An electronic nose consists of an array of chemical sensors for the detection of volatile organic compounds and an algorithm for pattern recognition. Breath analysis with an electronic nose has a high diagnostic performance for atopic asthma that can be increased when combined with measurement of fractional exhaled nitric oxide.
by Dr Paolo Montuschi
Several volatile organic compounds (VOCs) have been identified in exhaled breath in healthy subjects and patients with respiratory disease by gas-chromatography/mass spectrometry (GC/MS) [1]. An electronic nose (e-nose) is an artificial system that generally consists of an array of chemical sensors for volatile detection and an algorithm for pattern recognition [2]. Several types of e-noses are available. An e-nose has been used for distinguishing between asthmatic and healthy subjects [3,4], between patients with asthma of different severity [3], between patients with lung cancer and healthy subjects [5], between patients with lung cancer and COPD [6], and between patients with asthma and COPD [7].
We compared the diagnostic performance of an e-nose with fractional exhaled nitric oxide (FENO), an independent method for assessing airway inflammation, and lung function testing in patients with asthma. We also investigated whether an e-nose could discriminate between asthmatic and healthy subjects and to establish the best sampling protocol (alveolar air vs oro-pharyngeal/airway air) for e-nose analysis. The results presented here are from a previously published study [4].
Methods
Study subjects
Twenty-four healthy subjects and 27 Caucasian patients with intermittent or mild persistent atopic asthma were studied [Table 1]. All asthmatic patients had a physician-based diagnosis of asthma, and the diagnosis and classification of asthma was based on clinical history, examination and pulmonary function parameters according to current guidelines [8]. Patients had intermittent asthma with symptoms equal to or less often than twice a week (step 1) or mild persistent asthma with symptoms more often than twice a week (step 2), forced expiratory volume in one second (FEV1) of 80% or greater of predicted value, and positive skin prick tests. Asthma patients were not taking any regular medication, but used inhaled short-acting β2-agonists as needed for symptom relief. Healthy subjects had no history of asthma and atopy, had negative skin prick tests and normal spirometry.
All subjects were never-smokers, had no upper respiratory tract infections in the previous 3 weeks, and were not being treated with corticosteroids or anti-inflammatory drugs for asthma in the previous 4 weeks.
Study design
The type of study was cross-sectional. Subjects attended on one occasion for clinical examination, FENO measurement, e-nose analysis, lung function tests, and skin prick testing. Informed consent was obtained from patients. The study was approved by the Ethics Committee of the Catholic University of the Sacred Heart, Rome, Italy.
Pulmonary function
Spirometry was performed with a Pony FX spirometer (Cosmed, Rome, Italy) and the best of three consecutive manoeuvres chosen.
Exhaled nitric oxide measurement
FENO was measured with the NIOX system (Aerocrine, Stockholm, Sweden) with a single breath on-line method at constant flow of 50 ml/sec according to American Thoracic Society guidelines [9].
Collection of exhaled breath
No food or drinks were allowed at least 2 hours prior to breath sampling. Two procedures for collecting exhaled breath were followed to study the differences between total exhaled breath and alveolar breath [4]. Subjects were asked to inhale to total lung capacity and to exhale into a mouthpiece connected to a Tedlar bag through a three-way valve [3]. In the first sampling procedure, the first 150 ml, considered as dead space volume, were collected into a separate Tedlar bag and discarded [Fig. 1a]. The remaining exhaled breath, principally derived from the alveolar compartment, was collected and immediately analysed with e-nose [4]. In the second sampling procedure, total exhaled breath was
collected [Fig. 1b] [4].
Electronic nose
A prototype e-nose (Libranose, University of Rome Tor Vergata, Italy), consisting of an array of eight quartz microbalance gas sensors coated by molecular films of metallo-porphyrins, was used [4]. E-nose responses are expressed as frequency changes for each sensor [Fig. 2] and then analysed by pattern recognition algorithms [2]. Ambient VOCs were subtracted from measures. Results were automatically adjusted for ambient VOCs.
Skin testing
Atopy was assessed by skin prick tests for common aeroallergens (Stallergenes, Antony, France).
