The Laryngoscope C 2013 The American Laryngological, V
Rhinological and Otological Society, Inc.
Application of an Electronic Nose in the Diagnosis of Head and Neck Cancer Nicoline Leunis, MD; Marie-Louise Boumans; Bernd Kremer, MD, PhD; Sinh Din, MD; Ellen Stobberingh, MD, PhD; Alfons G. H. Kessels, PhD; Kenneth W. Kross, MD, PhD Objectives/Hypothesis: Electronic nose (E-nose) technology has various applications such as the monitoring of air quality and the detection of explosive and chemical agents. We studied the diagnostic accuracy of volatile organic compounds (VOC) pattern analysis in exhaled breath by means of an E-nose in patients with head and neck squamous cell carcinoma (HNSCC). Study Design: Cohort study. Exhaled breath samples from patients with HNSCC were analyzed by using an E-Nose. Methods: Thirty-six patients diagnosed with HNSCC exhaled into a 5-litre Tedlar bag. The control group consisted of 23 patients visiting the outpatient clinic for other (benign) conditions. Air samples were analyzed using an E-nose. Results: Logistic regression showed a significant difference (P < 0.05) in VOC resistance patterns between patients diagnosed with HNSCC and the control group, with a sensitivity of 90% and a corresponding specificity of 80%. Conclusions: E-nose application holds a promising potential for application in the diagnosis of HNSCC due to its rapid, simple, and noninvasive nature. Key Words: Electronic nose, volatile organic compounds, head and neck cancer. Level of Evidence: 3b. Laryngoscope, 00:000–000, 2013
INTRODUCTION Head and Neck Cancer (HNC) is the sixth most common form of cancer worldwide, with an estimated 644,000 new cases of HNC diagnosed each year and almost two-thirds of these cases occurring in developing countries.1 HNC is one of the most common malignancies in some of these countries, mostly due to factors such as poor oral hygiene, chewing betel nuts, smoking, and drinking alcohol.2 At the same time, other factors such as genetic susceptibility and viral infections may play a role in the carcinogenesis of HNC.3 Head and neck squamous cell carcinoma (HNSCC) forms 90% of all HNCs and is usually treated with either surgery or radiotherapy or both. In spite of advances that have been made in the diagnosis and treatment of HNSCC, a From the Department of Otolaryngology–Head and Neck Surgery(N.L., B.K., S.D., K.W.K.); and Department of Medical Microbiology(M-L.B., E.S.); and Department of Clinical Epidemiology and Medical Technology Assessment(A.G.H.K.), Maastricht University Medical Centre, Maastricht, The Netherlands Editor’s Note: This Manuscript was accepted for publication October 7, 2013. Presented at the 82th meeting of the German Society for Otolaryngology–Head and Neck Surgery, Freiburg, Germany, June 1, 2011; and the 22th meeting of the Scandinavian Society for Head and Neck Oncology, Bergen, Norway, May 3, 2011. The authors have no funding, financial relationships, or conflicts of interest to disclose. Send correspondence to Kenneth W. Kross, MD, PhD, Department of Otolaryngology–Head and Neck Surgery, Maastricht University Medical Centre, P. Debyelaan 25, 6229HX, Maastricht, The Netherlands. E-mail: [email protected] DOI: 10.1002/lary.24463
Laryngoscope 00: Month 2013
significant improvement in survival rate over the last 20 years has not been observed.4 The highest 5-year survival rate is for lip cancer at 91%, while the survival rate for hypopharyngeal cancer is only 31%.5 HNSCC is typically diagnosed by performing a panendoscopy, which consists of rigid tracheobronchoscopy, rigid esophagoscopy, direct laryngoscopy, hypopharyngoscopy, and inspection and palpation of the oral cavity and the oropharynx—with a subsequent biopsy for histopathological examination under general anaesthesia. For biopsies taken from the larynx after radiation therapy, it has been shown that less than 50% of these procedures actually can show recurrence.6 Therefore, many of such procedures should be considered futile. They entail unnecessary hospital admittance; general anaesthesia; and a risk of the exacerbation of, for example, postradiotherapy changes.7 Exhaled human breath contains hundreds of volatile organic compounds (VOCs) that can be detected by gas chromatography and mass spectrometry (GC-MS) on the compound level8 and by pattern recognition with an Enose.9 E-nose technology is used in various applications, for instance, in the food and beverage industry,10 the monitoring of air quality,11 and the detection of explosive and chemical agents.12 The interaction of VOCs with an array of partial selective chemical sensors (equivalent to the olfactory receptors in the human nose) results in a resistance or conductance change of the sensors and is transmitted to a processor. Studies have shown that the use of E-nose technology has many useful potential applications, including the diagnosis of tuberculosis,13–16 renal Leunis et al.: Use of an E-Nose in Head and Neck Cancer
1
TABLE I. Demographics of Studied Groups. HNSCC
No. of Patients Age Sex, male/female Smokers Pack-years Localization
Control Group
36
23
41–78 (mean 59) 25/11
20–82 (mean 48) 10/13
36 (100%)
23 (100%)
5–50 (mean 34)
2–60 (mean 22)
Oropharynx
18 (50%)
Hypopharynx Supraglottic larynx
8 (22%) 10 (28%)
Conditions Ear operation Nose operation
12 2
Benign neck operation
3
Benign larynx operation Other*
4 2
*Snoring operation, tonsillectomy. HNSCC 5 head and neck squamous cell carcinoma.
