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Binson VA, Thomas S, Subramoniam M, Arun J, Naveen S, Madhu S. A Review of Machine Learning Algorithms for Biomedical Applications. Ann Biomed Eng 2024; 52:1159-1183. [PMID: 38383870 DOI: 10.1007/s10439-024-03459-3] [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: 12/30/2023] [Accepted: 01/24/2024] [Indexed: 02/23/2024]
Abstract
As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.
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Affiliation(s)
- V A Binson
- Department of Electronics Engineering, Saintgits College of Engineering, Kottayam, India
| | - Sania Thomas
- Department of Computer Science and Engineering, Saintgits College of Engineering, Kottayam, India
| | - M Subramoniam
- Department of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - J Arun
- Centre for Waste Management-International Research Centre, Sathyabama Institute of Science and Technology, Chennai, 600119, India
| | - S Naveen
- Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
| | - S Madhu
- Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
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Martin JDM, Claudia F, Romain AC. How well does your e-nose detect cancer? Application of artificial breath analysis for performance assessment. J Breath Res 2024; 18:026002. [PMID: 38211310 DOI: 10.1088/1752-7163/ad1d64] [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: 10/02/2023] [Accepted: 01/11/2024] [Indexed: 01/13/2024]
Abstract
Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between 'healthy' samples (breath without additives) and 'cancer' samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.
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Affiliation(s)
- Justin D M Martin
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
| | - Falzone Claudia
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
| | - Anne-Claude Romain
- Department of Environmental Sciences, Sensing of Atmospheres and Monitoring (SAM), SPHERES Research Unit, University of Liège, 6700 Arlon, Belgium
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3
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Chen KC, Kuo SW, Shie RH, Yang HY. Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data. Respir Res 2024; 25:32. [PMID: 38225616 PMCID: PMC10790556 DOI: 10.1186/s12931-024-02668-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 01/02/2024] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported. OBJECTIVE The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm. METHODS We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard. RESULTS We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84-0.91), the summary specificity was 1.00 (95% CI 0.85-1.00), the AUC was 0.96 (95% CI 0.94-0.98), the pAUC was 0.92 (95% CI 0.89-0.96), and the DOR was 207.62 (95% CI 24.62-924.64). CONCLUSION Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.
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Affiliation(s)
- Ke-Cheng Chen
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
- National Taiwan University College of Medicine, Taipei, Taiwan
| | - Shuenn-Wen Kuo
- Division of Thoracic Surgery, Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan
- National Taiwan University College of Medicine, Taipei, Taiwan
| | - Ruei-Hao Shie
- Green Energy and Environmental Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Hsiao-Yu Yang
- Institute of Environmental and Occupational Health Sciences, National Taiwan University College of Public Health, No. 17 Xuzhou Road, Taipei, 10055, Taiwan.
- Department of Public Health, National Taiwan University College of Public Health, Taipei, Taiwan.
- Population Health Research Center, National Taiwan University, Taipei, Taiwan.
- Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei, Taiwan.
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4
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Li Y, Wei X, Zhou Y, Wang J, You R. Research progress of electronic nose technology in exhaled breath disease analysis. MICROSYSTEMS & NANOENGINEERING 2023; 9:129. [PMID: 37829158 PMCID: PMC10564766 DOI: 10.1038/s41378-023-00594-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 10/14/2023]
Abstract
Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.
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Affiliation(s)
- Ying Li
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192 China
- Laboratory of Intelligent Microsystems, Beijing Information Science and Technology University, Beijing, 100192 China
| | - Xiangyang Wei
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192 China
- Laboratory of Intelligent Microsystems, Beijing Information Science and Technology University, Beijing, 100192 China
| | - Yumeng Zhou
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192 China
| | - Jing Wang
- School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun, 130022 China
| | - Rui You
- School of Instrument Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing, 100192 China
- Laboratory of Intelligent Microsystems, Beijing Information Science and Technology University, Beijing, 100192 China
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5
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Stella GM, Lettieri S, Piloni D, Ferrarotti I, Perrotta F, Corsico AG, Bortolotto C. Smart Sensors and Microtechnologies in the Precision Medicine Approach against Lung Cancer. Pharmaceuticals (Basel) 2023; 16:1042. [PMID: 37513953 PMCID: PMC10385174 DOI: 10.3390/ph16071042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND AND RATIONALE The therapeutic interventions against lung cancer are currently based on a fully personalized approach to the disease with considerable improvement of patients' outcome. Alongside continuous scientific progresses and research investments, massive technologic efforts, innovative challenges, and consolidated achievements together with research investments are at the bases of the engineering and manufacturing revolution that allows a significant gain in clinical setting. AIM AND METHODS The scope of this review is thus to focus, rather than on the biologic traits, on the analysis of the precision sensors and novel generation materials, as semiconductors, which are below the clinical development of personalized diagnosis and treatment. In this perspective, a careful revision and analysis of the state of the art of the literature and experimental knowledge is presented. RESULTS Novel materials are being used in the development of personalized diagnosis and treatment for lung cancer. Among them, semiconductors are used to analyze volatile cancer compounds and allow early disease diagnosis. Moreover, they can be used to generate MEMS which have found an application in advanced imaging techniques as well as in drug delivery devices. CONCLUSIONS Overall, these issues represent critical issues only partially known and generally underestimated by the clinical community. These novel micro-technology-based biosensing devices, based on the use of molecules at atomic concentrations, are crucial for clinical innovation since they have allowed the recent significant advances in cancer biology deciphering as well as in disease detection and therapy. There is an urgent need to create a stronger dialogue between technologists, basic researchers, and clinicians to address all scientific and manufacturing efforts towards a real improvement in patients' outcome. Here, great attention is focused on their application against lung cancer, from their exploitations in translational research to their application in diagnosis and treatment development, to ensure early diagnosis and better clinical outcomes.