Multivariate data analysis
Feed forward neural network was used to classify e-nose, FENO, spirometry data. A feed-forward neural network, a biologically derived classification model, is formed by a number of processing units (neurons), organised in layers. The datasets were divided into a training and a testing set. The first 27 measures collected were used for training and the remaining 24 measures for testing.
Statistical analysis
FENO values were expressed as medians and interquartile ranges (25th and 75th percentiles), whereas spirometry values were expressed as mean ±SEM. Unpaired t-test and Mann–Whitney U test were used for comparing groups for normally distributed and nonparametric data, respectively. Correlation was expressed as a Pearson coefficient and significance defined as a value of P<0.05.
Results
Electronic nose
The best results were obtained when e-nose analysis was performed on alveolar air as opposed to total exhaled breath [Table 2]. The diagnostic performance was determined in terms of the number of correct identifications of asthma diagnosis in the test dataset. Combination of e-nose analysis of alveolar air and FENO had the highest diagnostic performance for asthma (95.8%). The E-nose (87.5%) had a discriminating capacity that was higher than that of FENO (79.2%), spirometry (70.8%), combination of FENO and spirometry (83.3%), and combination of e-nose analysis of total exhaled breath and FENO (83.3%) [Fig. 3].
Exhaled nitric oxide
Median FENO values were higher in asthmatic patients than in healthy subjects [37. 6 (26.0–61.5) ppb vs 13.4 (10.0–19.9) ppb, P<0.0001, respectively].
Lung function tests
Both study groups had normal FEV1 values [Table 1]. Asthmatic patients had lower absolute (P = 0.032) and percentage of predicted FEV1 values (P = 0.004) than healthy subjects [Table 1]. Asthmatic patients had lower absolute (P = 0.003) and percentage of predicted forced expiratory flow between 25% and 75% of forced vital capacity (FEF25%–75%) (P = 0.002) than healthy subjects [Table 2].
Correlation between electronic nose, FeNO, and lung function tests
E-nose, FENO and lung function testing data were not correlated in either asthma or healthy control group.
Discussion
The original aspects of our study are:
1) the comparison between an e-nose and FENO, in addition to spirometry;
2) the comparison between total and alveolar exhaled air;
3) the analysis of data based on a neural network that included a training and a test analysis performed in two separate datasets for stringent quality control.
Our study indicates that an e-nose might be useful for asthma diagnosis, particularly in combination with FENO. Spirometry had the lowest diagnostic performance in line with a well-maintained lung function in patients with intermittent and persistent mild asthma. Our study confirms that FENO has a good diagnostic performance for asthma and suggests the possibility of using different non-invasive techniques for achieving a greater asthma diagnostic performance.
However, large powered studies are required to establish the diagnostic performance of e-nose, FENO and lung function testing in asthma patients. Ascertaining whether an e-nose could be used for screening of asthmatic patients requires large prospective studies. Also, the E-nose is not suitable for identifying and quantifying single breath VOCs, for which GC/MS is required.
Asthma is principally characterized by airway inflammation. It may seem surprising that the best results with the e-nose were obtained when collecting alveolar air rather than total exhaled breath which includes exhaled breath from the airways. This might reflect the contribution of oro-pharyngeal air which might introduce confounding factors making it e-nose analysis less reflective of what occurs within the respiratory system [10]. Moreover, the results of e-nose analysis of alveolar air could partially reflect the production of VOCs within the peripheral airways (mixed airways/alveolar air) due to significant inter-individual variability in dead space volume.
The lack of correlation between the e-nose results and those from FENO might indicate that these techniques reflect different aspects of airway inflammation. Formal studies to ascertain whether the e-nose could be used for assessing and monitoring airway inflammation in asthmatic patients are warranted. The E-nose is not suitable for ascertaining the cellular source of breath VOCs. Persistent airway inflammation can modify the metabolic pathways in patients with asthma. As patients included in our study were not on regular, anti-inflammatory drugs for asthma, we were unable to assess the effect of pharmacological treatment on breath VOCs, which requires controlled studies. Likewise, the effect of atopy on e-nose classification of asthma patients has to be addressed in future studies.
