1
|
Gashimova E, Temerdashev A, Perunov D, Porkhanov V, Polyakov I. Diagnosis of Lung Cancer Through Exhaled Breath: A Comprehensive Study. Mol Diagn Ther 2024:10.1007/s40291-024-00744-8. [PMID: 39299985 DOI: 10.1007/s40291-024-00744-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2024] [Indexed: 09/22/2024]
Abstract
OBJECTIVES Exhaled breath analysis is an attractive lung cancer diagnostic tool. However, various factors that are not related to the disease status, comorbidities, and other diseases must be considered to obtain a reliable diagnostic model. METHODS Exhaled breath samples from 646 individuals including 273 patients with lung cancer (LC), 90 patients with cancer of other localizations (OC), 150 patients with noncancer lung diseases (NLD), and 133 healthy controls (HC) were analyzed using gas chromatography-mass spectrometry (GC-MS). The samples were collected in Tedlar bags. Volatile organic compounds (VOCs) were preconcentrated on Tenax TA sorbent tubes with subsequent two-stage thermal desorption followed by GC-MS analysis. The influence of age, gender, smoking status, time since last food consumption, and comorbidities on exhaled breath were evaluated. Also, the effect of histology, TNM, tumor localization, treatment status, and the presence of a tumor on VOC profile of patients with lung cancer were assessed. Intergroup statistics were estimated, diagnostic models were created using artificial neural networks (ANNs) and gradient boosted decision trees (GBDTs). RESULTS Smoking status and food consumption affect exhaled breath VOC profile: benzene, ethylbenzene, toluene, 1,3-pentadiene 1,4-pentadiene acetonitrile, and some ratios are significantly different in exhaled breath of smokers and nonsmokers; the ratios 2,3-butandione/2-pentanone, 2,3-butandione/dimethylsulfide, and 2-butanone/2-pentanone are affected by time since last food consumption. Exhaled breath of LC is affected by the form of the disease and comorbidities. One-pentanol and 2-butanone were different in exhaled breath of patients with various tumor localization; 2-butanone was different in exhaled breath of patients before and during treatment. Diabetes as a comorbidity affects the pentanal level in exhaled breath; obesity affects the ratios of 2,3-butanedione/dimethylsulfide and 2-butanone/isoprene. Sensitivity and specificity of diagnostic models aimed to discriminate LC and HC, OC, and NLD were 78.7% and 51.0%, 62.2% and 53.4%, and 60.4% and 58.0%, respectively. HC and patients, regardless of the disease, can be classified with sensitivity of 76.6% and specificity of 68.2%. CONCLUSIONS The models created to diagnose lung cancer can also classify OC and NLD as patients with lung cancer. Additionally, the influence of comorbidities and factors not related to the disease status must be considered before the creation of diagnostic models to avoid false results.
Collapse
Affiliation(s)
- Elina Gashimova
- Kuban State University, Stavropol'skaya St. 149, Krasnodar, 350040, Russia.
| | - Azamat Temerdashev
- Kuban State University, Stavropol'skaya St. 149, Krasnodar, 350040, Russia
| | - Dmitry Perunov
- Research Institute, Regional Clinical Hospital, No 1 n.a. Prof. S.V. Ochapovsky, 1 May St. 167, Krasnodar, 350086, Russia
| | - Vladimir Porkhanov
- Research Institute, Regional Clinical Hospital, No 1 n.a. Prof. S.V. Ochapovsky, 1 May St. 167, Krasnodar, 350086, Russia
| | - Igor Polyakov
- Research Institute, Regional Clinical Hospital, No 1 n.a. Prof. S.V. Ochapovsky, 1 May St. 167, Krasnodar, 350086, Russia
| |
Collapse
|
2
|
Zhou M, Wang Q, Lu X, Zhang P, Yang R, Chen Y, Xia J, Chen D. Exhaled breath and urinary volatile organic compounds (VOCs) for cancer diagnoses, and microbial-related VOC metabolic pathway analysis: a systematic review and meta-analysis. Int J Surg 2024; 110:1755-1769. [PMID: 38484261 PMCID: PMC10942174 DOI: 10.1097/js9.0000000000000999] [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: 09/13/2023] [Accepted: 12/04/2023] [Indexed: 03/17/2024]
Abstract