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Affiliation(s)
- Giulia Maria Stella
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy
- Cardiothoracic and Vascular Department, Unit of Respiratory Diseases, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Sara Lettieri
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy
- Cardiothoracic and Vascular Department, Unit of Respiratory Diseases, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Davide Piloni
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy
- Cardiothoracic and Vascular Department, Unit of Respiratory Diseases, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Ilaria Ferrarotti
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy
- Cardiothoracic and Vascular Department, Unit of Respiratory Diseases, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Fabio Perrotta
- Department of Translational Medical Sciences, University of Campania "L. Vanvitelli", 80131 Napoli, Italy
- U.O.C. Clinica Pneumologica "L. Vanvitelli", A.O. dei Colli, Ospedale Monaldi, 80131 Napoli, Italy
| | - Angelo Guido Corsico
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy
- Cardiothoracic and Vascular Department, Unit of Respiratory Diseases, IRCCS Policlinico San Matteo, 27100 Pavia, Italy
| | - Chandra Bortolotto
- Department of Clinical-Surgical, Diagnostic and Paediatric Sciences, University of Pavia Medical School, 27100 Pavia, Italy
- Department of Diagnostic Services and Imaging, Unit of Radiology, Fondazione IRCCS Policlinico San Matteo, 27100 Pavia, Italy
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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.
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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
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Epping R, Koch M. On-Site Detection of Volatile Organic Compounds (VOCs). Molecules 2023; 28:1598. [PMID: 36838585 PMCID: PMC9966347 DOI: 10.3390/molecules28041598] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/11/2023] Open
Abstract
Volatile organic compounds (VOCs) are of interest in many different fields. Among them are food and fragrance analysis, environmental and atmospheric research, industrial applications, security or medical and life science. In the past, the characterization of these compounds was mostly performed via sample collection and off-site analysis with gas chromatography coupled to mass spectrometry (GC-MS) as the gold standard. While powerful, this method also has several drawbacks such as being slow, expensive, and demanding on the user. For decades, intense research has been dedicated to find methods for fast VOC analysis on-site with time and spatial resolution. We present the working principles of the most important, utilized, and researched technologies for this purpose and highlight important publications from the last five years. In this overview, non-selective gas sensors, electronic noses, spectroscopic methods, miniaturized gas chromatography, ion mobility spectrometry and direct injection mass spectrometry are covered. The advantages and limitations of the different methods are compared. Finally, we give our outlook into the future progression of this field of research.
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Affiliation(s)
- Ruben Epping
- Division of Organic Trace and Food Analysis, Bundesanstalt für Materialforschung und -Prüfung, 12489 Berlin, Germany
| | - Matthias Koch
- Division of Organic Trace and Food Analysis, Bundesanstalt für Materialforschung und -Prüfung, 12489 Berlin, Germany
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Gashimova EM, Temerdashev AZ, Porkhanov VA, Polyakov IS, Perunov DV. Comparative Analysis of Pre- and Post-Surgery Exhaled Breath Profiles of Volatile Organic Compounds of Patients with Lung Cancer and Benign Tumors. JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1134/s1061934822120036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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9
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Abbasian MH, Ardekani AM, Sobhani N, Roudi R. The Role of Genomics and Proteomics in Lung Cancer Early Detection and Treatment. Cancers (Basel) 2022; 14:5144. [PMID: 36291929 PMCID: PMC9600051 DOI: 10.3390/cancers14205144] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/10/2022] [Accepted: 10/18/2022] [Indexed: 08/17/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related death worldwide, with non-small-cell lung cancer (NSCLC) being the primary type. Unfortunately, it is often diagnosed at advanced stages, when therapy leaves patients with a dismal prognosis. Despite the advances in genomics and proteomics in the past decade, leading to progress in developing tools for early diagnosis, targeted therapies have shown promising results; however, the 5-year survival of NSCLC patients is only about 15%. Low-dose computed tomography or chest X-ray are the main types of screening tools. Lung cancer patients without specific, actionable mutations are currently treated with conventional therapies, such as platinum-based chemotherapy; however, resistances and relapses often occur in these patients. More noninvasive, inexpensive, and safer diagnostic methods based on novel biomarkers for NSCLC are of paramount importance. In the current review, we summarize genomic and proteomic biomarkers utilized for the early detection and treatment of NSCLC. We further discuss future opportunities to improve biomarkers for early detection and the effective treatment of NSCLC.
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Affiliation(s)
- Mohammad Hadi Abbasian
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran 1497716316, Iran
| | - Ali M. Ardekani
- Department of Medical Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran 1497716316, Iran
| | - Navid Sobhani
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Raheleh Roudi
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA 94305, USA
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Keogh RJ, Riches JC. The Use of Breath Analysis in the Management of Lung Cancer: Is It Ready for Primetime? Curr Oncol 2022; 29:7355-7378. [PMID: 36290855 PMCID: PMC9600994 DOI: 10.3390/curroncol29100578] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/22/2022] [Accepted: 09/28/2022] [Indexed: 11/07/2022] Open
Abstract
Breath analysis is a promising non-invasive method for the detection and management of lung cancer. Exhaled breath contains a complex mixture of volatile and non-volatile organic compounds that are produced as end-products of metabolism. Several studies have explored the patterns of these compounds and have postulated that a unique breath signature is emitted in the setting of lung cancer. Most studies have evaluated the use of gas chromatography and mass spectrometry to identify these unique breath signatures. With recent advances in the field of analytical chemistry and machine learning gaseous chemical sensing and identification devices have also been created to detect patterns of odorant molecules such as volatile organic compounds. These devices offer hope for a point-of-care test in the future. Several prospective studies have also explored the presence of specific genomic aberrations in the exhaled breath of patients with lung cancer as an alternative method for molecular analysis. Despite its potential, the use of breath analysis has largely been limited to translational research due to methodological issues, the lack of standardization or validation and the paucity of large multi-center studies. It is clear however that it offers a potentially non-invasive alternative to investigations such as tumor biopsy and blood sampling.
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Gashimova EM, Temerdashev AZ, Porkhanov VA, Polyakov IS, Perunov DV. Volatile Organic Compounds in Exhaled Breath as Biomarkers of Lung Cancer: Advances and Potential Problems. JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1134/s106193482207005x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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12
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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.
