis (Sensitivity: >90%) provided efficient risk stratification of oral lesions, indicating the significance of biomarker-integrated cytopathology in the development of a Point-of-care assay.
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Affiliation(s)
- Sumsum P. Sunny
- Department of Head and Neck Oncology, Mazumdar Shaw Medical Center, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, Bangalore, India
- Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Ravindra D. R.
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, Bangalore, India
| | - Aditi Hariharan
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, Bangalore, India
| | - Nirza Mukhia
- Department of Oral Medicine and Radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Shubha Gurudath
- Department of Oral Medicine and Radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Keerthi G.
- Department of Oral Medicine and Radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Subhashini Raghavan
- Department of Oral Medicine and Radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Trupti Kolur
- Department of Head and Neck Oncology, Mazumdar Shaw Medical Center, Bangalore, India
| | - Vivek Shetty
- Department of Head and Neck Oncology, Mazumdar Shaw Medical Center, Bangalore, India
| | - Vidya Bushan R.
- Department of Head and Neck Oncology, Mazumdar Shaw Medical Center, Bangalore, India
| | - Avadhesha Surolia
- Department of Molecular Biophysics, Indian Institute of Science, Bangalore, India
| | - Satyajit T.
- Department of Oral and Maxillofacial Pathology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Pavithra Chandrashekhar
- Department of Oral and Maxillofacial Pathology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Nisheena R.
- Department of Head and Neck Oncology, Mazumdar Shaw Medical Center, Bangalore, India
| | - Hardik J. Pandya
- Department of Electronic Systems Engineering, Division of EECS, Indian Institute of Science, Bangalore, India
| | - Vijay Pillai
- Department of Head and Neck Oncology, Mazumdar Shaw Medical Center, Bangalore, India
| | - Praveen Birur N.
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, Bangalore, India
- Department of Oral Medicine and Radiology, KLE Society’s Institute of Dental Sciences, Bangalore, India
| | - Moni A. Kuriakose
- Department of Head and Neck Oncology, Mazumdar Shaw Medical Center, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, Bangalore, India
| | - Amritha Suresh
- Department of Head and Neck Oncology, Mazumdar Shaw Medical Center, Bangalore, India
- Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, Bangalore, India
- Manipal Academy of Higher Education, Manipal, Karnataka, India
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Srinivasan Rajsri K, McRae MP, Christodoulides NJ, Dapkins I, Simmons GW, Matz H, Dooley H, Fenyö D, McDevitt JT. Simultaneous Quantitative SARS-CoV-2 Antigen and Host Antibody Detection and Pre-Screening Strategy at the Point of Care. Bioengineering (Basel) 2023; 10:670. [PMID: 37370601 PMCID: PMC10295356 DOI: 10.3390/bioengineering10060670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/16/2023] [Accepted: 05/25/2023] [Indexed: 06/29/2023] Open
Abstract
As COVID-19 pandemic public health measures are easing globally, the emergence of new SARS-CoV-2 strains continue to present high risk for vulnerable populations. The antibody-mediated protection acquired from vaccination and/or infection is seen to wane over time and the immunocompromised populations can no longer expect benefit from monoclonal antibody prophylaxis. Hence, there is a need to monitor new variants and its effect on vaccine performance. In this context, surveillance of new SARS-CoV-2 infections and serology testing are gaining consensus for use as screening methods, especially for at-risk groups. Here, we described an improved COVID-19 screening strategy, comprising predictive algorithms and concurrent, rapid, accurate, and quantitative SARS-CoV-2 antigen and host antibody testing strategy, at point of care (POC). We conducted a retrospective analysis of 2553 pre- and asymptomatic patients who were tested for SARS-CoV-2 by RT-PCR. The pre-screening model had an AUC (CI) of 0.76 (0.73-0.78). Despite being the default method for screening, body temperature had lower AUC (0.52 [0.49-0.55]) compared to case incidence rate (0.65 [0.62-0.68]). POC assays for SARS-CoV-2 nucleocapsid protein (NP) and spike (S) receptor binding domain (RBD) IgG antibody showed promising preliminary results, demonstrating a convenient, rapid (<20 min), quantitative, and sensitive (ng/mL) antigen/antibody assay. This integrated pre-screening model and simultaneous antigen/antibody approach may significantly improve accuracy of COVID-19 infection and host immunity screening, helping address unmet needs for monitoring vaccine effectiveness and severe disease surveillance.
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Affiliation(s)
- Kritika Srinivasan Rajsri
- Division of Biomaterials, Department of Molecular Pathobiology, New York University School of Dentistry, New York, NY 10010, USA; (K.S.R.); (M.P.M.); (N.J.C.); (G.W.S.)