dysfunction,17 urinary infection,18,19 chronic obstructive pulmonary disease (COPD), asthma, pneumonia, and lung cancer.20–32 To our knowledge, no studies have been performed to investigate the use of E-nose technology in the diagnostic process of HNSCC. Because the use of the E-nose is noninvasive, rapid, and cheap, it holds a great potential for use as a diagnostic tool in patients with HNSCC, providing a radical innovation in the care of HNSCC patients. Early diagnosis could lead to better radical treatment, less loss of function, and a higher survival rate. In this pilot study, we aimed to determine the diagnostic performance of the E-nose to diagnose HNSCC. Histopathological examination of the biopsies was considered the gold standard.
The E-nose is an electronic nose device incorporating 12 metal-oxide sensors, consisting of four different sensor types (CH4, CO, NOx, Pt) in triplicate. The E-nose is equipped with a pump; the inlet is controlled by a solenoid switching between two different inlets, facilitating an active airflow across the sensors. One inlet is connected to an active carbon filter to provide a baseline free from environmental influence; the second inlet is attached to the sample bag. The E-nose measures the air composition every 20 seconds using a 32-step sinusoidal modulation of the sensor surface temperature between 260 C and 340 C, thus resulting in a vector of 32 values every 20 seconds for each of the 12 sensors. A single measurement lasts 10 minutes: During the first 5 minutes, the exposure of the sensors to the breath sample is actively taken from the sample bag. During the last 5 minutes—the recovery phase—the adsorbed chemicals are released from the system under influence of the clean reference air. Thus, each single measurement is comprised of the chemical adsorption and desorption dynamics at the sensor surface. The main objective is not to define a specific VOC in the measurement but rather to determine the pattern of resistance changes in the sensors caused by the absorptions of the various VOCs in the breath of patients. This results in a graphic pattern specific for HNSCC.
Data Reduction and Analysis Data were downloaded from the E-nose and processed by a series of data reduction methods. First, potential pollution was filtered out by deleting the first and last section of the measurement. Normative values were obtained to minimize internal differences such as the age of sensors, whereby outcome
MATERIALS AND METHODS Patients This study was performed in a tertiary care referral hospital between March 2010 and January 2013. Thirty-six patients with histopathological confirmed HNSCC of the oropharynx, hypopharynx, or supraglottic larynx were included. All patients were smokers, had not previously undergone treatment, and ranged in age from 41 to78 years old. A control group consisting of 23 patients who were visiting the clinic for other, benign, conditions were smokers and ranged in age from 20 to 82 years old. See Table I for patient characteristics.
Breath Sampling All breath samples were taken in the hospital prior to surgery. No food had been consumed for at least 8 hours. Exhaled breath samples were collected by inflating a 5-litre Tedlar bag through multiple and repeated exhalations. Air samples were analyzed using an E-nose (DiagNose, C-it BV, Zutphen, the Netherlands). After collection, the sample bag was attached to the sample inlet of the E-nose.