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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
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13
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Chiu HY, Chao HS, Chen YM. Application of Artificial Intelligence in Lung Cancer. Cancers (Basel) 2022; 14:cancers14061370. [PMID: 35326521 PMCID: PMC8946647 DOI: 10.3390/cancers14061370] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 03/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Lung cancer is the leading cause of malignancy-related mortality worldwide. AI has the potential to help to treat lung cancer from detection, diagnosis and decision making to prognosis prediction. AI could reduce the labor work of LDCT, CXR, and pathology slides reading. AI as a second reader in LDCT and CXR reading reduces the effort of radiologists and increases the accuracy of nodule detection. Introducing AI to WSI in digital pathology increases the Kappa value of the pathologist and help to predict molecular phenotypes with radiomics and H&E staining. By extracting radiomics from image data and WSI from the histopathology field, clinicians could use AI to predict tumor properties such as gene mutation and PD-L1 expression. Furthermore, AI could help clinicians in decision-making by predicting treatment response, side effects, and prognosis prediction in medical treatment, surgery, and radiotherapy. Integrating AI in the future clinical workflow would be promising. Abstract Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient’s prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.
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Affiliation(s)
- Hwa-Yen Chiu
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- Institute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Division of Internal Medicine, Hsinchu Branch, Taipei Veterans General Hospital, Hsinchu 310, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: ; Tel.: +886-2-28712121
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan; (H.-Y.C.); (Y.-M.C.)
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
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14
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Kaloumenou M, Skotadis E, Lagopati N, Efstathopoulos E, Tsoukalas D. Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22031238. [PMID: 35161984 PMCID: PMC8840008 DOI: 10.3390/s22031238] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 01/30/2022] [Accepted: 02/01/2022] [Indexed: 05/07/2023]
Abstract
Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.
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Affiliation(s)
- Maria Kaloumenou
- Department of Applied Physics, National Technical University of Athens, 15780 Athens, Greece; (M.K.); (D.T.)
| | - Evangelos Skotadis
- Department of Applied Physics, National Technical University of Athens, 15780 Athens, Greece; (M.K.); (D.T.)
- Correspondence:
| | - Nefeli Lagopati
- Medical School, National and Kapodistrian University of Athens, 75, Mikras Asias Str., Goudi, 11527 Athens, Greece; (N.L.); (E.E.)
| | - Efstathios Efstathopoulos
- Medical School, National and Kapodistrian University of Athens, 75, Mikras Asias Str., Goudi, 11527 Athens, Greece; (N.L.); (E.E.)
| | - Dimitris Tsoukalas
- Department of Applied Physics, National Technical University of Athens, 15780 Athens, Greece; (M.K.); (D.T.)
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15
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郭 玲, 邬 红, 李 强, 许 川, 刘 羽. [Advances on Collection and Analysis of Volatile Organic Compounds
in the Diagnosis of Lung Cancer]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2021; 24:796-803. [PMID: 34802212 PMCID: PMC8607281 DOI: 10.3779/j.issn.1009-3419.2021.101.41] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 09/20/2021] [Accepted: 09/28/2021] [Indexed: 11/05/2022]
Abstract
Lung cancer is a leading cause of cancer-related morbidity and mortality globally, which is the biggest menace to the health and life of the population. Screening and early detection of lung cancer are effective in reducing its mortality, and the measurement of volatile organic compounds (VOCs) has become a promising clinical means for early detection, course detection and prognosis management of lung cancer, with advantages of rapid speed, non-invasiveness and convenience. Now, a variety of VOCs collection ways and analysis methods have emerged at home and abroad. This report summarized three aspects, including VOCs collection, multiple methods of analysis and progress in the diagnosis and treatment of lung cancer. At last, we discussed the limitations and prospects of VOCs analysis.
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Affiliation(s)
- 玲 郭
- 610041 四川,电子科技大学医学院附属肿瘤医院/四川省肿瘤医院Department of Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610041, China
| | - 红 邬
- 610041 四川,电子科技大学医学院附属肿瘤医院/四川省肿瘤医院Department of Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610041, China
| | - 强 李
- 610041 四川,电子科技大学医学院附属肿瘤医院/四川省肿瘤医院Department of Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610041, China
| | - 川 许
- 610041 四川,电子科技大学医学院附属肿瘤医院/四川省肿瘤医院Department of Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610041, China
| | - 羽阳 刘
- 100853 北京,解放军医学院Medical School of Chinese PLA, Beijing 100853, China
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16
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Khashei M, Bakhtiarvand N, Etemadi S. A novel reliability-based regression model for medical modeling and forecasting. Diabetes Metab Syndr 2021; 15:102331. [PMID: 34781137 DOI: 10.1016/j.dsx.2021.102331] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/29/2021] [Accepted: 10/31/2021] [Indexed: 01/23/2023]
Abstract
BACKGROUND AND AIMS In recent decades, modeling and forecasting have played a significant role in the diagnosis and treatment of different diseases. Various forecasting models have been developed to improve data-based decision-making processes in medical systems. Although these models differ in many aspects, they all originate from the assumption that more generalizable results are achieved by more accurate models. This means that accuracy is considered as the only prominent feature to evaluate the generalizability of forecasting models. On the other side, due to the changeable medical situations and even changeable models' results, making stable and reliable performance is necessary to adopt appropriate medical decisions. Hence, reliability and stability of models' performance is another effective factor on the model's generalizability that should be taken into consideration in developing medical forecasting models. METHODS In this paper, a new reliability-based forecasting approach is developed to address this gap and achieve more consistent performance in making medical predictions. The proposed approach is implemented on the classic regression model which is a common accuracy-based statistical method in medical fields. To evaluate the effectiveness of the proposed model, it has been performed by using two medical benchmark datasets from UCI and obtained results are compared with the classic regression model. RESULTS Empirical results show that the proposed model has outperformed the classic regression model in terms of error criteria such as MSE and MAE. So, the presented model can be utilized as an appropriate alternative for the traditional regression model in making effective medical decisions. CONCLUSIONS Based on the obtained results, the proposed model can be an appropriate alternative for traditional multiple linear regression for modeling in real-world applications, especially when more generalization and/or more reliability is needed.