- Department of Pathology, Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY 10010, USA
| | - Michael P. McRae
- Division of Biomaterials, Department of Molecular Pathobiology, New York University School of Dentistry, New York, NY 10010, USA; (K.S.R.); (M.P.M.); (N.J.C.); (G.W.S.)
| | - Nicolaos J. Christodoulides
- Division of Biomaterials, Department of Molecular Pathobiology, New York University School of Dentistry, New York, NY 10010, USA; (K.S.R.); (M.P.M.); (N.J.C.); (G.W.S.)
| | - Isaac Dapkins
- Departments of Population Health and Medicine, New York University School of Medicine, New York, NY 10010, USA;
| | - Glennon W. Simmons
- Division of Biomaterials, Department of Molecular Pathobiology, New York University School of Dentistry, New York, NY 10010, USA; (K.S.R.); (M.P.M.); (N.J.C.); (G.W.S.)
| | - Hanover Matz
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (H.M.); (H.D.)
| | - Helen Dooley
- Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (H.M.); (H.D.)
| | - David Fenyö
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY 10010, USA;
| | - John T. McDevitt
- Division of Biomaterials, Department of Molecular Pathobiology, New York University School of Dentistry, New York, NY 10010, USA; (K.S.R.); (M.P.M.); (N.J.C.); (G.W.S.)
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Dixit S, Kumar A, Srinivasan K. A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions. Diagnostics (Basel) 2023; 13:diagnostics13071353. [PMID: 37046571 PMCID: PMC10093759 DOI: 10.3390/diagnostics13071353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 03/25/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people’s lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI’s drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.
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Affiliation(s)
- Shriniket Dixit
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Anant Kumar
- School of Bioscience and Technology, Vellore Institute of Technology, Vellore 632014, India
| | - Kathiravan Srinivasan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India
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McRae MP, Rajsri KS, Alcorn TM, McDevitt JT. Smart Diagnostics: Combining Artificial Intelligence and In Vitro Diagnostics. SENSORS (BASEL, SWITZERLAND) 2022; 22:6355. [PMID: 36080827 PMCID: PMC9459970 DOI: 10.3390/s22176355] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/12/2022] [Accepted: 08/19/2022] [Indexed: 06/15/2023]
Abstract
We are beginning a new era of Smart Diagnostics-integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis digitizes biology by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive Score reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care.
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Affiliation(s)
- Michael P. McRae
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, 433 First Ave. Rm 822, New York, NY 10010, USA
| | - Kritika S. Rajsri
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, 433 First Ave. Rm 822, New York, NY 10010, USA
- Department of Pathology, Vilcek Institute, New York University School of Medicine, 160 E 34th St, New York, NY 10016, USA
| | - Timothy M. Alcorn
- Latham BioPharm Group, 6810 Deerpath Rd Suite 405, Elkridge, MD 21075, USA
| | - John T. McDevitt
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, 433 First Ave. Rm 822, New York, NY 10010, USA
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9
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Rajsri KS, McRae MP, Simmons GW, Christodoulides NJ, Matz H, Dooley H, Koide A, Koide S, McDevitt JT. A Rapid and Sensitive Microfluidics-Based Tool for Seroprevalence Immunity Assessment of COVID-19 and Vaccination-Induced Humoral Antibody Response at the Point of Care. BIOSENSORS 2022; 12:621. [PMID: 36005017 PMCID: PMC9405565 DOI: 10.3390/bios12080621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/05/2022] [Accepted: 08/06/2022] [Indexed: 12/14/2022]
Abstract
As of 8 August 2022, SARS-CoV-2, the causative agent of COVID-19, has infected over 585 million people and resulted in more than 6.42 million deaths worldwide. While approved SARS-CoV-2 spike (S) protein-based vaccines induce robust seroconversion in most individuals, dramatically reducing disease severity and the risk of hospitalization, poorer responses are observed in aged, immunocompromised individuals and patients with certain pre-existing health conditions. Further, it is difficult to predict the protection conferred through vaccination or previous infection against new viral variants of concern (VoC) as they emerge. In this context, a rapid quantitative point-of-care (POC) serological assay able to quantify circulating anti-SARS-CoV-2 antibodies would allow clinicians to make informed decisions on the timing of booster shots, permit researchers to measure the level of cross-reactive antibody against new VoC in a previously immunized and/or infected individual, and help assess appropriate convalescent plasma donors, among other applications. Utilizing a lab-on-a-chip ecosystem, we present proof of concept, optimization, and validation of a POC strategy to quantitate COVID-19 humoral protection. This platform covers the entire diagnostic timeline of the disease, seroconversion, and vaccination response spanning multiple doses of immunization in a single POC test. Our results demonstrate that this platform is rapid (~15 min) and quantitative for SARS-CoV-2-specific IgG detection.