BACKGROUND The gradual evolution of the detection and quantification of volatile organic compounds (VOCs) has been instrumental in cancer diagnosis. The primary objective of this study was to assess the diagnostic potential of exhaled breath and urinary VOCs in cancer detection. As VOCs are indicative of tumor and human metabolism, our work also sought to investigate the metabolic pathways linked to the development of cancerous tumors. MATERIALS AND METHODS An electronic search was performed in the PubMed database. Original studies on VOCs within exhaled breath and urine for cancer detection with a control group were included. A meta-analysis was conducted using a bivariate model to assess the sensitivity and specificity of the VOCs for cancer detection. Fagan's nomogram was designed to leverage the findings from our diagnostic analysis for the purpose of estimating the likelihood of cancer in patients. Ultimately, MetOrigin was employed to conduct an analysis of the metabolic pathways associated with VOCs in relation to both human and/or microbiota. RESULTS The pooled sensitivity, specificity and the area under the curve for cancer screening utilizing exhaled breath and urinary VOCs were determined to be 0.89, 0.88, and 0.95, respectively. A pretest probability of 51% can be considered as the threshold for diagnosing cancers with VOCs. As the estimated pretest probability of cancer exceeds 51%, it becomes more appropriate to emphasize the 'ruling in' approach. Conversely, when the estimated pretest probability of cancer falls below 51%, it is more suitable to emphasize the 'ruling out' approach. A total of 14, 14, 6, and 7 microbiota-related VOCs were identified in relation to lung, colorectal, breast, and liver cancers, respectively. The enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in the aforementioned tumor types. CONCLUSIONS The analysis of exhaled breath and urinary VOCs showed promise for cancer screening. In addition, the enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in four tumor types, namely lung, colorectum, breast and liver. These findings hold significant implications for the prospective clinical application of multiomics correlation in disease management and the exploration of potential therapeutic targets.
Collapse
Affiliation(s)
- Min Zhou
- Department of Breast Surgery, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi Maternity and Child Health Care Hospital
| | - Qinghua Wang
- Research Institute for Reproductive Health and Genetic Diseases, Women’s Hospital of Jiangnan University
| | - Xinyi Lu
- Department of Breast Surgery, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi Maternity and Child Health Care Hospital
| | - Ping Zhang
- Department of Breast Surgery, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi Maternity and Child Health Care Hospital
| | - Rui Yang
- Research Institute for Reproductive Health and Genetic Diseases, Women’s Hospital of Jiangnan University
| | - Yu Chen
- Research Institute for Reproductive Health and Genetic Diseases, Women’s Hospital of Jiangnan University
| | - Jiazeng Xia
- Department of General Surgery and Translational Medicine Center, The Affiliated Wuxi No. 2 People’s Hospital of Nanjing Medical University, Jiangnan University Medical Center, Wuxi, People’s Republic of China
| | - Daozhen Chen
- Department of Breast Surgery, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi Maternity and Child Health Care Hospital
- Research Institute for Reproductive Health and Genetic Diseases, Women’s Hospital of Jiangnan University
| |
Collapse
|
3
|
Liu C, Guan C, Li Y, Li Z, Wang Y, Han G. Advances in Electrochemical Biosensors for the Detection of Common Oral Diseases. Crit Rev Anal Chem 2024:1-21. [PMID: 38366356 DOI: 10.1080/10408347.2024.2315112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
Limiting and preventing oral diseases remains a major challenge to the health of populations around the world, so finding ways to detect early-stage diseases (e.g., caries, periodontal disease, and oral cancer) and aiding in their prevention has always been an important clinical treatment concept. The development and application of electrochemical detection technology can provide important support for the early detection and non-invasive diagnosis of oral diseases and make up for the shortcomings of traditional diagnostic methods, which are highly sensitive, non-invasive, cost-effective, and less labor-intensive. It detects specific disease markers in body fluids through electrochemical reactions, discovers early warning signals of diseases, and realizes rapid and reliable diagnosis. This paper comprehensively summarizes the development and application of electrochemical biosensors in the detection and diagnosis of common oral diseases in terms of application platforms, sensing types, and disease detection, and discusses the challenges faced by electrochemical biosensors in the detection of oral diseases as well as the great prospects for future applications, in the hope of providing important insights for the future development of electrochemical biosensors for the early detection of oral diseases.