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Affiliation(s)
- Mehdi Khashei
- Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, Iran; Center for Optimization and Intelligent Decision Making in Healthcare Systems (COID-Health), Isfahan University of Technology (IUT), Isfahan, 8415683111, Iran.
| | - Negar Bakhtiarvand
- Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, Iran
| | - Sepideh Etemadi
- Department of Industrial and Systems Engineering, Isfahan University of Technology (IUT), Isfahan, Iran
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17
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van der Sar IG, Wijbenga N, Nakshbandi G, Aerts JGJV, Manintveld OC, Wijsenbeek MS, Hellemons ME, Moor CC. The smell of lung disease: a review of the current status of electronic nose technology. Respir Res 2021; 22:246. [PMID: 34535144 PMCID: PMC8448171 DOI: 10.1186/s12931-021-01835-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/26/2021] [Indexed: 02/08/2023] Open
Abstract
There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-time non-invasive diagnostic tool, and for monitoring disease course and therapeutic effects. To date, multiple eNoses have been developed and evaluated in clinical studies across a wide spectrum of lung diseases, mainly for diagnostic purposes. Heterogeneity in study design, analysis techniques, and differences between eNose devices currently hamper generalization and comparison of study results. Moreover, many pilot studies have been performed, while validation and implementation studies are scarce. These studies are needed before implementation in clinical practice can be realised. This review summarises the technical aspects of available eNose devices and the available evidence for clinical application of eNose technology in different lung diseases. Furthermore, recommendations for future research to pave the way for clinical implementation of eNose technology are provided.
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Affiliation(s)
- I G van der Sar
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - N Wijbenga
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - G Nakshbandi
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - J G J V Aerts
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - O C Manintveld
- Department of Cardiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - M S Wijsenbeek
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - M E Hellemons
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - C C Moor
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
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18
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Mota I, Teixeira-Santos R, Cavaleiro Rufo J. Detection and identification of fungal species by electronic nose technology: A systematic review. FUNGAL BIOL REV 2021. [DOI: 10.1016/j.fbr.2021.03.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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19
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Peled N, Fuchs V, Kestenbaum EH, Oscar E, Bitran R. An Update on the Use of Exhaled Breath Analysis for the Early Detection of Lung Cancer. LUNG CANCER-TARGETS AND THERAPY 2021; 12:81-92. [PMID: 34429674 PMCID: PMC8378913 DOI: 10.2147/lctt.s320493] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 07/17/2021] [Indexed: 12/18/2022]
Abstract
Lung cancer has historically been the main responsible for cancer associated deaths. Owing to this is our current inability to screen for and diagnose early pathological findings, preventing us from a timely intervention when cure is still achievable. Over the last decade, together with the extraordinary progress in therapeutical alternatives in the field, there has been an ongoing search for a biomarker that would allow for this. Numerous technologies have been developed but their clinical application is yet to come. In this review, we provide an update on volatile organic compounds, a non-invasive method that can hold the key for detecting early metabolic pathway changes in carcinogenesis. For its compilation, web-based search engines of scientific literature such as PubMed were explored and reviewed, using articles, research, and papers deemed meaningful by authors discretion. After a brief description, we depict how this technique can complement current methods and present the value of electronic noses in the identification of the “breathprint”. Lastly, we bring some of the latest updates in the field together with the current limitations and final remarks.
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Affiliation(s)
- Nir Peled
- Shaare Zedek Medical Center, The Hebrew University, Jerusalem, Israel
| | - Vered Fuchs
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Emily H Kestenbaum
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Elron Oscar
- Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Raul Bitran
- The Legacy Heritage Oncology Center & Dr. Larry Norton Institute, Soroka Medical Center, Beer-Sheva, Israel
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20
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MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis. CHEMOSENSORS 2021. [DOI: 10.3390/chemosensors9080209] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.
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21
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Binson VA, Subramoniam M, Mathew L. Discrimination of COPD and lung cancer from controls through breath analysis using a self-developed e-nose. J Breath Res 2021; 15. [PMID: 34243176 DOI: 10.1088/1752-7163/ac1326] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/09/2021] [Indexed: 01/22/2023]
Abstract
This work details the application of a metal oxide semiconductor (MOS) sensor based electronic nose (e-nose) system in the discrimination of lung cancer and chronic obstructive pulmonary disease (COPD) from healthy controls. The sensor array integrated with supervised classification algorithms was able to detect and classify exhaled breath samples from healthy controls, patients with COPD, and lung cancer by recognizing the amount of volatile organic compounds present in it. This paper details the e-nose design, participant selection, sampling methods, and data analysis. The clinical feasibility of the system was checked in 32 lung cancer patients, 38 COPD patients, and 72 healthy controls including smokers and non-smokers. One of the advantages of the equipment design was portability and robustness since the system was conditioned with elements that allowed its easy movement. In the discrimination of lung cancer from controls, the k-nearest neighbors gave an acceptable accuracy, sensitivity, and specificity of 91.3%, 84.4%, and 94.4% respectively. The support vector machine gave better results for COPD discrimination from controls with 90.9% accuracy, 81.6% sensitivity, and 95.8% specificity. Even though the attained results were good, further examinations are essential to enhance the sensor array system, investigate the long-run reproducibility, repeatability, and enlarge its relevancy.