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2
Fig. 1. Data points from sensor measurements were used to construct a ROC-curve. Logistic regression showed a significant difference (P < 0.05) in VOC resistance patterns between patients diagnosed with HNSCC and the control group, with a sensitivity of 90% and a specificity of 80%. HNSCC 5 head and neck squamous cell carcinoma; ROC 5 receiver operating characteristic; VOC 5 volatile organic compounds. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
Leunis et al.: Use of an E-Nose in Head and Neck Cancer
TABLE II. Results Per Patient (HNSCC Group). Patient
Localisation
TNM
Gender Pack Years False Negative
1 2
hypopharynx T4N0M0 M supraglottic T4aN2cM0 M
42 50
3
oropharynx
T1N2bM0
M
30
4 5
oropharynx supraglottic
T2N1M0 T3N0M0
M M
35 40
6
hypopharynx T3N2cM0
M
25
7 8
supraglottic T4aN2cM0 M hypopharynx T2N2bM0 M
50 5
9
hypopharynx T4N2cM0
M
50
false negative
10 11
oropharynx oropharynx
T2N2bM1 T1N2aM0
F F
20 40
false negative
12
oropharynx
T3N0M0
M
5
13 14
oropharynx T4aN2cM0 M hypopharynx T1N3M0 M
40 50
15
oropharynx
T4N0M0
M
35
16 17
oropharynx oropharynx
T4N0M0 T1N0M0
M M
30 40
18
supraglottic
T2N0M0
M
40
19 20
oropharynx oropharynx
T3N2bM0 T3N2bM0
M F
40 30
21
supraglottic
T3N1M0
F
50
22 23
hypopharynx T4aN2bM0 F oropharynx T2N2bM0 M
35 30
24
supraglottic
TisN0M0
F
30
25 26
oropharynx supraglottic
T4bN2cM0 F T2N0M0 F
20 30
27
oropharynx
T1N0M0
M
30
28 29
oropharynx supraglottic
T1N1M0 T4N0M0
M M
25 65
30
supraglottic
T1N0M0
M
70
31 32
orpharynx oropharynx
T3N2bM0 T2N0M0
M F
60 30
33
oropharynx
T4N2bM0
F
40
34 35
hypopharynx T1N2bM0 hypopharynx T3N2cM0
M M
30 30
36
supraglottic
F
45
T3N2cM0
false negative
measurements per time point were divided by the delta from the temperature cycle data minus the lowest value of that cycle. Then, the area under the time curve (AUC) per sensor was calculated. Because each sensor is present in triplicate, the mean value per sensor type was calculated. This resulted in 32 values for each of the four sensors; thus, a total of 128 values. These data points were analyzed using logistic regression (method forward selection) using the statistical program SPSS version 18 (SPSS Inc., Chicago, IL). The outcomes were used to construct a ROC-curve (Fig. 1). Internal validation was conducted using a bootstrap procedure in STATA version 11 (StataCorp, College Station, TX).
Separate logistic regression analysis of all four sensors showed that each was able to distinguish between HNSCC patients and the control group. In total, we found 11 AUC values of sensor CH4, 17 AUC values of sensor CO, 20 AUC values of sensor NOx, and 16 AUC values of sensor Pt, indicating a significant difference (P < 0,05) in resistance pattern between the VOCs from the HNSCC patients compared with the control group. To sidestep colinearity problems between these 30 AUC values, just one AUC value (from sensor NOx) was modeled with logistic regression. There were three patients in the study group with a false negative test using logistic regression modeling: one patient with an oropharynx carcinoma with distant metastasis, one patient with a hypopharynx carcinoma, and one patient with a supraglottic carcinoma. In the control group, there were four patients with false positive test results. These patients had diverse diagnoses. Results per patient are shown in Tables II and III. The ROC curve obtained (Fig. 1) had an area under the curve of 0.89 (95% CI 0.79–0.98). Sensitivity of 90% and specificity of 80% was calculated. Upon applying bootstrapping, the area under the curve decreased only minimally to 0.85 (95% CI 0.75–0.95).
TABLE III. Results per Patient (Control Group). Patient
The collection of breath samples did not result in any adverse effects. Laryngoscope 00: Month 2013
Gender
Pack Years
False Positive
1
tympanoplasty
F
5
2 3
BAHA cochleair implantation
F F
30 30
4
uvulopalatoplasty
M
30
5 6
cochleair implantation septum correction
F M
35 35
7
cochleair implantation
M
30
8 9
BAHA septum correction
F F
15 5
10
BAHA
M
15
11 12
Reinkes edema* tympanoplasty
F M
10 30
13
tympanoplasty
F
5
14 15
laryngeal polyp* tympanoplasty
F F
5 30
16
tympanoplasty
M
5
17
F
30
false positive
18
parotidectomy (Whartin tumor) laryngocele*
M
50
false positive
19
tonsillectomy
F
2
20 21
tympanoplasty M parotidectomy M (pleiomorf adenoma) epiglottitis M
10 30
submandibulectomy (sialolithiasis)
5
22 23
RESULTS
Condition
F
false positive
60 false positive
*Microlaryngoscopy was performed. BAHA 5 bone anchored hearing aid implantation.
Leunis et al.: Use of an E-Nose in Head and Neck Cancer
3
DISCUSSION In this pilot study, we studied the feasibility and potential of an E-nose for diagnosing HNSCC in a clinical setting. The E-nose was able to distinguish between HNSCC and benign conditions with 90% sensitivity and 80% specificity by means of VOCs pattern recognition. Internal validation resulted in a negligible decrease in the area under the ROC-curve, indicating an acceptable internal validity. To our knowledge, this is the first study about the use of E-nose technology in the diagnostic process of HNSCC. The main aim of this study was to obtain a proof of concept. To minimize the differences between our study group and the control group, all included subjects were smokers and were sober at the time of breath sampling. Previous studies had already shown that a distinctive profile of VOCs in breath can discriminate between health and disease. Philips et al.20 detected lung cancer by using gas chromatography and mass spectroscopy (GC-MS). Dragonieri et al.29 have shown that VOC patterns of exhaled breath discriminate between patients with lung cancer from COPD patients as well as from healthy controls. In the field of otolaryngology, Shykhon et al.33 have used an E-nose to discriminate between various types of ENT pathology, including otitis externa, chronic suppurative otitis media, and nasal vestibulitis, with an overall 88% accuracy. Thaler et al.34 and Bruno et al.35 used an E-nose in the diagnosis of respiratory bacterial sinusitis and chronic rhinosinusitis (classification rate 72%). One pilot study of Schmutzhard et al.36 showed a statistically significant difference between a HNSCC group and a control group by means of proton transfer reaction mass spectrometry (PTR-MS). However, PTR-MS is an expensive, immobile tool that is not suited for bedside diagnostic analysis. What distinguishes E-nose technology from gas chromatography and other methods is that it does not measure a specific VOC; rather it evokes resistance pattern changes caused by an adherence of the VOCs to the sensors. As such, it is not possible to define a specific VOC responsible for the outcome found in this study. However, this might not be that important when considering the usefulness of this device in clinical practice. To gain insight into the biologic nature of this tumor, studies focused on defining the specific marker(s) produced in HNSCC would be useful and would contribute to the future development of devices such as the E-nose.