Collapse
Affiliation(s)
- Chaoran Liu
- Department of Oral Geriatrics, Hospital of Stomatology, Jilin University, Changchun, China
| | - Changjun Guan
- School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China
| | - Yanan Li
- Department of Oral Geriatrics, Hospital of Stomatology, Jilin University, Changchun, China
| | - Ze Li
- Department of Oral Geriatrics, Hospital of Stomatology, Jilin University, Changchun, China
| | - Yanchun Wang
- Department of Oral Geriatrics, Hospital of Stomatology, Jilin University, Changchun, China
| | - Guanghong Han
- Department of Oral Geriatrics, Hospital of Stomatology, Jilin University, Changchun, China
| |
Collapse
|
4
|
Temilola DO, Adeola HA, Grobbelaar J, Chetty M. Liquid Biopsy in Head and Neck Cancer: Its Present State and Future Role in Africa. Cells 2023; 12:2663. [PMID: 37998398 PMCID: PMC10670726 DOI: 10.3390/cells12222663] [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: 10/10/2023] [Revised: 11/12/2023] [Accepted: 11/17/2023] [Indexed: 11/25/2023] Open
Abstract
The rising mortality and morbidity rate of head and neck cancer (HNC) in Africa has been attributed to factors such as the poor state of health infrastructures, genetics, and late presentation resulting in the delayed diagnosis of these tumors. If well harnessed, emerging molecular and omics diagnostic technologies such as liquid biopsy can potentially play a major role in optimizing the management of HNC in Africa. However, to successfully apply liquid biopsy technology in the management of HNC in Africa, factors such as genetic, socioeconomic, environmental, and cultural acceptability of the technology must be given due consideration. This review outlines the role of circulating molecules such as tumor cells, tumor DNA, tumor RNA, proteins, and exosomes, in liquid biopsy technology for the management of HNC with a focus on studies conducted in Africa. The present state and the potential opportunities for the future use of liquid biopsy technology in the effective management of HNC in resource-limited settings such as Africa is further discussed.
Collapse
Affiliation(s)
- Dada Oluwaseyi Temilola
- Department of Craniofacial Biology, Faculty of Dentistry, University of the Western Cape, Tygerberg Hospital, Cape Town 7505, South Africa;
| | - Henry Ademola Adeola
- Department of Oral and Maxillofacial Pathology, Faculty of Dentistry, University of the Western Cape, Tygerberg Hospital, Cape Town 7505, South Africa;
- Division of Dermatology, Department of Medicine, Faculty of Health Sciences and Groote Schuur Hospital, University of Cape Town, Cape Town 7925, South Africa
| | - Johan Grobbelaar
- Division of Otorhinolaryngology, Department of Surgical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg Hospital, Cape Town 7505, South Africa;
| | - Manogari Chetty
- Department of Craniofacial Biology, Faculty of Dentistry, University of the Western Cape, Tygerberg Hospital, Cape Town 7505, South Africa;
| |
Collapse
|
5
|
Kok R, van Schaijik B, Johnson NW, Malki MI, Frydrych A, Kujan O. Breath biopsy, a novel technology to identify head and neck squamous cell carcinoma: A systematic review. Oral Dis 2023; 29:3034-3048. [PMID: 35801385 DOI: 10.1111/odi.14305] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/22/2022] [Accepted: 07/05/2022] [Indexed: 11/27/2022]
Abstract
Head and neck cancers are a heterogeneous group of neoplasms, which together comprise the sixth most common cancer globally. Breath biopsies are a non-invasive clinical investigation that detect volatile organic compounds (VOCs) in exhaled breath. This systematic review examines current applications of breath biopsy for the diagnosis of head and neck squamous cell carcinoma (HNSCC), including data on efficacy and utility, and speculates on the future uses of this non-invasive detection method. Medline, PubMed, Web of Science, Cochrane and Scopus, as well as the grey literature were searched using a search strategy developed to identify relevant studies on the role of breath biopsy in the diagnosis of HNSCC. All included studies were subject to a thorough methodological quality assessment. The initial search generated a total of 1443 articles, 20 of which were eligible for review. A total of 660 HNSCC samples were investigated across the included studies. 3,7-dimethylundecane and benzaldehyde were among several VOCs to be significantly correlated with the presence of HNSCC compared to healthy controls. We show that current breath biopsy methods have high accuracy, specificity and sensitivity for identifying HNSCC. However, further studies are needed given the reported poor quality of the included studies.