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Affiliation(s)
- V A Binson
- Department of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.,Department of Electronics Engineering, Saintgits College of Engineering, Kottayam, Kerala, India
| | - M Subramoniam
- Department of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - Luke Mathew
- Department of Pulmonology, Believers Church Medical College Hospital, Thiruvalla, Kerala, India
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22
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Sierra-Padilla A, García-Guzmán JJ, López-Iglesias D, Palacios-Santander JM, Cubillana-Aguilera L. E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing. SENSORS (BASEL, SWITZERLAND) 2021; 21:4976. [PMID: 34372213 PMCID: PMC8347095 DOI: 10.3390/s21154976] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/17/2021] [Accepted: 07/18/2021] [Indexed: 01/14/2023]
Abstract
Conducting polymers (CPs) are extensively studied due to their high versatility and electrical properties, as well as their high environmental stability. Based on the above, their applications as electronic devices are promoted and constitute an interesting matter of research. This review summarizes their application in common electronic devices and their implementation in electronic tongues and noses systems (E-tongues and E-noses, respectively). The monitoring of diverse factors with these devices by multivariate calibration methods for different applications is also included. Lastly, a critical discussion about the enclosed analytical potential of several conducting polymer-based devices in electronic systems reported in literature will be offered.
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Affiliation(s)
- Alfonso Sierra-Padilla
- Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Polígono del Río San Pedro S/N, 11510 Puerto Real, Cadiz, Spain; (A.S.-P.); (L.C.-A.)
| | - Juan José García-Guzmán
- Instituto de Investigación e Innovación Biomédica de Cadiz (INiBICA), Hospital Universitario ‘Puerta del Mar’, Universidad de Cadiz, 11009 Cadiz, Cadiz, Spain;
| | - David López-Iglesias
- Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Polígono del Río San Pedro S/N, 11510 Puerto Real, Cadiz, Spain; (A.S.-P.); (L.C.-A.)
| | - José María Palacios-Santander
- Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Polígono del Río San Pedro S/N, 11510 Puerto Real, Cadiz, Spain; (A.S.-P.); (L.C.-A.)
| | - Laura Cubillana-Aguilera
- Institute of Research on Electron Microscopy and Materials (IMEYMAT), Department of Analytical Chemistry, Faculty of Sciences, Campus de Excelencia Internacional del Mar (CEIMAR), University of Cadiz, Campus Universitario de Puerto Real, Polígono del Río San Pedro S/N, 11510 Puerto Real, Cadiz, Spain; (A.S.-P.); (L.C.-A.)
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23
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Gashimova E, Osipova A, Temerdashev A, Porkhanov V, Polyakov I, Perunov D, Dmitrieva E. Study of confounding factors influence on lung cancer diagnostics effectiveness using gas chromatography-mass spectrometry analysis of exhaled breath. Biomark Med 2021; 15:821-829. [PMID: 34223778 DOI: 10.2217/bmm-2020-0828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Aim: The purpose of this study was to estimate volatile organic compounds (VOCs) ability to distinguish exhaled breath samples of lung cancer patients and healthy volunteers and to assess the effect of smoking status and gender on parameters. Patients & methods: Exhaled breath samples of 40 lung cancer patients and 40 healthy individuals were analyzed using gas chromatography-mass spectrometry. Influence of other factors on the exhaled breath VOCs profile was investigated. Results: Some parameters correlating with the disease status were affected by other factors. Excluding these parameters allows creating a logistic regression diagnostic model with 83% sensitivity and 81% specificity. Conclusion: Influence of other factors on the exhaled breath VOCs profile has to be taken into account to avoid misleading results.
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Affiliation(s)
- Elina Gashimova
- Department of Analytical Chemistry, Kuban State University, Krasnodar, Russia
| | - Anna Osipova
- Department of Analytical Chemistry, Kuban State University, Krasnodar, Russia
| | - Azamat Temerdashev
- Department of Analytical Chemistry, Kuban State University, Krasnodar, Russia
| | - Vladimir Porkhanov
- Research Institute - Regional Clinical Hospital No. 1 named after Prof. SV Ochapovsky, Krasnodar, Russia
| | - Igor Polyakov
- Research Institute - Regional Clinical Hospital No. 1 named after Prof. SV Ochapovsky, Krasnodar, Russia
| | - Dmitry Perunov
- Research Institute - Regional Clinical Hospital No. 1 named after Prof. SV Ochapovsky, Krasnodar, Russia
| | - Ekaterina Dmitrieva
- Department of Analytical Chemistry, Kuban State University, Krasnodar, Russia
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24
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Recognizing lung cancer and stages using a self-developed electronic nose system. Comput Biol Med 2021; 131:104294. [PMID: 33647830 DOI: 10.1016/j.compbiomed.2021.104294] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 02/17/2021] [Accepted: 02/17/2021] [Indexed: 12/25/2022]
Abstract
Exhaled breath contains thousands of gaseous volatile organic compounds (VOCs) that could be used as non-invasive biomarkers of lung cancer. Breath-based lung cancer screening has attracted wide attention on account of its convenience, low cost and easy popularization. In this paper, the research of lung cancer detection and staging is conducted by the self-developed electronic nose (e-nose) system. In order to investigate the performance of the device in distinguishing lung cancer patients from healthy controls, two feature extraction methods and two different classification models were adopted. Among all the models, kernel principal component analysis (KPCA) combined with extreme gradient boosting (XGBoost) achieved the best results among 235 breath samples. The accuracy, sensitivity and specificity of e-nose system were 93.59%, 95.60% and 91.09%, respectively. Meanwhile, the device could innovatively classify stages of 90 lung cancer patients (i.e., 44 stage III and 46 stage IV). Experimental results indicated that the recognition accuracy of lung cancer stages was more than 80%. Further experiments of this research also showed that the combination of sensor array and pattern recognition algorithms could identify and distinguish the expiratory characteristics of lung cancer, smoking and other respiratory diseases.