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Affiliation(s)
- Kritika Srinivasan Rajsri
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY 10010, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University School of Medicine, New York, NY 10016, USA
| | - Michael P. McRae
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY 10010, USA
| | - Glennon W. Simmons
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY 10010, USA
| | - Nicolaos J. Christodoulides
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY 10010, USA
| | - Hanover Matz
- Department of Microbiology and Immunology, Institute of Marine and Environmental Technology, University of Maryland School of Medicine, Baltimore, MD 21202, USA
| | - Helen Dooley
- Department of Microbiology and Immunology, Institute of Marine and Environmental Technology, University of Maryland School of Medicine, Baltimore, MD 21202, USA
| | - Akiko Koide
- Department of Medicine, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Shohei Koide
- Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - John T. McDevitt
- Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY 10010, USA
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, Brooklyn, NY 11201, USA
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10
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Machine-Learning Applications in Oral Cancer: A Systematic Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115715] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice.
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11
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Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review. Cancers (Basel) 2021; 13:cancers13184600. [PMID: 34572831 PMCID: PMC8467703 DOI: 10.3390/cancers13184600] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/29/2021] [Accepted: 09/09/2021] [Indexed: 01/06/2023] Open
Abstract
The early diagnosis of cancer can facilitate subsequent clinical patient management. Artificial intelligence (AI) has been found to be promising for improving the diagnostic process. The aim of the present study is to increase the evidence on the application of AI to the early diagnosis of oral cancer through a scoping review. A search was performed in the PubMed, Web of Science, Embase and Google Scholar databases during the period from January 2000 to December 2020, referring to the early non-invasive diagnosis of oral cancer based on AI applied to screening. Only accessible full-text articles were considered. Thirty-six studies were included on the early detection of oral cancer based on images (photographs (optical imaging and enhancement technology) and cytology) with the application of AI models. These studies were characterized by their heterogeneous nature. Each publication involved a different algorithm with potential training data bias and few comparative data for AI interpretation. Artificial intelligence may play an important role in precisely predicting the development of oral cancer, though several methodological issues need to be addressed in parallel to the advances in AI techniques, in order to allow large-scale transfer of the latter to population-based detection protocols.
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12
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Adeoye J, Tan JY, Choi SW, Thomson P. Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review. Int J Med Inform 2021; 154:104557. [PMID: 34455119 DOI: 10.1016/j.ijmedinf.2021.104557] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES Machine learning platforms are now being introduced into modern oncological practice for classification and prediction of patient outcomes. To determine the current status of the application of these learning models as adjunctive decision-making tools in oral cavity cancer management, this systematic review aims to summarize the accuracy of machine-learning based models for disease outcomes. METHODS Electronic databases including PubMed, Scopus, EMBASE, Cochrane Library, LILACS, SciELO, PsychINFO, and Web of Science were searched up until December 21, 2020. Pertinent articles detailing the development and accuracy of machine learning prediction models for oral cavity cancer outcomes were selected in a two-stage process. Quality assessment was conducted using the Quality in Prognosis Studies (QUIPS) tool and results of base studies were qualitatively synthesized by all authors. Outcomes of interest were malignant transformation of precancer lesions, cervical lymph node metastasis, as well as treatment response, and prognosis of oral cavity cancer. RESULTS Twenty-seven articles out of 950 citations identified from electronic and manual searching were included in this study. Five studies had low bias concerns on the QUIPS tool. Prediction of malignant transformation, cervical lymph node metastasis, treatment response, and prognosis were reported in three, six, eight, and eleven articles respectively. Accuracy of these learning models on the internal or external validation sets ranged from 0.85 to 0.97 for malignant transformation prediction, 0.78-0.91 for cervical lymph node metastasis prediction, 0.64-1.00 for treatment response prediction, and 0.71-0.99 for prognosis prediction. In general, most trained algorithms predicting these outcomes performed better than alternate methods of prediction. We also found that models including molecular markers in training data had better accuracy estimates for malignant transformation, treatment response, and prognosis prediction. CONCLUSION Machine learning algorithms have a satisfactory to excellent accuracy for predicting three of four oral cavity cancer outcomes i.e., malignant transformation, nodal metastasis, and prognosis. However, considering the training approach of many available classifiers, these models may not be streamlined enough for clinical application currently.