CONCLUSION E-nose is a small, portable, rapid, low cost, and noninvasive instrument. The high values of sensitivity and specificity obtained indicate the usefulness of this device in the diagnostic process of HNSCC. More studies will be needed to establish protocols for the use of the E-nose in a clinical setting.
BIBLIOGRAPHY 1. Marur S, Forastiere AA. Head and neck cancer: changing epidemiology, diagnosis, and treatment. Mayo Clin Proc 2008;83:489–501. 2. Wei WI. Commentary: head and neck carcinomas in the developing world. BMJ 2002;325:827.
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3. Johnson N. Tobacco use and oral cancer: a global perspective. J Dent Educ 2001;65:328–339. 4. Mork J, Glattre E. Squamous cell carcinomas of the head and neck in Norway, 1953–1992: an epidemiologic study of a low-risk population. Cancer Causes Control 1998;9:37–48. 5. McMahon S, Chen AY. Head and neck cancer. Cancer Metastasis Rev 2003; 22:21–24. 6. de Bree R, van der Putten L, Hoekstra OS, et al. A randomized trial of PET scanning to improve diagnostic yield of direct laryngoscopy in patients with suspicion of recurrent laryngeal carcinoma after radiotherapy. Contemp Clin Trials 2007;28:705–712. 7. Zbaren P, Weidner S, Thoeny HC. Laryngeal and hypopharyngeal carcinomas after (chemo) radiotherapy: a diagnostic dilemma. Curr Opin Otolaryngol Head Neck Surg 2008;16:147–153. 8. Pauling L, Robinson AB, Teranishi R, Cary P. Quantitative analysis of urine vapor and breath by gas-liquid partition chromatography. Proc Natl Acad Sci U S A 1971;68:2374–2376. 9. Persaud K, Dodd G. Analysis of discrimination mechanisms in the mammalian olfactory system using a model nose. Nature 1982;299:352–355. 10. Peris M, Escuder-Gilabert L. A 21st century technique for food control: electronic noses. Anal Chim Acta 2009;638:1–15. 11. Kramer KE, Rose-Pehrsson SL, Hammond MH, Tillett D, Streckert HH. Detection and classification of gaseous sulfur compounds by solid electrolyte cyclic voltammetry of cermet sensor array. Anal Chim Acta 2007; 584:78–88. 12. Fernandes DL, Gomes MT. Development of an electronic nose to identify and quantify volatile hazardous compounds. Talanta 2008;77:77–83. 13. Pavlou AK, Magan N, Jones JM, Brown J, Klatser P, Turner AP. Detection of mycobacterium tuberculosis (TB) in vitro and in situ using an electronic nose in combination with a neural network system. Biosens Bioelectron 2004;20:538–544. 14. Fend R, Kolk AH, Bessant C, Buijtels P, Klatser PR, Woodman AC. Prospects for clinical application of electronic-nose technology to early detection of Mycobacterium tuberculosis in culture and sputum. J Clin Microbiol 2006;44:2039–2045. 15. Kolk A, Hoelscher M, Maboko L, et al. Electronic-nose technology using sputum samples in diagnosis of patients with tuberculosis. J Clin Microbiol 2010;48:4235–4238. 16. Bruins M, Rahim Z, Bos A, van de Sande WW, Endtz HP, van Belkum A. Diagnosis of active tuberculosis by e-nose analysis of exhaled air. Tuberculosis (Edinb) 2013;93:232–228. doi: 10.1016/j.tube.2012.10.002. Epub 2012. 17. Voss A, Baier V, Reisch R, et al. Smelling renal dysfunction via electronic nose. Ann Biomed Eng 2005;33:656–660. 18. Pavlou AK, Magan N, McNulty C, et al. Use of an electronic nose system for diagnoses of urinary tract infections. Biosens Bioelectron 2002;17: 893–899. 