Collapse
Affiliation(s)
- Rachel Kok
- UWA Dental School, The University of Western Australia, Perth, Western Australia, Australia
| | - Bede van Schaijik
- UWA Dental School, The University of Western Australia, Perth, Western Australia, Australia
| | - Newell W Johnson
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
- Faculty of Dentistry, Oral and Craniofacial Sciences, King's College London, London, UK
| | | | - Agnieszka Frydrych
- UWA Dental School, The University of Western Australia, Perth, Western Australia, Australia
| | - Omar Kujan
- UWA Dental School, The University of Western Australia, Perth, Western Australia, Australia
| |
Collapse
|
6
|
Lupi SM. A Retrospect of the Special Issue "Second Edition of Innovative Solutions for Oral Healthcare". Healthcare (Basel) 2023; 11:1952. [PMID: 37444786 DOI: 10.3390/healthcare11131952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Medicine and oral health are constantly progressing through a series of small advancements that, together, lead to significant discoveries and breakthroughs [...].
Collapse
Affiliation(s)
- Saturnino Marco Lupi
- Department of Clinical Surgical, Diagnostic and Pediatric Sciences, School of Dentistry, University of Pavia, 27100 Pavia, Italy
| |
Collapse
|
7
|
Grizzi F, Bax C, Hegazi MAAA, Lotesoriere BJ, Zanoni M, Vota P, Hurle RF, Buffi NM, Lazzeri M, Tidu L, Capelli L, Taverna G. Early Detection of Prostate Cancer: The Role of Scent. CHEMOSENSORS 2023; 11:356. [DOI: 10.3390/chemosensors11070356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
Prostate cancer (PCa) represents the cause of the second highest number of cancer-related deaths worldwide, and its clinical presentation can range from slow-growing to rapidly spreading metastatic disease. As the characteristics of most cases of PCa remains incompletely understood, it is crucial to identify new biomarkers that can aid in early detection. Despite the prostate-specific antigen serum (PSA) levels, prostate biopsy, and imaging representing the actual gold-standard for diagnosing PCa, analyzing volatile organic compounds (VOCs) has emerged as a promising new frontier. We and other authors have reported that highly trained dogs can recognize specific VOCs associated with PCa with high accuracy. However, using dogs in clinical practice has several limitations. To exploit the potential of VOCs, an electronic nose (eNose) that mimics the dog olfactory system and can potentially be used in clinical practice was designed. To explore the eNose as an alternative to dogs in diagnosing PCa, we conducted a systematic literature review and meta-analysis of available studies. PRISMA guidelines were used for the identification, screening, eligibility, and selection process. We included six studies that employed trained dogs and found that the pooled diagnostic sensitivity was 0.87 (95% CI 0.86–0.89; I2, 98.6%), the diagnostic specificity was 0.83 (95% CI 0.80–0.85; I2, 98.1%), and the area under the summary receiver operating characteristic curve (sROC) was 0.64 (standard error, 0.25). We also analyzed five studies that used an eNose to diagnose PCa and found that the pooled diagnostic sensitivity was 0.84 (95% CI, 0.80–0.88; I2, 57.1%), the diagnostic specificity was 0.88 (95% CI, 0.84–0.91; I2, 66%), and the area under the sROC was 0.93 (standard error, 0.03). These pooled results suggest that while highly trained dogs have the potentiality to diagnose PCa, the ability is primarily related to olfactory physiology and training methodology. The adoption of advanced analytical techniques, such as eNose, poses a significant challenge in the field of clinical practice due to their growing effectiveness. Nevertheless, the presence of limitations and the requirement for meticulous study design continue to present challenges when employing eNoses for the diagnosis of PCa.