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Affiliation(s)
- John Adeoye
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Jia Yan Tan
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region.
| | - Siu-Wai Choi
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region.
| | - Peter Thomson
- Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region
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13
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Walsh T, Macey R, Kerr AR, Lingen MW, Ogden GR, Warnakulasuriya S. Diagnostic tests for oral cancer and potentially malignant disorders in patients presenting with clinically evident lesions. Cochrane Database Syst Rev 2021; 7:CD010276. [PMID: 34282854 PMCID: PMC8407012 DOI: 10.1002/14651858.cd010276.pub3] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Squamous cell carcinoma is the most common form of malignancy of the oral cavity, and is often proceeded by oral potentially malignant disorders (OPMD). Early detection of oral cavity squamous cell carcinoma (oral cancer) can improve survival rates. The current diagnostic standard of surgical biopsy with histology is painful for patients and involves a delay in order to process the tissue and render a histological diagnosis; other diagnostic tests are available that are less invasive and some are able to provide immediate results. This is an update of a Cochrane Review first published in 2015. OBJECTIVES Primary objective: to estimate the diagnostic accuracy of index tests for the detection of oral cancer and OPMD, in people presenting with clinically evident suspicious and innocuous lesions. SECONDARY OBJECTIVE to estimate the relative accuracy of the different index tests. SEARCH METHODS Cochrane Oral Health's Information Specialist searched the following databases: MEDLINE Ovid (1946 to 20 October 2020), and Embase Ovid (1980 to 20 October 2020). The US National Institutes of Health Ongoing Trials Register (ClinicalTrials.gov) and the World Health Organization International Clinical Trials Registry Platform were also searched for ongoing trials to 20 October 2020. No restrictions were placed on the language or date of publication when searching the electronic databases. We conducted citation searches, and screened reference lists of included studies for additional references. SELECTION CRITERIA We selected studies that reported the diagnostic test accuracy of the following index tests when used as an adjunct to conventional oral examination in detecting OPMD or oral cavity squamous cell carcinoma: vital staining (a dye to stain oral mucosa tissues), oral cytology, light-based detection and oral spectroscopy, blood or saliva analysis (which test for the presence of biomarkers in blood or saliva). DATA COLLECTION AND ANALYSIS Two review authors independently screened titles and abstracts for relevance. Eligibility, data extraction and quality assessment were carried out by at least two authors, independently and in duplicate. Studies were assessed for methodological quality using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis was used to combine the results of studies for each index test using the bivariate approach to estimate the expected values of sensitivity and specificity. MAIN RESULTS This update included 63 studies (79 datasets) published between 1980 and 2020 evaluating 7942 lesions for the quantitative meta-analysis. These studies evaluated the diagnostic accuracy of conventional oral examination with: vital staining (22 datasets), oral cytology (24 datasets), light-based detection or oral spectroscopy (24 datasets). Nine datasets assessed two combined index tests. There were no eligible diagnostic accuracy studies evaluating blood or salivary sample analysis. Two studies were classed as being at low risk of bias across all domains, and 33 studies were at low concern for applicability across the three domains, where patient selection, the index test, and the reference standard used were generalisable across the population attending secondary care. The summary estimates obtained from the meta-analysis were: - vital staining: sensitivity 0.86 (95% confidence interval (CI) 0.79 to 0.90) specificity 0.68 (95% CI 0.58 to 0.77), 20 studies, sensitivity low-certainty evidence, specificity very low-certainty evidence; - oral cytology: sensitivity 0.90 (95% CI 0.82 to 0.94) specificity 0.94 (95% CI 0.88 to 0.97), 20 studies, sensitivity moderate-certainty evidence, specificity moderate-certainty evidence; - light-based: sensitivity 0.87 (95% CI 0.78 to 0.93) specificity 0.50 (95% CI 0.32 to 0.68), 23 studies, sensitivity low-certainty evidence, specificity very low-certainty evidence; and - combined tests: sensitivity 0.78 (95% CI 0.45 to 0.94) specificity 0.71 (95% CI 0.53 to 0.84), 9 studies, sensitivity very low-certainty evidence, specificity very low-certainty evidence. AUTHORS' CONCLUSIONS At present none of the adjunctive tests can be recommended as a replacement for the currently used standard of a surgical biopsy and histological assessment. Given the relatively high values of the summary estimates of sensitivity and specificity for oral cytology, this would appear to offer the most potential. Combined adjunctive tests involving cytology warrant further investigation. Potentially eligible studies of blood and salivary biomarkers were excluded from the review as they were of a case-control design and therefore ineligible. In the absence of substantial improvement in the tests evaluated in this updated review, further research into biomarkers may be warranted.