19. Guernion N, Ratcliffe NM, Spencer-Phillips PT, Howe RA. Identifying bacteria in human urine: current practice and the potential for rapid, nearpatient diagnosis by sensing volatile organic compounds. Clin Chem Lab Med 2001;39:893–906. 20. Phillips M, Gleeson K, Hughes JM, et al. Volatile organic compounds in breath as markers of lung cancer: a cross-sectional study. Lancet 1999; 353:1930–1933. 21. Di Natale C, Macagnano A, Martinelli E, et al. Lung cancer identification by the analysis of breath by means of an array of non-selective gas sensors. Biosens Bioelectron 2003;18:1209–1218. 22. Hanson CW, 3rd, Thaler ER. Electronic nose prediction of a clinical pneumonia score: biosensors and microbes. Anesthesiology 2005;102:63–68. 23. Hockstein NG, Thaler ER, Lin Y, Lee DD, Hanson CW. Correlation of pneumonia score with electronic nose signature: A prospective study. Ann Otol Rhinol Laryngol 2005;114:504–508. 24. Hockstein NG, Thaler ER, Torigian D, Miller WT Jr, Deffenderfer O, Hanson CW. Diagnosis of pneumonia with an electronic nose: correlation of vapor signature with chest computed tomography scan findings. Laryngoscope 2004;114:1701–1705. 25. Machado RF, Laskowski D, Deffenderfer O, et al. Detection of lung cancer by sensor array analyses of exhaled breath. Am J Respir Crit Care Med 2005;171:1286–1291. 26. Chen X, Xu F, Wang Y, et al. A study of the volatile organic compounds exhaled by lung cancer cells in vitro for breath diagnosis. Cancer 2007; 110:835–844. 27. Gendron KB, Hockstein NG, Thaler ER, Vachani A, Hanson CW. In vitro discrimination of tumor cell lines with an electronic nose. Otolaryngol Head Neck Surg 2007;137:269–273. 28. Dragonieri S, Schot R, Mertens BJ, et al. An electronic nose in the discrimination of patients with asthma and controls. J Allergy Clin Immunol 2007;120:856–862. 29. Dragonieri S, Annema JT, Schot R, et al. An electronic nose in the discrimination of patients with non-small cell lung cancer and COPD. Lung Cancer 2009; 64:166–170. doi: 10.1016/j.lungcan.2008.08.008. Epub 2008. 30. Fens N, Zwinderman AH, van der Schee MP, et al. Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma. Am J Respir Crit Care Med 2009;180:1076–1082. 31. D’Amico A, Pennazza G, Santonico M, et al. An investigation on electronic nose diagnosis of lung cancer. Lung Cancer 2010;68:170–176. 32. Montuschi P, Santonico M, Mondino C, et al. Diagnostic performance of an electronic nose, fractional exhaled nitric oxide, and lung function testing in asthma. Chest 2010;137:790–796.
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33. Shykhon ME, Morgan DW, Dutta R, Hines EL, Gardner JW. Clinical evaluation of the electronic nose in the diagnosis of ear, nose and throat infection: a preliminary study. J Laryngol Otol 2004;118:706– 709. 34. Thaler ER, Hanson CW. Use of an electronic nose to diagnose bacterial sinusitis. Am J Rhinol 2006;20:170–172.
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35. Bruno E, Alessandrini M, Ottaviani F, et al. Can the electronic nose diagnose chronic rhinosinusitis? A new experimental study. Eur Arch Otorhinolaryngol 2008;265:425–428. 36. Schmutzhard J, Rieder J, Deibl M, et al. Pilot study: volatile organic compounds as a diagnostic marker for head and neck tumors. Head Neck 2008;30:743–749.
Leunis et al.: Use of an E-Nose in Head and Neck Cancer
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Rhinological and Otological Society, Inc.