Validation of the classification model is essential. In our study, two different datasets for training and testing, obtained in different periods of time, were used. This way, the predictive capacity of the classification model is more suitable for a real life situation.
The E-nose analysis is a non-invasive technique that is potentially applicable to respiratory medicine. Several methodological issues including optimisation and standardisation of sample collection, transfer and storage of samples, use of calibration VOC mixtures, and qualitative and quantitative GC/MS analysis, have to be addressed.
In conclusion, an e-nose discriminates between asthma and healthy subjects and usage in combination with FENO increases the e-nose’s discriminatory ability. Large studies are required to establish the asthma diagnostic performance of e-nose. Whether this integrated non-invasive approach will translate into an early asthma diagnosis has still to be clarified.
Abbreviations
Abbreviations: FEF25%–75%, forced expiratory flow at 25% to 75% of forced vital capacity; FeNO, fractional exhaled nitric oxide; FEV1, forced expiratory volume in one second; FVC, forced vital capacity; GC/MS, gas chromatography/mass spectrometry; PEF, peak expiratory flow; VOC, volatile organic compound.
Acknowledgements
This study was supported by Merck Sharp and Dohme, and the Catholic University of the Sacred Heart.
References
1. Phillips M, Herrera J, et al. Variation in volatile organic compounds in the breath of normal humans. J Chromatogr B Biomed Sci Appl 1999; 729: 75–88.
2. Montuschi P, Mores N, et al. The electronic nose in respiratory medicine. Respiration (DOI: 10.1159/000340044, in press).
3. Dragonieri S, et al. An electronic nose in the discrimination of patients with asthma and controls. Allergy Clin Immunol. 2007; 120: 856–862.
4. Montuschi P, et al. Diagnostic performance of an electronic nose, fractional exhaled nitric oxide and lung function testing in asthma. Chest 2010; 137: 790–796.
5. Machado R, et al. Detection of lung cancer by sensor array analyses of exhaled breath. Am J Respir Care Med 2005; 171: 2186–1291.
6. Dragonieri S, et al. An electronic nose in the discrimination of patients with non-small cell lung cancer and COPD. Lung Cancer. 2009; 64: 166–170.
7. Fens N, et al: Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma. Am J Respir Crit Care Med 2009; 180: 1076–1082.
8. National Asthma Education and Prevention Program: Expert panel report III. Guidelines for the diagnosis and management of asthma. MD, Bethesda: National Heart, Lung, and Blood Institute, 2007; 1–61 (NIH publication no. 08-5847). Available at: www.nhlbi.nih.gov.
9. Recommendations for standardized procedures for the on-line and off-line measurement of exhaled lower respiratory nitric oxide and nasal nitric oxide in adults and children-1999: official statement of the American Thoracic Society 1999. Am J Respir Crit Care Med 1999; 160: 2104–2117.
10. van den Velde S, et al. Differences between alveolar air and mouth air. Anal Chem 2007; 79: 3425–3429.
The author
Paolo Montuschi, MD
Department of Pharmacology, Faculty of Medicine
Catholic University of the Sacred Heart
Largo F. Vito 1, 00168 Rome, Italy
E-mail: pmontuschi@rm.unicatt.it
Optical coherence tomography (OCT) has long been routinely used in ophthalmology, but recent studies in the field of renal cell carcinoma have demonstrated the ability of OCT to distinguish between renal malignancies and normal renal tissue. This suggests the possibility that, eventually, diagnosis by invasive biopsy could be replaced by non-invasive techniques.
by D. M. de Bruin, Dr P. Wagstaf, Dr K. Barwari, Prof. T. G. van leeuwen, Dr D. J. Faber, Prof. J. J. de la Rosette and Dr M. P. Laguna
The diagnosis of small renal masses
The diagnosis of small renal masses (SRMs) has seen a dramatic increase in presentation in recent decades. This change is mainly attributed to an increased use of abdominal imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI). However, the large imaging depth of such modalities is accompanied by a relatively low resolution of the obtained images, hindering conclusions at the level of histological composition. Recent studies have shown an inverse correlation between tumour size and malignancy, and up to 10 % of all extirpated (and thus deemed malignant) tumours appear to be benign on histopathological examination. This inverse relationship increases to 25% when small renal masses (SRM) (≤4 cm) are considered [1]. Therefore, pre-operative diagnosis of (small) renal tumours would be desirable. However, due to the high number of non-diagnostic biopsy results (up to 30 % in SRM), systematic use of pre-operative renal mass biopsies is still not recommended in the major guidelines [2–5].