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Affiliation(s)
- Tanya Walsh
- Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Richard Macey
- Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Alexander R Kerr
- Department of Oral and Maxillofacial Pathology, Radiology and Medicine, New York University College of Dentistry, New York, USA
| | - Mark W Lingen
- Pritzker School of Medicine, Division of Biological Sciences, Department of Pathology, University of Chicago, Chicago, Illinois, USA
| | - Graham R Ogden
- Division of Oral and Maxillofacial Clinical Sciences, School of Dentistry, University of Dundee, Dundee, UK
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14
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Li C, Zhou Y, Deng Y, Shen X, Shi L, Liu W. Development and validation of a risk model for noninvasive detection of cancer in oral potentially malignant disorders using DNA image cytometry. Cancer Biol Med 2021; 18:j.issn.2095-3941.2020.0531. [PMID: 34018388 PMCID: PMC8330543 DOI: 10.20892/j.issn.2095-3941.2020.0531] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 12/15/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE To elucidate whether DNA aneuploidy was an independent discriminator for carcinoma within oral potentially malignant disorders (OPMDs), and further establish and validate a risk model based on DNA aneuploidy for the detection of oral cancer. METHODS A total of 810 consecutive patients with OPMD were prospectively enrolled from March 2013 to December 2018, and divided into a training set (n = 608) and a test set (n = 202). Brushing and biopsy samples from each patient were processed by DNA-DNA image cytometry and histopathological examination, respectively. RESULTS DNA aneuploidy of an outside DNA index ≥ 3.5 in OPMD was an independent marker strongly associated with malignant risk [adjusted odds ratio: 13.04; 95% confidence interval (CI): 5.46-31.14]. In the training and test sets, the area under the curve (AUC) was 0.87 (95% CI: 0.82-0.91) and 0.77 (95% CI: 0.57-0.97), respectively, for detecting carcinoma in OPMD patients. The independent risk factors of lateral/ventral tongue and non-homogenous type combined with a risk model built with a multivariate logistic regression revealed a more favorable diagnostic efficacy associated with the training set (AUC: 0.93; 95% CI: 0.91-0.96) and test set (AUC: 0.94; 95% CI: 0.90-0.98). The sensitivity and specificity of carcinoma detection within OPMD was improved to 100% and 88.1%, respectively. CONCLUSIONS This large-scale diagnostic study established a risk model based on DNA aneuploidy that consisted of a noninvasive strategy with lateral/ventral tongue and non-homogenous features. The results showed favorable diagnostic efficacy for detecting carcinoma within OPMD, irrespective of the clinical and pathological diagnoses of OPMD. Multicenter validation and longitudinal studies are warranted to evaluate community practices and clinical applications.
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Affiliation(s)
- Chenxi Li
- Department of Oral Mucosal Diseases, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai 200011, China
| | - Yongmei Zhou
- Department of Oral Mucosal Diseases, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai 200011, China
| | - Yiwen Deng
- Department of Oral Mucosal Diseases, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai 200011, China
| | - Xuemin Shen
- Department of Oral Mucosal Diseases, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai 200011, China
| | - Linjun Shi
- Department of Oral Mucosal Diseases, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai 200011, China
| | - Wei Liu
- Department of Oral and Maxillofacial-Head and Neck Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
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15
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Kaur J, Srivastava R, Borse V. Recent advances in point-of-care diagnostics for oral cancer. Biosens Bioelectron 2021; 178:112995. [PMID: 33515983 DOI: 10.1016/j.bios.2021.112995] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 01/07/2021] [Accepted: 01/10/2021] [Indexed: 12/24/2022]
Abstract
Early-stage diagnosis is a crucial step in reducing the mortality rate in oral cancer cases. Point-of-care (POC) devices for oral cancer diagnosis hold great future potential in improving the survival rates as well as the quality of life of oral cancer patients. The conventional oral examination followed by needle biopsy and histopathological analysis have limited diagnostic accuracy. Besides, it involves patient discomfort and is not feasible in resource-limited settings. POC detection of biomarkers and diagnostic adjuncts has emerged as non- or minimally invasive tools for the diagnosis of oral cancer at an early stage. Various biosensors have been developed for the rapid detection of oral cancer biomarkers at the point-of-care. Several optical imaging methods have also been employed as adjuncts to detect alterations in oral tissue indicative of malignancy. This review summarizes the different POC platforms developed for the detection of oral cancer biomarkers, along with various POC imaging and cytological adjuncts that aid in oral cancer diagnosis, especially in low resource settings. Various immunosensors and nucleic acid biosensors developed to detect oral cancer biomarkers are summarized with examples. The different imaging methods used to detect oral tissue malignancy are also discussed herein. Additionally, the currently available commercial devices used as adjuncts in the POC detection of oral cancer are emphasized along with their characteristics. Finally, we discuss the limitations and challenges that persist in translating the developed POC techniques in the clinical settings for oral cancer diagnosis, along with future perspectives.
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Affiliation(s)
- Jasmeen Kaur
- NanoBios Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Rohit Srivastava
- NanoBios Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India
| | - Vivek Borse
- NanoBioSens Laboratory, Centre for Nanotechnology, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India.
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16
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Alabi RO, Youssef O, Pirinen M, Elmusrati M, Mäkitie AA, Leivo I, Almangush A. Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future-A systematic review. Artif Intell Med 2021; 115:102060. [PMID: 34001326 DOI: 10.1016/j.artmed.2021.102060] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 01/27/2021] [Accepted: 03/23/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. OBJECTIVES This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. DATA SOURCES We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. ELIGIBILITY CRITERIA Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. DATA EXTRACTION Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. RESULTS A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. CONCLUSION Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.