Collapse
Affiliation(s)
- Fabio Grizzi
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy
| | - Carmen Bax
- Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, 20133 Milan, Italy
| | - Mohamed A. A. A. Hegazi
- Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Beatrice Julia Lotesoriere
- Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, 20133 Milan, Italy
| | - Matteo Zanoni
- Department of Urology, Humanitas Mater Domini, 21100 Castellanza, Italy
| | - Paolo Vota
- Department of Urology, Humanitas Mater Domini, 21100 Castellanza, Italy
| | - Rodolfo Fausto Hurle
- Department of Urology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Nicolò Maria Buffi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy
- Department of Urology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Massimo Lazzeri
- Department of Urology, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy
| | - Lorenzo Tidu
- Italian Ministry of Defenses, “Vittorio Veneto” Division, 50136 Firenze, Italy
| | - Laura Capelli
- Politecnico di Milano, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, 20133 Milan, Italy
| | - Gianluigi Taverna
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy
- Department of Urology, Humanitas Mater Domini, 21100 Castellanza, Italy
| |
Collapse
|
8
|
P H, Rangarajan M, Pandya HJ. Breath VOC analysis and machine learning approaches for disease screening: a review. J Breath Res 2023; 17. [PMID: 36634360 DOI: 10.1088/1752-7163/acb283] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 01/12/2023] [Indexed: 01/14/2023]
Abstract
Early disease detection is often correlated with a reduction in mortality rate and improved prognosis. Currently, techniques like biopsy and imaging that are used to screen chronic diseases are invasive, costly or inaccessible to a large population. Thus, a non-invasive disease screening technology is the need of the hour. Existing non-invasive methods like gas chromatography-mass spectrometry, selected-ion flow-tube mass spectrometry, and proton transfer reaction-mass-spectrometry are expensive. These techniques necessitate experienced operators, making them unsuitable for a large population. Various non-invasive sources are available for disease detection, of which exhaled breath is preferred as it contains different volatile organic compounds (VOCs) that reflect the biochemical reactions in the human body. Disease screening by exhaled breath VOC analysis can revolutionize the healthcare industry. This review focuses on exhaled breath VOC biomarkers for screening various diseases with a particular emphasis on liver diseases and head and neck cancer as examples of diseases related to metabolic disorders and diseases unrelated to metabolic disorders, respectively. Single sensor and sensor array-based (Electronic Nose) approaches for exhaled breath VOC detection are briefly described, along with the machine learning techniques used for pattern recognition.
Collapse
Affiliation(s)
- Haripriya P
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Madhavan Rangarajan
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Hardik J Pandya
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore 560012, India.,Centre for Product Design and Manufacturing, Indian Institute of Science, Bangalore 560012, India
| |
Collapse
|
9
|
Adeoye J, Akinshipo A, Koohi-Moghadam M, Thomson P, Su YX. Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review. Front Oncol 2022; 12:976168. [DOI: 10.3389/fonc.2022.976168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
BackgroundThe impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.MethodsPubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.ResultsML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.ConclusionOverall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designsSystematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.
Collapse
|
10
|
Kwiatkowski A, Borys S, Sikorska K, Drozdowska K, Smulko JM. Clinical studies of detecting COVID-19 from exhaled breath with electronic nose. Sci Rep 2022; 12:15990. [PMID: 36163492 PMCID: PMC9512806 DOI: 10.1038/s41598-022-20534-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 09/14/2022] [Indexed: 11/17/2022] Open
Abstract
The COVID-19 pandemic has attracted numerous research studies because of its impact on society and the economy. The pandemic has led to progress in the development of diagnostic methods, utilizing the polymerase chain reaction (PCR) as the gold standard for coronavirus SARS-CoV-2 detection. Numerous tests can be used at home within 15 min or so but of with lower accuracy than PCR. There is still a need for point-of-care tests available for mass daily screening of large crowds in airports, schools, and stadiums. The same problem exists with fast and continuous monitoring of patients during their medical treatment. The rapid methods can use exhaled breath analysis which is non-invasive and delivers the result quite fast. Electronic nose can detect a cocktail of volatile organic com-pounds (VOCs) induced by virus infection and disturbed metabolism in the human body. In our exploratory studies, we present the results of COVID-19 detection in a local hospital by applying the developed electronic setup utilising commercial VOC gas sensors. We consider the technical problems noticed during the reported studies and affecting the detection results. We believe that our studies help to advance the proposed technique to limit the spread of COVID-19 and similar viral infections.