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25
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Liu L, Li W, He Z, Chen W, Liu H, Chen K, Pi X. Detection of lung cancer with electronic nose using a novel ensemble learning framework. J Breath Res 2021; 15. [PMID: 33578407 DOI: 10.1088/1752-7163/abe5c9] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/12/2021] [Indexed: 02/02/2023]
Abstract
Breath analysis based on electronic nose (e-nose) is a promising new technology for the detection of lung cancer that is non-invasive, simple to operate and cost-effective. Lung cancer screening by e-nose relies on predictive models established using machine learning methods. However, using only a single machine learning method to detect lung cancer has some disadvantages, including low detection accuracy and high false negative rate. To address these problems, groups of individual learning models with excellent performance were selected from classic models, including Support Vector Machine, Decision Tree, Random Forest, Logistic Regression and K-nearest neighbor regression, to build an ensemble learning framework (PCA-SVE). The output result of the PCA-SVE framework was obtained by voting. To test this approach, we analyzed 214 breath samples measured by e-nose with 11 gas sensors of four types using the proposed PCA-SVE framework. Experimental results indicated that the accuracy, sensitivity, and specificity of the proposed framework were 95.75%, 94.78%, and 96.96%, respectively. This framework overcomes the disadvantages of a single model, thereby providing an improved, practical alternative for exhaled breath analysis by e-nose.
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Affiliation(s)
- Lei Liu
- Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing, P.R. China, Chongqing, Chongqing, 400044, CHINA
| | - Wang Li
- School of Pharmacy and Bioengineering, Chongqing University of Technology, No.174 Shazhengjie, Shapingba, Chongqing, Chongqing, 400044, CHINA
| | - ZiChun He
- Chongqing Red Cross Hospital (People's Hospital of Jiangbei District), Chongqing Red Cross Hospital, 168 Hai'er Rd, Chongqing, 400020 , CHINA
| | - Weimin Chen
- Kunming University, No.727 South Jingming Rd, Chenggong District, Kunming, Yunnan, 650500, CHINA
| | - Hongying Liu
- Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, No.174 Shazhengjie, Shapingba, Chongqing, Chongqing, 400044, CHINA
| | - Ke Chen
- Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, , Chongqing University, No.174 Shazhengjie, Shapingba, Chongqing, Chongqing, 400044, CHINA
| | - Xitian Pi
- Key Laboratory of Biotechnology Science and Technology, Ministry of Education, College of Bioengineering, , Chongqing University, No.174 Shazhengjie, Shapingba, Chongqing, Chongqing, 400044, CHINA
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Potential of the Electronic Nose for the Detection of Respiratory Diseases with and without Infection. Int J Mol Sci 2020; 21:ijms21249416. [PMID: 33321951 PMCID: PMC7763696 DOI: 10.3390/ijms21249416] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/16/2020] [Accepted: 11/23/2020] [Indexed: 02/06/2023] Open
Abstract
Respiratory tract infections are common, and when affecting the lower airways and lungs, can result in significant morbidity and mortality. There is an unfilled need for simple, non-invasive tools that can be used to screen for such infections at the clinical point of care. The electronic nose (eNose) is a novel technology that detects volatile organic compounds (VOCs). Early studies have shown that certain diseases and infections can result in characteristic changes in VOC profiles in the exhaled breath. This review summarizes current knowledge on breath analysis by the electronic nose and its potential for the detection of respiratory diseases with and without infection.
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Rodríguez-Hernández P, Cardador MJ, Arce L, Rodríguez-Estévez V. Analytical Tools for Disease Diagnosis in Animals via Fecal Volatilome. Crit Rev Anal Chem 2020; 52:917-932. [PMID: 33180561 DOI: 10.1080/10408347.2020.1843130] [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] [Indexed: 01/05/2023]
Abstract
Volatilome analysis is growing in attention for the diagnosis of diseases in animals and humans. In particular, volatilome analysis in fecal samples is starting to be proposed as a fast, easy and noninvasive method for disease diagnosis. Volatilome comprises volatile organic compounds (VOCs), which are produced during both physiological and patho-physiological processes. Thus, VOCs from a pathological condition often differ from those of a healthy state and therefore the VOCs profile can be used in the detection of some diseases. Due to their strengths and advantages, feces are currently being used to obtain information related to health status in animals. However, they are complex samples, that can present problems for some analytical techniques and require special consideration in their use and preparation before analysis. This situation demands an effort to clarify which analytic options are currently being used in the research context to analyze the possibilities these offer, with the final objectives of contributing to develop a standardized methodology and to exploit feces potential as a diagnostic matrix. The current work reviews the studies focused on the diagnosis of animal diseases through fecal volatilome in order to evaluate the analytical methods used and their advantages and limitations. The alternatives found in the literature for sampling, storage, sample pretreatment, measurement and data treatment have been summarized, considering all the steps involved in the analytical process.
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Affiliation(s)
| | - M J Cardador
- Department of Analytical Chemistry, Institute of Fine Chemistry and Nanochemistry, University of Córdoba, Córdoba, Spain
| | - L Arce
- Department of Analytical Chemistry, Institute of Fine Chemistry and Nanochemistry, University of Córdoba, Córdoba, Spain
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28
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The Role of Electronic Noses in Phenotyping Patients with Chronic Obstructive Pulmonary Disease. BIOSENSORS-BASEL 2020; 10:bios10110171. [PMID: 33187142 PMCID: PMC7697924 DOI: 10.3390/bios10110171] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/05/2020] [Accepted: 11/09/2020] [Indexed: 12/12/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is a common progressive disorder of the respiratory system which is currently the third leading cause of death worldwide. Exhaled breath analysis is a non-invasive method to study lung diseases, and electronic noses have been extensively used in breath research. Studies with electronic noses have proved that the pattern of exhaled volatile organic compounds is different in COPD. More recent investigations have reported that electronic noses could potentially distinguish different endotypes (i.e., neutrophilic vs. eosinophilic) and are able to detect microorganisms in the airways responsible for exacerbations. This article will review the published literature on electronic noses and COPD and help in identifying methodological, physiological, and disease-related factors which could affect the results.