Application of an Electronic Nose in the Diagnosis of Head and Neck Cancer Nicoline Leunis, MD; Marie-Louise Boumans; Bernd Kremer, MD, PhD; Sinh Din, MD; Ellen Stobberingh, MD, PhD; Alfons G. H. Kessels, PhD; Kenneth W. Kross, MD, PhD Objectives/Hypothesis: Electronic nose (E-nose) technology has various applications such as the monitoring of air quality and the detection of explosive and chemical agents. We studied the diagnostic accuracy of volatile organic compounds (VOC) pattern analysis in exhaled breath by means of an E-nose in patients with head and neck squamous cell carcinoma (HNSCC). Study Design: Cohort study. Exhaled breath samples from patients with HNSCC were analyzed by using an E-Nose. Methods: Thirty-six patients diagnosed with HNSCC exhaled into a 5-litre Tedlar bag. The control group consisted of 23 patients visiting the outpatient clinic for other (benign) conditions. Air samples were analyzed using an E-nose. Results: Logistic regression showed a significant difference (P < 0.05) in VOC resistance patterns between patients diagnosed with HNSCC and the control group, with a sensitivity of 90% and a corresponding specificity of 80%. Conclusions: E-nose application holds a promising potential for application in the diagnosis of HNSCC due to its rapid, simple, and noninvasive nature. Key Words: Electronic nose, volatile organic compounds, head and neck cancer. Level of Evidence: 3b. Laryngoscope, 00:000–000, 2013
INTRODUCTION Head and Neck Cancer (HNC) is the sixth most common form of cancer worldwide, with an estimated 644,000 new cases of HNC diagnosed each year and almost two-thirds of these cases occurring in developing countries.1 HNC is one of the most common malignancies in some of these countries, mostly due to factors such as poor oral hygiene, chewing betel nuts, smoking, and drinking alcohol.2 At the same time, other factors such as genetic susceptibility and viral infections may play a role in the carcinogenesis of HNC.3 Head and neck squamous cell carcinoma (HNSCC) forms 90% of all HNCs and is usually treated with either surgery or radiotherapy or both. In spite of advances that have been made in the diagnosis and treatment of HNSCC, a From the Department of Otolaryngology–Head and Neck Surgery(N.L., B.K., S.D., K.W.K.); and Department of Medical Microbiology(M-L.B., E.S.); and Department of Clinical Epidemiology and Medical Technology Assessment(A.G.H.K.), Maastricht University Medical Centre, Maastricht, The Netherlands Editor’s Note: This Manuscript was accepted for publication October 7, 2013. Presented at the 82th meeting of the German Society for Otolaryngology–Head and Neck Surgery, Freiburg, Germany, June 1, 2011; and the 22th meeting of the Scandinavian Society for Head and Neck Oncology, Bergen, Norway, May 3, 2011. The authors have no funding, financial relationships, or conflicts of interest to disclose. Send correspondence to Kenneth W. Kross, MD, PhD, Department of Otolaryngology–Head and Neck Surgery, Maastricht University Medical Centre, P. Debyelaan 25, 6229HX, Maastricht, The Netherlands. E-mail: [email protected] DOI: 10.1002/lary.24463
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significant improvement in survival rate over the last 20 years has not been observed.4 The highest 5-year survival rate is for lip cancer at 91%, while the survival rate for hypopharyngeal cancer is only 31%.5 HNSCC is typically diagnosed by performing a panendoscopy, which consists of rigid tracheobronchoscopy, rigid esophagoscopy, direct laryngoscopy, hypopharyngoscopy, and inspection and palpation of the oral cavity and the oropharynx—with a subsequent biopsy for histopathological examination under general anaesthesia. For biopsies taken from the larynx after radiation therapy, it has been shown that less than 50% of these procedures actually can show recurrence.6 Therefore, many of such procedures should be considered futile. They entail unnecessary hospital admittance; general anaesthesia; and a risk of the exacerbation of, for example, postradiotherapy changes.7 Exhaled human breath contains hundreds of volatile organic compounds (VOCs) that can be detected by gas chromatography and mass spectrometry (GC-MS) on the compound level8 and by pattern recognition with an Enose.9 E-nose technology is used in various applications, for instance, in the food and beverage industry,10 the monitoring of air quality,11 and the detection of explosive and chemical agents.12 The interaction of VOCs with an array of partial selective chemical sensors (equivalent to the olfactory receptors in the human nose) results in a resistance or conductance change of the sensors and is transmitted to a processor. Studies have shown that the use of E-nose technology has many useful potential applications, including the diagnosis of tuberculosis,13–16 renal Leunis et al.: Use of an E-Nose in Head and Neck Cancer
1
TABLE I. Demographics of Studied Groups. HNSCC
No. of Patients Age Sex, male/female Smokers Pack-years Localization
Control Group
36
23
41–78 (mean 59) 25/11
20–82 (mean 48) 10/13
36 (100%)
23 (100%)
5–50 (mean 34)
2–60 (mean 22)
Oropharynx
18 (50%)
Hypopharynx Supraglottic larynx
8 (22%) 10 (28%)
Conditions Ear operation Nose operation
12 2
Benign neck operation
3
Benign larynx operation Other*
4 2
*Snoring operation, tonsillectomy. HNSCC 5 head and neck squamous cell carcinoma.
dysfunction,17 urinary infection,18,19 chronic obstructive pulmonary disease (COPD), asthma, pneumonia, and lung cancer.20–32 To our knowledge, no studies have been performed to investigate the use of E-nose technology in the diagnostic process of HNSCC. Because the use of the E-nose is noninvasive, rapid, and cheap, it holds a great potential for use as a diagnostic tool in patients with HNSCC, providing a radical innovation in the care of HNSCC patients. Early diagnosis could lead to better radical treatment, less loss of function, and a higher survival rate. In this pilot study, we aimed to determine the diagnostic performance of the E-nose to diagnose HNSCC. Histopathological examination of the biopsies was considered the gold standard.