Collapse
Affiliation(s)
- Andrzej Kwiatkowski
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233, Gdańsk, Poland
| | - Sebastian Borys
- University Center of Maritime and Tropical Medicine, Powstania Styczniowego 9B, 81-519, Gdynia, Poland
| | - Katarzyna Sikorska
- University Center of Maritime and Tropical Medicine, Powstania Styczniowego 9B, 81-519, Gdynia, Poland.,Division of Tropical and Parasitic Diseases, Faculty of Health Sciences, Medical University of Gdańsk, Powstania Styczniowego 9B, 81-519, Gdynia, Poland
| | - Katarzyna Drozdowska
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233, Gdańsk, Poland
| | - Janusz M Smulko
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233, Gdańsk, Poland.
| |
Collapse
|
11
|
Machine learning in point-of-care automated classification of oral potentially malignant and malignant disorders: a systematic review and meta-analysis. Sci Rep 2022; 12:13797. [PMID: 35963880 PMCID: PMC9376104 DOI: 10.1038/s41598-022-17489-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/26/2022] [Indexed: 11/08/2022] Open
Abstract
Machine learning (ML) algorithms are becoming increasingly pervasive in the domains of medical diagnostics and prognostication, afforded by complex deep learning architectures that overcome the limitations of manual feature extraction. In this systematic review and meta-analysis, we provide an update on current progress of ML algorithms in point-of-care (POC) automated diagnostic classification systems for lesions of the oral cavity. Studies reporting performance metrics on ML algorithms used in automatic classification of oral regions of interest were identified and screened by 2 independent reviewers from 4 databases. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. 35 studies were suitable for qualitative synthesis, and 31 for quantitative analysis. Outcomes were assessed using a bivariate random-effects model following an assessment of bias and heterogeneity. 4 distinct methodologies were identified for POC diagnosis: (1) clinical photography; (2) optical imaging; (3) thermal imaging; (4) analysis of volatile organic compounds. Estimated AUROC across all studies was 0.935, and no difference in performance was identified between methodologies. We discuss the various classical and modern approaches to ML employed within identified studies, and highlight issues that will need to be addressed for implementation of automated classification systems in screening and early detection.
Collapse
|
12
|
Early Diagnosis of Oral Squamous Cell Carcinoma Based on Histopathological Images Using Deep and Hybrid Learning Approaches. Diagnostics (Basel) 2022; 12:diagnostics12081899. [PMID: 36010249 PMCID: PMC9406837 DOI: 10.3390/diagnostics12081899] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 07/30/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022] Open
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most common head and neck cancer types, which is ranked the seventh most common cancer. As OSCC is a histological tumor, histopathological images are the gold diagnosis standard. However, such diagnosis takes a long time and high-efficiency human experience due to tumor heterogeneity. Thus, artificial intelligence techniques help doctors and experts to make an accurate diagnosis. This study aimed to achieve satisfactory results for the early diagnosis of OSCC by applying hybrid techniques based on fused features. The first proposed method is based on a hybrid method of CNN models (AlexNet and ResNet-18) and the support vector machine (SVM) algorithm. This method achieved superior results in diagnosing the OSCC data set. The second proposed method is based on the hybrid features extracted by CNN models (AlexNet and ResNet-18) combined with the color, texture, and shape features extracted using the fuzzy color histogram (FCH), discrete wavelet transform (DWT), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM) algorithms. Because of the high dimensionality of the data set features, the principal component analysis (PCA) algorithm was applied to reduce the dimensionality and send it to the artificial neural network (ANN) algorithm to diagnose it with promising accuracy. All the proposed systems achieved superior results in histological image diagnosis of OSCC, the ANN network based on the hybrid features using AlexNet, DWT, LBP, FCH, and GLCM achieved an accuracy of 99.1%, specificity of 99.61%, sensitivity of 99.5%, precision of 99.71%, and AUC of 99.52%.
Collapse
|
13
|
Elmakaty I, Elmarasi M, Amarah A, Abdo R, Malki MI. Accuracy of artificial intelligence-assisted detection of Oral Squamous Cell Carcinoma: A systematic review and meta-analysis. Crit Rev Oncol Hematol 2022; 178:103777. [PMID: 35931404 DOI: 10.1016/j.critrevonc.2022.103777] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/21/2022] [Accepted: 08/01/2022] [Indexed: 10/16/2022] Open
Abstract
Oral Squamous Cell Carcinoma (OSCC) is an aggressive tumor with a poor prognosis. Accurate and timely diagnosis is therefore essential for reducing the burden of advanced disease and improving outcomes. In this meta-analysis, we evaluated the accuracy of artificial intelligence (AI)-assisted technologies in detecting OSCC. We included studies that validated any diagnostic modality that used AI to detect OSCC. A search was performed in six databases: PubMed, Embase, Scopus, Cochrane Library, ProQuest, and Web of Science up to 15 Mar 2022. The Quality Assessment Tool for Diagnostic Accuracy Studies was used to evaluate the included studies' quality, while the Split Component Synthesis method was utilized to quantitatively synthesize the pooled diagnostic efficacy estimates. We considered 16 out of the 566 yielded studies, which included twelve different AI models with a total of 6606 samples. The summary sensitivity, summary specificity, positive and negative likelihood ratios as well as the pooled diagnostic odds ratio were 92.0 % (95 % confidence interval [CI] 86.7-95.4 %), 91.9 % (95 % CI 86.5-95.3 %), 11.4 (95 % CI 6.74-19.2), 0.087 (95 % CI 0.051-0.146) and 132 (95 % CI 62.6-277), respectively. Our findings support the capability of AI-assisted systems to detect OSCC with high accuracy, potentially aiding the histopathological examination in early diagnosis, yet more prospective studies are needed to justify their use in the real population.