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Affiliation(s)
- Rasheed Omobolaji Alabi
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland.
| | - Omar Youssef
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Mohammed Elmusrati
- Department of Industrial Digitalization, School of Technology and Innovations, University of Vaasa, Vaasa, Finland
| | - Antti A Mäkitie
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Department of Otorhinolaryngology - Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Ilmo Leivo
- University of Turku, Institute of Biomedicine, Pathology, Turku, Finland
| | - Alhadi Almangush
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; University of Turku, Institute of Biomedicine, Pathology, Turku, Finland; Faculty of Dentistry, Misurata University, Misurata, Libya
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17
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Pritzker KPH, Darling MR, Hwang JTK, Mock D. Oral Potentially Malignant Disorders (OPMD): What is the clinical utility of dysplasia grade? Expert Rev Mol Diagn 2021; 21:289-298. [PMID: 33682567 DOI: 10.1080/14737159.2021.1898949] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Oral epithelial dysplasia is considered a potential histologic precursor of subsequent squamous cell cancer. As standard clinical practice, pathologists grade dysplasia to assess risk for progression to malignancy. Except for the most advanced grade, severe dysplasia, dysplasia grading has failed to correlate well with the risk to develop invasive cancer. The questions of what process dysplasia grading best represents and what clinical utility dysplasia grading may have are explored. AREAS COVERED This narrative review is based on PubMed search with emphasis on papers since 2010. Epithelial dysplasia as a precursor lesion of cancer and dysplasia grading as a risk assessment tool for progression to cancer are discussed. The close clinical association of dysplasia with known carcinogens, alcohol, and tobacco products is presented. EXPERT OPINION Oral epithelial dysplasia is often, associated with prolonged exposure to tobacco and alcohol products. With reduction of carcinogen exposure, dysplasia is known to regress in some cases. It is proposed that histologic dysplasia grade together with macroscopic images of dysplastic clinical lesions be used as an educational tool to incentivize patients to reduce their known carcinogen exposure. This strategy has the potential to reduce lesion progression thereby reducing the disease burden of oral cancer.
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Affiliation(s)
- Kenneth P H Pritzker
- Professor Emeritus, Laboratory Medicine and Pathobiology; Surgery University of Toronto, Toronto, Ontario, Canada.,Proteocyte Diagnostics Inc., Toronto, Canada.,Department of Pathology and Laboratory Medicine, Pathology & Laboratory Medicine Mount Sinai Hospital, Toronto, Canada
| | - Mark R Darling
- Professor, Department of Pathology and Laboratory Medicine, Schulich Faculty of Medicine and Dentistry, Western University London Ontario, Canada
| | | | - David Mock
- Department of Pathology and Laboratory Medicine, Pathology & Laboratory Medicine Mount Sinai Hospital, Toronto, Canada.,Professor, Pathology/Oral Medicine & Dean Emeritus, Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada.,Department of Dentistry, Dentistry Mount Sinai Hospital, Toronto, Canada
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18
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Lollie TK, Krane JF. Applications of Computational Pathology in Head and Neck Cytopathology. Acta Cytol 2021; 65:330-334. [PMID: 33621977 DOI: 10.1159/000513286] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 11/04/2020] [Indexed: 11/19/2022]
Abstract
BACKGROUND The application of computational technology to head and neck cytology material has been explored experimentally in several areas with a variety of potential applications. SUMMARY This review summarizes the application of these techniques to the diagnosis of thyroid, salivary gland, and other head and neck fine-needle aspiration specimens. Current limitations and potential future applications in diagnosis are discussed along with the possibilities for therapeutic applications of computational methodology. Key Message: Particularly promising applications include resolving diagnostic uncertainty in indeterminate thyroid aspirates and assessing the tumor microenvironment in response to immunotherapy for squamous cell carcinoma.