The E-nose is an electronic nose device incorporating 12 metal-oxide sensors, consisting of four different sensor types (CH4, CO, NOx, Pt) in triplicate. The E-nose is equipped with a pump; the inlet is controlled by a solenoid switching between two different inlets, facilitating an active airflow across the sensors. One inlet is connected to an active carbon filter to provide a baseline free from environmental influence; the second inlet is attached to the sample bag. The E-nose measures the air composition every 20 seconds using a 32-step sinusoidal modulation of the sensor surface temperature between 260 C and 340 C, thus resulting in a vector of 32 values every 20 seconds for each of the 12 sensors. A single measurement lasts 10 minutes: During the first 5 minutes, the exposure of the sensors to the breath sample is actively taken from the sample bag. During the last 5 minutes—the recovery phase—the adsorbed chemicals are released from the system under influence of the clean reference air. Thus, each single measurement is comprised of the chemical adsorption and desorption dynamics at the sensor surface. The main objective is not to define a specific VOC in the measurement but rather to determine the pattern of resistance changes in the sensors caused by the absorptions of the various VOCs in the breath of patients. This results in a graphic pattern specific for HNSCC.
Data Reduction and Analysis Data were downloaded from the E-nose and processed by a series of data reduction methods. First, potential pollution was filtered out by deleting the first and last section of the measurement. Normative values were obtained to minimize internal differences such as the age of sensors, whereby outcome
MATERIALS AND METHODS Patients This study was performed in a tertiary care referral hospital between March 2010 and January 2013. Thirty-six patients with histopathological confirmed HNSCC of the oropharynx, hypopharynx, or supraglottic larynx were included. All patients were smokers, had not previously undergone treatment, and ranged in age from 41 to78 years old. A control group consisting of 23 patients who were visiting the clinic for other, benign, conditions were smokers and ranged in age from 20 to 82 years old. See Table I for patient characteristics.
Breath Sampling All breath samples were taken in the hospital prior to surgery. No food had been consumed for at least 8 hours. Exhaled breath samples were collected by inflating a 5-litre Tedlar bag through multiple and repeated exhalations. Air samples were analyzed using an E-nose (DiagNose, C-it BV, Zutphen, the Netherlands). After collection, the sample bag was attached to the sample inlet of the E-nose.
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Fig. 1. Data points from sensor measurements were used to construct a ROC-curve. Logistic regression showed a significant difference (P < 0.05) in VOC resistance patterns between patients diagnosed with HNSCC and the control group, with a sensitivity of 90% and a specificity of 80%. HNSCC 5 head and neck squamous cell carcinoma; ROC 5 receiver operating characteristic; VOC 5 volatile organic compounds. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
Leunis et al.: Use of an E-Nose in Head and Neck Cancer
TABLE II. Results Per Patient (HNSCC Group). Patient
Localisation
TNM
Gender Pack Years False Negative
1 2
hypopharynx T4N0M0 M supraglottic T4aN2cM0 M
42 50
3
oropharynx
T1N2bM0
M
30
4 5
oropharynx supraglottic
T2N1M0 T3N0M0
M M
35 40
6
hypopharynx T3N2cM0
M
25
7 8
supraglottic T4aN2cM0 M hypopharynx T2N2bM0 M
50 5
9
hypopharynx T4N2cM0
M
50
false negative
10 11
oropharynx oropharynx
T2N2bM1 T1N2aM0
F F
20 40
false negative
12
oropharynx
T3N0M0
M
5
13 14
oropharynx T4aN2cM0 M hypopharynx T1N3M0 M
40 50
15
oropharynx
T4N0M0
M
35
16 17
oropharynx oropharynx
T4N0M0 T1N0M0
M M
30 40
18
supraglottic
T2N0M0
M
40
19 20
oropharynx oropharynx
T3N2bM0 T3N2bM0
M F
40 30
21
supraglottic
T3N1M0
F
50
22 23
hypopharynx T4aN2bM0 F oropharynx T2N2bM0 M
35 30
24
supraglottic
TisN0M0
F
30
25 26
oropharynx supraglottic
T4bN2cM0 F T2N0M0 F
20 30
27
oropharynx
T1N0M0
M
30
28 29
oropharynx supraglottic
T1N1M0 T4N0M0
M M
25 65
30
supraglottic
T1N0M0
M
70
31 32
orpharynx oropharynx
T3N2bM0 T2N0M0
M F
60 30
33
oropharynx
T4N2bM0
F
40
34 35
hypopharynx T1N2bM0 hypopharynx T3N2cM0
M M
30 30
36
supraglottic
F
45
T3N2cM0
false negative
measurements per time point were divided by the delta from the temperature cycle data minus the lowest value of that cycle. Then, the area under the time curve (AUC) per sensor was calculated. Because each sensor is present in triplicate, the mean value per sensor type was calculated. This resulted in 32 values for each of the four sensors; thus, a total of 128 values. These data points were analyzed using logistic regression (method forward selection) using the statistical program SPSS version 18 (SPSS Inc., Chicago, IL). The outcomes were used to construct a ROC-curve (Fig. 1). Internal validation was conducted using a bootstrap procedure in STATA version 11 (StataCorp, College Station, TX).