Collapse
Affiliation(s)
| | | | - Ahmed Amarah
- College of Medicine, QU Health, Qatar University, Doha, Qatar.
| | - Ruba Abdo
- College of Medicine, QU Health, Qatar University, Doha, Qatar.
| | - Mohammed Imad Malki
- Pathology Unit, Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, Qatar.
| |
Collapse
|
14
|
Scheepers MHMC, Al-Difaie Z, Brandts L, Peeters A, van Grinsven B, Bouvy ND. Diagnostic Performance of Electronic Noses in Cancer Diagnoses Using Exhaled Breath: A Systematic Review and Meta-analysis. JAMA Netw Open 2022; 5:e2219372. [PMID: 35767259 PMCID: PMC9244610 DOI: 10.1001/jamanetworkopen.2022.19372] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
IMPORTANCE There has been a growing interest in the use of electronic noses (e-noses) in detecting volatile organic compounds in exhaled breath for the diagnosis of cancer. However, no systematic evaluation has been performed of the overall diagnostic accuracy and methodologic challenges of using e-noses for cancer detection in exhaled breath. OBJECTIVE To provide an overview of the diagnostic accuracy and methodologic challenges of using e-noses for the detection of cancer. DATA SOURCES An electronic search was performed in the PubMed and Embase databases (January 1, 2000, to July 1, 2021). STUDY SELECTION Inclusion criteria were the following: (1) use of e-nose technology, (2) detection of cancer, and (3) analysis of exhaled breath. Exclusion criteria were (1) studies published before 2000; (2) studies not performed in humans; (3) studies not performed in adults; (4) studies that only analyzed biofluids; and (5) studies that exclusively used gas chromatography-mass spectrometry to analyze exhaled breath samples. DATA EXTRACTION AND SYNTHESIS PRISMA guidelines were used for the identification, screening, eligibility, and selection process. Quality assessment was performed using Quality Assessment of Diagnostic Accuracy Studies 2. Generalized mixed-effects bivariate meta-analysis was performed. MAIN OUTCOMES AND MEASURES Main outcomes were sensitivity, specificity, and mean area under the receiver operating characteristic curve. RESULTS This review identified 52 articles with a total of 3677 patients with cancer. All studies were feasibility studies. The sensitivity of e-noses ranged from 48.3% to 95.8% and the specificity from 10.0% to 100.0%. Pooled analysis resulted in a mean (SE) area under the receiver operating characteristic curve of 94% (95% CI, 92%-96%), a sensitivity of 90% (95% CI, 88%-92%), and a specificity of 87% (95% CI, 81%-92%). Considerable heterogeneity existed among the studies because of differences in the selection of patients, endogenous and exogenous factors, and collection of exhaled breath. CONCLUSIONS AND RELEVANCE Results of this review indicate that e-noses have a high diagnostic accuracy for the detection of cancer in exhaled breath. However, most studies were feasibility studies with small sample sizes, a lack of standardization, and a high risk of bias. The lack of standardization and reproducibility of e-nose research should be addressed in future research.
Collapse
Affiliation(s)
- Max H. M. C. Scheepers
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Zaid Al-Difaie
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Lloyd Brandts
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, the Netherlands
| | - Andrea Peeters
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, the Netherlands
| | - Bart van Grinsven
- Sensor Engineering, Faculty of Science and Engineering, Maastricht University, Maastricht, the Netherlands
| | - Nicole D. Bouvy
- Department of Surgery, Maastricht University Medical Center, Maastricht, the Netherlands
| |
Collapse
|