Renal mass biopsy
Most renal biopsies are performed percutaneously and are supported by image guidance using computed tomography (CT) or ultrasound. The biopsies are normally performed under local anesthesia in an outpatient setting. When a renal tumour is evaluated, a biopsy can deliver one of two results: diagnostic (benign or malignant) or non-diagnostic, the later including the presence of necrosis, fibrosis and normal renal parenchyma with absence of tumour cells [Figure 1]. When the biopsy is diagnostic, other characteristics such as histopathologic subtype and grade can also be assessed [4, 6, 7].
Conceptually a failed biopsy means that there is no tumour tissue available for assessment in the biopsy specimen, although other types of tissue might be present in the sample. The reason for a failed biopsy could be a technical failure of the puncture method (e.g. misfire or malfunctioning of the biopsy gun) or incorrect sampling caused by imperfect image guidance. Incorrect sampling is sometimes unavoidable due to the nature of renal tumours, which may contain necrotic and fibrotic tissue, or be mixed in nature with solid and cystic components. Also, the presence of normal renal tissue implies that the sampling is incorrect, as very few renal masses are composed of normal renal tissue. The presence of fibrotic, inflammatory, fatty or necrotic tissue in the specimen means that a differential diagnosis between malignant and benign tumour cannot be made. Besides the fact that histopathological analysis requires time, it is also subject to a certain degree of discordance among different pathologists [8].
A diagnostic imaging tool that allowed real-time visualization of micro-scale tissue architecture and subsequent differentiation of tissue type during the procedure would accelerate and simplify the overall diagnostic procedure.
Optical imaging
Optical diagnostic imaging comprises a novel group of imaging modalities that provide information by assessing differences between incident and detected light caused by the interaction of light with tissue. Scattering and absorption are tissue-specific optical properties and, by assessing these interactions,
diffeent tissue types can be distinguished.
Optical imaging has shown potential in several medical fields where they are employed routinely in various forms, ranging from pulse oximeters to fundus cameras, and experimental reports show promising results in the field of oncology [9].
Optical coherence tomography (OCT) is a technology developed in the early 1990s for ophthalmological applications [10] and is routinely used in that setting in current clinical practice. OCT is the optical equivalent of ultrasound, using light instead of sound to produce micrometer-scale resolution, cross-sectional images up to a depth of about 2 mm in renal tissue [Figure 2]. Resolutions up to 5 µm can be achieved, being 100–250 times higher than high-resolution ultrasound [11] and approaching that of microscopy. An image produced by OCT resembles the tissue structures observed in histology and can, therefore, be considered as an ‘optical biopsy’ [12] [Figure 2]. Moreover, data extracted from the original OCT images can be used for functional quantitative analysis after careful calibration of the OCT system. This finally results in a ‘functional optical biopsy’. The imaging depth is primarily limited by the scattering of light by cellular structures, hindering the return of reflections to the receiver. This scattering causes the light intensity to attenuate as it penetrates deeper into the tissue and this attenuation of OCT signal can be quantified by measuring the decay of signal intensity per unit depth. Using Lambert–Beer’s law and after careful calibration of the OCT system, a tissue specific attenuation coefficient (μOCT mm-1) can be derived [13–15]. Because malignant tissue displays an increased number, larger and more irregularly shaped nuclei with a higher refractive index and more active mitochondria, the μOCT is expected to be higher compared to normal and benign tissue [Figure 3].