Separate logistic regression analysis of all four sensors showed that each was able to distinguish between HNSCC patients and the control group. In total, we found 11 AUC values of sensor CH4, 17 AUC values of sensor CO, 20 AUC values of sensor NOx, and 16 AUC values of sensor Pt, indicating a significant difference (P < 0,05) in resistance pattern between the VOCs from the HNSCC patients compared with the control group. To sidestep colinearity problems between these 30 AUC values, just one AUC value (from sensor NOx) was modeled with logistic regression. There were three patients in the study group with a false negative test using logistic regression modeling: one patient with an oropharynx carcinoma with distant metastasis, one patient with a hypopharynx carcinoma, and one patient with a supraglottic carcinoma. In the control group, there were four patients with false positive test results. These patients had diverse diagnoses. Results per patient are shown in Tables II and III. The ROC curve obtained (Fig. 1) had an area under the curve of 0.89 (95% CI 0.79–0.98). Sensitivity of 90% and specificity of 80% was calculated. Upon applying bootstrapping, the area under the curve decreased only minimally to 0.85 (95% CI 0.75–0.95).
TABLE III. Results per Patient (Control Group). Patient
The collection of breath samples did not result in any adverse effects. Laryngoscope 00: Month 2013
Gender
Pack Years
False Positive
1
tympanoplasty
F
5
2 3
BAHA cochleair implantation
F F
30 30
4
uvulopalatoplasty
M
30
5 6
cochleair implantation septum correction
F M
35 35
7
cochleair implantation
M
30
8 9
BAHA septum correction
F F
15 5
10
BAHA
M
15
11 12
Reinkes edema* tympanoplasty
F M
10 30
13
tympanoplasty
F
5
14 15
laryngeal polyp* tympanoplasty
F F
5 30
16
tympanoplasty
M
5
17
F
30
false positive
18
parotidectomy (Whartin tumor) laryngocele*
M
50
false positive
19
tonsillectomy
F
2
20 21
tympanoplasty M parotidectomy M (pleiomorf adenoma) epiglottitis M
10 30
submandibulectomy (sialolithiasis)
5
22 23
RESULTS
Condition
F
false positive
60 false positive
*Microlaryngoscopy was performed. BAHA 5 bone anchored hearing aid implantation.
Leunis et al.: Use of an E-Nose in Head and Neck Cancer
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DISCUSSION In this pilot study, we studied the feasibility and potential of an E-nose for diagnosing HNSCC in a clinical setting. The E-nose was able to distinguish between HNSCC and benign conditions with 90% sensitivity and 80% specificity by means of VOCs pattern recognition. Internal validation resulted in a negligible decrease in the area under the ROC-curve, indicating an acceptable internal validity. To our knowledge, this is the first study about the use of E-nose technology in the diagnostic process of HNSCC. The main aim of this study was to obtain a proof of concept. To minimize the differences between our study group and the control group, all included subjects were smokers and were sober at the time of breath sampling. Previous studies had already shown that a distinctive profile of VOCs in breath can discriminate between health and disease. Philips et al.20 detected lung cancer by using gas chromatography and mass spectroscopy (GC-MS). Dragonieri et al.29 have shown that VOC patterns of exhaled breath discriminate between patients with lung cancer from COPD patients as well as from healthy controls. In the field of otolaryngology, Shykhon et al.33 have used an E-nose to discriminate between various types of ENT pathology, including otitis externa, chronic suppurative otitis media, and nasal vestibulitis, with an overall 88% accuracy. Thaler et al.34 and Bruno et al.35 used an E-nose in the diagnosis of respiratory bacterial sinusitis and chronic rhinosinusitis (classification rate 72%). One pilot study of Schmutzhard et al.36 showed a statistically significant difference between a HNSCC group and a control group by means of proton transfer reaction mass spectrometry (PTR-MS). However, PTR-MS is an expensive, immobile tool that is not suited for bedside diagnostic analysis. What distinguishes E-nose technology from gas chromatography and other methods is that it does not measure a specific VOC; rather it evokes resistance pattern changes caused by an adherence of the VOCs to the sensors. As such, it is not possible to define a specific VOC responsible for the outcome found in this study. However, this might not be that important when considering the usefulness of this device in clinical practice. To gain insight into the biologic nature of this tumor, studies focused on defining the specific marker(s) produced in HNSCC would be useful and would contribute to the future development of devices such as the E-nose.
CONCLUSION E-nose is a small, portable, rapid, low cost, and noninvasive instrument. The high values of sensitivity and specificity obtained indicate the usefulness of this device in the diagnostic process of HNSCC. More studies will be needed to establish protocols for the use of the E-nose in a clinical setting.
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