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29
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Baldini C, Billeci L, Sansone F, Conte R, Domenici C, Tonacci A. Electronic Nose as a Novel Method for Diagnosing Cancer: A Systematic Review. BIOSENSORS-BASEL 2020; 10:bios10080084. [PMID: 32722438 PMCID: PMC7459473 DOI: 10.3390/bios10080084] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/13/2020] [Accepted: 07/21/2020] [Indexed: 12/13/2022]
Abstract
Cancer is fast becoming the most important cause of death worldwide, its mortality being mostly caused by late or wrong diagnosis. Novel strategies have been developed to identify early signs of cancer in a minimally obtrusive way, including the Electronic Nose (E-Nose) technology, user-friendly, cost- and time-saving alternative to classical approaches. This systematic review, conducted under the PRISMA guidelines, identified 60 articles directly dealing with the E-Nose application in cancer research published up to 31 January 2020. Among these works, the vast majority reported successful E-Nose use for diagnosing Lung Cancer, showing promising results especially when employing the Aeonose tool, discriminating subjects with Lung Cancer from controls in more than 80% of individuals, in most studies. In order to tailor the main limitations of the proposed approach, including the application of the protocol to advanced stage of cancer, sample heterogeneity and massive confounders, future studies should be conducted on early stage patients, and on larger cohorts, as to better characterize the specific breathprint associated with the various subtypes of cancer. This would ultimately lead to a better and faster diagnosis and to earlier treatment, possibly reducing the burden associated to such conditions.
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Affiliation(s)
- Chiara Baldini
- School of Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy;
| | - Lucia Billeci
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Francesco Sansone
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Raffaele Conte
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Claudio Domenici
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
| | - Alessandro Tonacci
- Institute of Clinical Physiology—National Research Council of Italy (IFC-CNR), Via Moruzzi 1, 56124 Pisa, Italy; (L.B.); (F.S.); (R.C.); (C.D.)
- Correspondence:
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30
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Kort S, Brusse-Keizer M, Gerritsen JW, Schouwink H, Citgez E, de Jongh F, van der Maten J, Samii S, van den Bogart M, van der Palen J. Improving lung cancer diagnosis by combining exhaled-breath data and clinical parameters. ERJ Open Res 2020; 6:00221-2019. [PMID: 32201682 PMCID: PMC7073409 DOI: 10.1183/23120541.00221-2019] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 01/14/2020] [Indexed: 12/25/2022] Open
Abstract
Introduction Exhaled-breath analysis of volatile organic compounds could detect lung cancer earlier, possibly leading to improved outcomes. Combining exhaled-breath data with clinical parameters may improve lung cancer diagnosis. Methods Based on data from a previous multi-centre study, this article reports additional analyses. 138 subjects with non-small cell lung cancer (NSCLC) and 143 controls without NSCLC breathed into the Aeonose. The diagnostic accuracy, presented as area under the receiver operating characteristic curve (AUC-ROC), of the Aeonose itself was compared with 1) performing a multivariate logistic regression analysis of the distinct clinical parameters obtained, and 2) using this clinical information beforehand in the training process of the artificial neural network (ANN) for the breath analysis. Results NSCLC patients (mean±sd age 67.1±9.1 years, 58% male) were compared with controls (62.1±7.0 years, 40.6% male). The AUC-ROC of the classification value of the Aeonose itself was 0.75 (95% CI 0.69–0.81). Adding age, number of pack-years and presence of COPD to this value in a multivariate regression analysis resulted in an improved performance with an AUC-ROC of 0.86 (95% CI 0.81–0.90). Adding these clinical variables beforehand to the ANN for classifying the breath print also led to an improved performance with an AUC-ROC of 0.84 (95% CI 0.79–0.89). Conclusions Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose, either post hoc in a multivariate regression analysis or a priori to the ANN, significantly improves the diagnostic accuracy to detect the presence or absence of lung cancer. Adding readily available clinical information to the classification value of exhaled-breath analysis with the Aeonose significantly improves the diagnostic accuracy to detect the presence or absence of lung cancerhttp://bit.ly/38ps6fH
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Affiliation(s)
- Sharina Kort
- Dept of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | | | | | - Hugo Schouwink
- Dept of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Emanuel Citgez
- Dept of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Frans de Jongh
- Dept of Pulmonary Medicine, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Jan van der Maten
- Dept of Pulmonary Medicine, Medisch Centrum Leeuwarden, Leeuwarden, the Netherlands
| | - Suzy Samii
- Dept of Pulmonary Medicine, Deventer Ziekenhuis, Deventer, the Netherlands
| | | | - Job van der Palen
- Medical School Twente, Medisch Spectrum Twente, Enschede, the Netherlands.,Dept of Research Methodology, Measurement, and Data Analysis, University of Twente, Enschede, the Netherlands
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31
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Zerka F, Barakat S, Walsh S, Bogowicz M, Leijenaar RTH, Jochems A, Miraglio B, Townend D, Lambin P. Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care. JCO Clin Cancer Inform 2020; 4:184-200. [PMID: 32134684 PMCID: PMC7113079 DOI: 10.1200/cci.19.00047] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/16/2020] [Indexed: 02/06/2023] Open
Abstract
Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns.Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives.Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes.Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. Our purpose is to review the major implementations of distributed learning in health care.
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Affiliation(s)
- Fadila Zerka
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Oncoradiomics, Liège, Belgium
| | - Samir Barakat
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Oncoradiomics, Liège, Belgium
| | - Sean Walsh
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Oncoradiomics, Liège, Belgium
| | - Marta Bogowicz
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Ralph T. H. Leijenaar
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
- Oncoradiomics, Liège, Belgium
| | - Arthur Jochems
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | | | - David Townend
- Department of Health, Ethics, and Society, CAPHRI (Care and Public Health Research Institute), Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab, Department of Precision Medicine, GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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32
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van Geffen WH, Lamote K, Costantini A, Hendriks LEL, Rahman NM, Blum TG, van Meerbeeck J. The electronic nose: emerging biomarkers in lung cancer diagnostics. Breathe (Sheff) 2020; 15:e135-e141. [PMID: 32280381 PMCID: PMC7121878 DOI: 10.1183/20734735.0309-2019] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Lung cancer is very common and the most common cause of cancer death worldwide. Despite recent progress in the systemic treatment of lung cancer (checkpoint inhibitors and tyrosine kinase inhibitors), each year, >1.5 million people die due to this disease. Most lung cancer patients already have advanced disease at the time of diagnosis. Computed tomography screening of high-risk individuals can detect lung cancer at an earlier stage but at a cost of false-positive findings. Biomarkers could lead towards a reduction of these false-positive findings and earlier lung cancer diagnosis, and have the potential to improve outcomes and treatment monitoring. To date, there is a lack of such biomarkers for lung cancer and other thoracic malignancies, although electronic nose (e-nose)-derived biomarkers are of interest. E-nose techniques using exhaled breath component measurements can detect lung cancer with a sensitivity ranging from 71% to 96% and specificity from 33 to 100%. In some case series, such results have been validated but this is mostly using internal validation and hence, more work is needed. Furthermore, standardised sampling and analysis methods are lacking, impeding interstudy comparison and clinical implementation. In this narrative review, we provide an overview of the currently available data on E-nose technology for lung cancer detection.