In urology, the early research on OCT has been focused on tissue diagnosis predominantly in bladder and prostate cancer [12, 16] and, more recently, attention has turned to the field of renal cell carcinoma (RCC) and research is currently ongoing [17–20]. We were the first authors to publish data on the ability of OCT to differentiate renal malignancies from normal renal tissue using quantitative analysis. Subsequently, we performed an in vivo pilot study assessing the difference of the attenuation-coefficient of malignant renal tumours from normal renal parenchyma and benign tumours [18]. OCT-imaging took place using an in vivo OCT-probe during surgery, and a significant difference was found between the attenuation-coefficient of normal renal tissue and that of malignant tumours. Attenuation-coefficients of malignant and benign tumours did differ, although it is likely that the small sample size (3 benign tumours vs 11 malignant) is hindering a statistical significance, suggesting that a clear difference might be found in larger samples. Linehan et al. assessed qualitative differences of OCT images of different types of renal tumours showing that certain tumour subtypes do have different appearances on OCT-imaging; however, intriguingly, clinical distinction of tumours such as RCC from oncocytomas could not be demonstrated [19].
Future developments
Finally, anticipating the validation of results showing optical diagnostics being able to differentiate renal tissues, there is a potential role for the techniques in several clinical scenarios. Before going as far as replacing pathological examination as discussed earlier, the two techniques might be complementary with the real-time- and non-invasive nature of the optical techniques serving as guidance for correct needle placement in order to reduce the number of non-diagnostic biopsy results, as is already done in other malignancies, and the small in vivo probes necessary for such interventions are becoming commercially available. The technological configuration behind OCT allows for easy integration with diffuse reflectance spectroscopy (DRS) and Raman spectroscopy (RS). Moreover, the structural-imaging- and light-scattering based quantitative possibilities of OCT together with the quantitative light absorption sensitivity of DRS and the inelastic light scattering (and therefore biochemical) sensitivity of RS yields the full potential of a functional optical biopsy.
We would like to thank the Cure for Cancer Foundation (CFC) and the Technology Foundation (STW) for project funding. This work is part of the innovative Medical Imaging Technologies program (iMIT) of STW and the Novel Biopsy Methods program of CFC.
References
1. Tan H-J et al. Understanding the role of percutaneous biopsy in the management of patients with a small renal mass. Urology 2012; 79(2): 372–377.
2. Volpe A, Jewett MA. Current role, techniques and outcomes of percutaneous biopsy of renal tumors. Expert Rev Anticancer Ther 2009; 9(6): 773–783.
3. Motzer RJ et al. NCCN clinical practice guidelines in oncology: kidney cancer. J Natl Compr Canc Netw 2009; 7(6): 618–630.
4. Leveridge MJ et al. Outcomes of small renal mass needle core biopsy, nondiagnostic percutaneous biopsy, and the role of repeat biopsy. Eur Urol 2011; 60(3): 578–584.
5. Ljungberg B et al. EAU guidelines on renal cell carcinoma: the 2010 update. Eur Urol 2010; 58(3): 398–406.
6. Menogue SR et al. Percutaneous core biopsy of small renal mass lesions: a diagnostic tool to better stratify patients for surgical intervention. BJU Int 2012; doi: 10.1111/j.1464-410X.2012.11384.x.
7. Laguna MP et al. Biopsy of a renal mass: where are we now? Curr Opin Urol 2009; 19(5): 447–453.
8. Kümmerlin IP et al. Cytological punctures in the diagnosis of renal tumours: a study on accuracy and reproducibility. Eur Urol 2009; 55(1): 187–198.
9. Pierce MC, Javier DJ, Richards‐Kortum R. Optical contrast agents and imaging systems for detection and diagnosis of cancer. Int J Cancer 2008; 123(9): 1979–1990.
10. Huang D et al. Optical coherence tomography. Diss. Massachusetts Institute of Technology, Whitaker College of Health Sciences and Technology, 1993.
11. Fujimoto, JG et al. Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy. Neoplasia 2000; 2(1–2): 9–25.
12. Crow P et al. Optical diagnostics in urology: current applications and future prospects. BJU Int 2003; 92(4): 400–407.
13. Faber DJ et al. Quantitative measurement of attenuation coefficients of weakly scattering media using optical coherence tomography. Optics Express 2004; 12(19): 4353–4365.
14. van Leeuwen TG, Faber DJ, Aalders MC. Measurement of the axial point spread function in scattering media using single-mode fiber-based optical coherence tomography. IEEE Journal of Selected Topics in Quantum Electronics 2003; 9(2): 227–233.