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Affiliation(s)
- Trang K Lollie
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Jeffrey F Krane
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA,
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19
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McRae MP, Kerr AR, Janal MN, Thornhill MH, Redding SW, Vigneswaran N, Kang SK, Niederman R, Christodoulides NJ, Trochesset DA, Murdoch C, Dapkins I, Bouquot J, Modak SS, Simmons GW, McDevitt JT. Nuclear F-actin Cytology in Oral Epithelial Dysplasia and Oral Squamous Cell Carcinoma. J Dent Res 2020; 100:479-486. [PMID: 33179547 DOI: 10.1177/0022034520973162] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Oral cavity cancer has a low 5-y survival rate, but outcomes improve when the disease is detected early. Cytology is a less invasive method to assess oral potentially malignant disorders relative to the gold-standard scalpel biopsy and histopathology. In this report, we aimed to determine the utility of cytological signatures, including nuclear F-actin cell phenotypes, for classifying the entire spectrum of oral epithelial dysplasia and oral squamous cell carcinoma. We enrolled subjects with oral potentially malignant disorders, subjects with previously diagnosed malignant lesions, and healthy volunteers without lesions and obtained brush cytology specimens and matched scalpel biopsies from 486 subjects. Histopathological assessment of the scalpel biopsy specimens classified lesions into 6 categories. Brush cytology specimens were analyzed by machine learning classifiers trained to identify relevant cytological features. Multimodal diagnostic models were developed using cytology results, lesion characteristics, and risk factors. Squamous cells with nuclear F-actin staining were associated with early disease (i.e., lower proportions in benign lesions than in more severe lesions), whereas small round parabasal-like cells and leukocytes were associated with late disease (i.e., higher proportions in severe dysplasia and carcinoma than in less severe lesions). Lesions with the impression of oral lichen planus were unlikely to be either dysplastic or malignant. Cytological features substantially improved upon lesion appearance and risk factors in predicting squamous cell carcinoma. Diagnostic models accurately discriminated early and late disease with AUCs (95% CI) of 0.82 (0.77 to 0.87) and 0.93 (0.88 to 0.97), respectively. The cytological features identified here have the potential to improve screening and surveillance of the entire spectrum of oral potentially malignant disorders in multiple care settings.
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Affiliation(s)
- M P McRae
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentsitry, New York, NY, USA
| | - A R Kerr
- Department of Oral and Maxillofacial Pathology, Radiology & Medicine, New York University College of Dentistry, New York, NY, USA
| | - M N Janal
- Department of Epidemiology and Health Promotion, New York University College of Dentistry, New York, NY, USA
| | - M H Thornhill
- Department of Oral & Maxillofacial Medicine, Surgery and Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK
| | - S W Redding
- Department of Comprehensive Dentistry and Mays Cancer Center, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - N Vigneswaran
- Department of Diagnostic and Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - S K Kang
- Departments of Radiology, Population Health New York University School of Medicine, New York, NY, USA
| | - R Niederman
- Department of Epidemiology and Health Promotion, New York University, New York, NY, USA
| | - N J Christodoulides
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentsitry, New York, NY, USA
| | - D A Trochesset
- Department of Oral and Maxillofacial Pathology, Radiology & Medicine, New York University College of Dentistry, New York, NY, USA
| | - C Murdoch
- Department of Oral & Maxillofacial Medicine, Surgery and Pathology, School of Clinical Dentistry, University of Sheffield, Sheffield, UK
| | - I Dapkins
- Departments of Population Health and Medicine, New York University School of Medicine, New York, NY, USA
| | - J Bouquot
- Department of Diagnostic and Biomedical Sciences, The University of Texas School of Dentistry at Houston, Houston, TX, USA
| | - S S Modak
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentsitry, New York, NY, USA
| | - G W Simmons
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentsitry, New York, NY, USA
| | - J T McDevitt
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentsitry, New York, NY, USA
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20
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Yang X, Chen F, Shen X, Zhang C, Liu W. Profiling risk factors of micro-invasive carcinoma within oral potentially malignant disorders: a cross-sectional study. Clin Oral Investig 2020; 24:3715-3720. [PMID: 32902677 DOI: 10.1007/s00784-020-03568-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 09/02/2020] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To investigate the clinicopathological profile and risk factors of micro-invasive carcinoma within oral potentially malignant disorders (OPMD). METHODS Micro-invasive carcinomas were identified in a large prospective series of OPMD patients (n = 810) from eastern China. Logistic regression was applied to evaluate odds ratios (OR) with 95% confidence interval (CI) for indicative of malignant risk in general OPMD. RESULTS Leukoplakia (41.4%), lichen planus (28.0%), and lichenoid lesion (23.7%) were the most 3 clinical subtypes of OPMD. A total of 62 (7.7%) micro-invasive carcinomas within OPMD were identified, and 96.8% of micro-invasive carcinoma was found within leukoplakia and erythroplakia. Multivariate regression analysis revealed that the risk of malignant change within OPMD located on lateral/ventral tongue (OR, 15.1; 95% CI, 1.85-122.8; P = 0.011) was higher than other sites. The risk of malignant change within non-homogenous type (OR, 103.3; 95% CI, 13.39-796.7; P < 0.001) was strikingly higher than other subtypes of OPMD, respectively. Intriguingly, the risk of micro-invasive carcinoma diagnosed in current smoker (OR, 3.96; 95% CI, 1.31-12.02; P = 0.015) was higher than non-smoker. CONCLUSION This large-scale cross-sectional study elucidated the clinical factors and risk assessment of micro-invasive carcinoma within OPMD. CLINICAL RELEVANCE Non-homogenous lesions located on lateral/ventral tongue might be monitored at closer intervals, and the need for rigorous management to detect malignant changes.