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Affiliation(s)
- Wouter H van Geffen
- Dept of Pulmonary Medicine, Medical Centre Leeuwarden, Leeuwarden, The Netherlands
| | - Kevin Lamote
- Dept of Pulmonology, Antwerp University Hospital, Edegem, Belgium.,Laboratory of Experimental Medicine and Pediatrics, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.,Infla-Med Consortium of Excellence, University of Antwerp, Antwerp, Belgium.,Internal Medicine and Paediatrics, Ghent University, Ghent, Belgium
| | - Adrien Costantini
- Dept of Respiratory Diseases and Thoracic Oncology, APHP, Hôpital Ambroise Paré, Boulogne-Billancourt, France
| | - Lizza E L Hendriks
- Dept of Pulmonary Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Najib M Rahman
- Oxford Respiratory Trials Unit, Nuffield Dept of Medicine, University of Oxford, Oxford, UK.,Oxford NIHR Biomedical Research Centre, Oxford, UK
| | - Torsten G Blum
- Lungenklinik Heckeshorn, Helios Klinikum Emil von Behring, Berlin, Germany
| | - Jan van Meerbeeck
- Dept of Pulmonology, Antwerp University Hospital, Edegem, Belgium.,Laboratory of Experimental Medicine and Pediatrics, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium.,Infla-Med Consortium of Excellence, University of Antwerp, Antwerp, Belgium.,Internal Medicine and Paediatrics, Ghent University, Ghent, Belgium
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33
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Bruderer T, Gaisl T, Gaugg MT, Nowak N, Streckenbach B, Müller S, Moeller A, Kohler M, Zenobi R. On-Line Analysis of Exhaled Breath Focus Review. Chem Rev 2019; 119:10803-10828. [PMID: 31594311 DOI: 10.1021/acs.chemrev.9b00005] [Citation(s) in RCA: 108] [Impact Index Per Article: 21.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
On-line analysis of exhaled breath offers insight into a person's metabolism without the need for sample preparation or sample collection. Due to its noninvasive nature and the possibility to sample continuously, the analysis of breath has great clinical potential. The unique features of this technology make it an attractive candidate for applications in medicine, beyond the task of diagnosis. We review the current methodologies for on-line breath analysis, discuss current and future applications, and critically evaluate challenges and pitfalls such as the need for standardization. Special emphasis is given to the use of the technology in diagnosing respiratory diseases, potential niche applications, and the promise of breath analysis for personalized medicine. The analytical methodologies used range from very small and low-cost chemical sensors, which are ideal for continuous monitoring of disease status, to optical spectroscopy and state-of-the-art, high-resolution mass spectrometry. The latter can be utilized for untargeted analysis of exhaled breath, with the capability to identify hitherto unknown molecules. The interpretation of the resulting big data sets is complex and often constrained due to a limited number of participants. Even larger data sets will be needed for assessing reproducibility and for validation of biomarker candidates. In addition, molecular structures and quantification of compounds are generally not easily available from on-line measurements and require complementary measurements, for example, a separation method coupled to mass spectrometry. Furthermore, a lack of standardization still hampers the application of the technique to screen larger cohorts of patients. This review summarizes the present status and continuous improvements of the principal on-line breath analysis methods and evaluates obstacles for their wider application.
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Affiliation(s)
- Tobias Bruderer
- Department of Chemistry and Applied Biosciences , Swiss Federal Institute of Technology , CH-8093 Zurich , Switzerland.,Division of Respiratory Medicine , University Children's Hospital Zurich and Children's Research Center Zurich , CH-8032 Zurich , Switzerland
| | - Thomas Gaisl
- Department of Pulmonology , University Hospital Zurich , CH-8091 Zurich , Switzerland.,Zurich Center for Interdisciplinary Sleep Research , University of Zurich , CH-8091 Zurich , Switzerland
| | - Martin T Gaugg
- Department of Chemistry and Applied Biosciences , Swiss Federal Institute of Technology , CH-8093 Zurich , Switzerland
| | - Nora Nowak
- Department of Chemistry and Applied Biosciences , Swiss Federal Institute of Technology , CH-8093 Zurich , Switzerland
| | - Bettina Streckenbach
- Department of Chemistry and Applied Biosciences , Swiss Federal Institute of Technology , CH-8093 Zurich , Switzerland
| | - Simona Müller
- Department of Chemistry and Applied Biosciences , Swiss Federal Institute of Technology , CH-8093 Zurich , Switzerland
| | - Alexander Moeller
- Division of Respiratory Medicine , University Children's Hospital Zurich and Children's Research Center Zurich , CH-8032 Zurich , Switzerland
| | - Malcolm Kohler
- Department of Pulmonology , University Hospital Zurich , CH-8091 Zurich , Switzerland.,Center for Integrative Human Physiology , University of Zurich , CH-8091 Zurich , Switzerland.,Zurich Center for Interdisciplinary Sleep Research , University of Zurich , CH-8091 Zurich , Switzerland
| | - Renato Zenobi
- Department of Chemistry and Applied Biosciences , Swiss Federal Institute of Technology , CH-8093 Zurich , Switzerland
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