15. de Bruin DM et al. Optical phantoms of varying geometry based on thin building blocks with controlled optical properties. J Biomed Opt 2010; 15(2): 025001.
16. Cauberg EC et al. Quantitative measurement of attenuation coefficients of bladder biopsies using optical coherence tomography for grading urothelial carcinoma of the bladder. J Biomed Opt 2010; 15(6): 066013.
17. Barwari K et al. Advanced diagnostics in renal mass using optical coherence tomography: a preliminary report. J Endourol 2011; 25(2): 311–315.
18. Barwari K et al. Differentiation between normal renal tissue and renal tumours using functional optical coherence tomography: a phase I in vivo human study. BJU Int 2012; 110(8 Pt B):E415–20.
19. Linehan JA et al. Feasibility of optical coherence tomography imaging to characterize renal neoplasms: limitations in resolution and depth of penetration. BJU Int 2011; 108(11): 1820–1824.
20. Onozato ML et al. Optical coherence tomography of human kidney. J Urol 2010; 183(5): 2090–2094.
The authors
D. Martijn de Bruin1,2,* Msc; Peter G. Wagstaff1 MD; Kurdo Barwari1 PhD, MD; Ton G. van Leeuwen2 PhD; Dirk J. Faber2 PhD; Jean J. de la Rosette1 PhD, MD; M. Pilar Laguna1 PhD, MD.
1 Department of Urology, Academic Medical Center, Amsterdam, Meibergdreef 9, 1105 AZ, The Netherlands
2 Department of Engineering & Physics, Academic Medical Center, Amsterdam, Meibergdreef 9, 1105 AZ, The Netherlands
*Corresponding author
E-mail: d.m.debruin@amc.uva.nl
Alzheimer’s disease (AD) is now the fifth leading cause of death in people over 65 years old. The prevalence of AD is increasing rapidly as the world population ages; data show that the incidence increases exponentially after the age of 65, with more than 40% of those aged over 85 now affected. According to a 2012 WHO report, nearly 36 million people globally are living with dementia, around two thirds of whom have AD, and this number is predicted to triple by 2050. The 18th World Alzheimer’s day on the 21st of September emphasized the need to reduce the stigma of dementia and make communities ‘dementia-friendly’. While these aims are laudable, the pressing need is for very early diagnosis and timely effective treatment if health services are not to be totally overwhelmed by the escalating numbers of AD patients needing care.
Two major abnormalities, clearly visible at autopsy, are present in abundance in the brains of AD patients, namely beta-amyloid plaques (Aβ) and neurofibrillary tangles (tau protein). However these lesions are not very evident using even advanced neuroimaging techniques, and the disease is most frequently diagnosed by psychological tests and rule-out of other causes of neurodegeneration, so that many early cases remain undiagnosed. Clinical research to allow early diagnosis has mainly focused on fluid biomarkers, and genetic risk factors and markers. Stanford University School of Medicine, USA, has been concentrating on the former approach with the aim of eventually developing a simple blood test that would confirm the onset of AD several years before clinical symptoms were apparent. Initially researchers compared signalling proteins from the blood of patients with and without AD. Their more recent work uses animal models to compare neurons from the hippocampal formation, which are very vulnerable and die in the early stages of AD, with neurons which are not affected until the late stages of the disease. Several labs based in Europe are concentrating on finding cerebrospinal fluid markers present in the early stages of AD, such as total tau, phosphorylated tau and the 42 amino acid form of Aβ, which would allow early specific and sensitive diagnosis. The search for genetic markers has demonstrated that the genes APOE and PICALM consistently affect Aβ.
Early diagnosis, however, must be followed by effective treatment. Currently cholinesterase inhibitors and NMDA receptor antagonists are used to alleviate symptoms but are not curative. Sadly just before this year’s World Alzheimer’s day it was announced that two antibody drugs targetting Aβ, namely Bapineuzumab from Pfizer and Solanezumab from Eli Lilly, had proved to be no better than placebo in Phase III clinical trials. Last year the European Parliament called for dedicated plans to reduce the burden of AD; a new funding model to ensure that big pharma doesn’t withdraw from the AD challenge could be the most valuable strategy.
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