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Affiliation(s)
- Xi Yang
- Department of Oral and Maxillofacial-Head and Neck Oncology, Fengcheng Hospital, Fengxian District, Shanghai, China.,Department of Oral and Maxillofacial-Head and Neck Oncology, College of Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fubo Chen
- Department of Stomatology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xuemin Shen
- Department of Oral Mucosal Diseases, College of Stomatology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chenping Zhang
- Department of Oral and Maxillofacial-Head and Neck Oncology, College of Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Liu
- Department of Oral and Maxillofacial-Head and Neck Oncology, College of Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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21
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McRae MP, Simmons GW, Christodoulides NJ, Lu Z, Kang SK, Fenyo D, Alcorn T, Dapkins IP, Sharif I, Vurmaz D, Modak SS, Srinivasan K, Warhadpande S, Shrivastav R, McDevitt JT. Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with COVID-19. LAB ON A CHIP 2020; 20:2075-2085. [PMID: 32490853 PMCID: PMC7360344 DOI: 10.1039/d0lc00373e] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). Although this analysis represents patients with cardiac comorbidities (hypertension), the inclusion of biomarkers from other pathophysiologies implicated in COVID-19 (e.g., D-dimer for thrombotic events, CRP for infection or inflammation, and PCT for bacterial co-infection and sepsis) may improve future predictions for a more general population. These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.
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Affiliation(s)
- Michael P McRae
- Department of Biomaterials, Bioengineering Institute, New York University, 433 First Avenue, Room 820, New York, NY 10010-4086, USA.
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22
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McRae MP, Simmons GW, Christodoulides NJ, Lu Z, Kang SK, Fenyo D, Alcorn T, Dapkins IP, Sharif I, Vurmaz D, Modak SS, Srinivasan K, Warhadpande S, Shrivastav R, McDevitt JT. Clinical Decision Support Tool and Rapid Point-of-Care Platform for Determining Disease Severity in Patients with COVID-19. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2020:2020.04.16.20068411. [PMID: 32511607 PMCID: PMC7276034 DOI: 10.1101/2020.04.16.20068411] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
SARS-CoV-2 is the virus that causes coronavirus disease (COVID-19) which has reached pandemic levels resulting in significant morbidity and mortality affecting every inhabited continent. The large number of patients requiring intensive care threatens to overwhelm healthcare systems globally. Likewise, there is a compelling need for a COVID-19 disease severity test to prioritize care and resources for patients at elevated risk of mortality. Here, an integrated point-of-care COVID-19 Severity Score and clinical decision support system is presented using biomarker measurements of C-reactive protein (CRP), N-terminus pro B type natriuretic peptide (NT-proBNP), myoglobin (MYO), D-dimer, procalcitonin (PCT), creatine kinase-myocardial band (CK-MB), and cardiac troponin I (cTnI). The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality. The COVID-19 Severity Score was trained and evaluated using data from 160 hospitalized COVID-19 patients from Wuhan, China. Our analysis finds that COVID-19 Severity Scores were significantly higher for the group that died versus the group that was discharged with median (interquartile range) scores of 59 (40-83) and 9 (6-17), respectively, and area under the curve of 0.94 (95% CI 0.89-0.99). These promising initial models pave the way for a point-of-care COVID-19 Severity Score system to impact patient care after further validation with externally collected clinical data. Clinical decision support tools for COVID-19 have strong potential to empower healthcare providers to save lives by prioritizing critical care in patients at high risk for adverse outcomes.
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Affiliation(s)
- Michael P McRae
- Department of Biomaterials, Bioengineering Institute, New York University, New York, NY, USA
| | - Glennon W Simmons
- Department of Biomaterials, Bioengineering Institute, New York University, New York, NY, USA
| | | | - Zhibing Lu
- Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Stella K Kang
- Departments of Radiology, Population Health New York University School of Medicine, New York, NY, USA
| | - David Fenyo
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY, USA
| | | | - Isaac P Dapkins
- Department of Population Health and Internal Medicine, New York University School of Medicine, New York, NY, USA
| | - Iman Sharif
- Departments of Pediatrics and Population Health, New York University School of Medicine, New York, NY, USA
| | - Deniz Vurmaz
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, New York University, New York, NY, USA
| | - Sayli S Modak
- Department of Biomaterials, Bioengineering Institute, New York University, New York, NY, USA
| | - Kritika Srinivasan
- Departments of Biomaterials, Pathology, New York University School of Medicine, New York University, New York, NY, USA
| | - Shruti Warhadpande
- Department of Biomaterials, Bioengineering Institute, New York University, New York, NY, USA
| | - Ravi Shrivastav
- Department of Biomaterials, Bioengineering Institute, New York University, New York, NY, USA
| | - John T McDevitt
- Department of Biomaterials, Bioengineering Institute, New York University, New York, NY, USA
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