104473454 Smells Like Cancer Electronic Noses and Disease Diagnostics

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Smells Like Cancer

Electronic Noses and Disease Diagnostics


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Abstract

The scents associated with different disease states have been documented for millennia.

Research in more recent times has focussed on the design of electronic noses based on sensor

array technology which can be used in the diagnosis of disease through the identification of

volatile organic biomarkers. This literature review focuses on the use of electronic noses in

the detection of cancers, with an emphasis on the identification of lung cancer. The

technology involved in the development of electronic noses is outlined and compared with

human olfaction systems in an attempt to describe how the technology works, particularly in

relation to its use in the diagnosis of cancer. The review will concentrate on giving an

overview of the research in this area to date and will attempt to identify areas which could

benefit from further investigation.


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Table of Contents

Page Section

3 Introduction

5 Olfactory Mechanisms

9 Volatile Organic Compounds and Human Breath

11 The Link between VOCs and Lung Cancer

13 Application of the Electronic Nose

14 Diagnostic Mechanisms

15 Conclusion

16 References


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Introduction

Since medical practice has been documented, reference has been made to the smell associated

with particular conditions. In modern medicine smell still acts as a warning precursor to the

presence of conditions such as diabetes, with the characteristic acetone scent which

accompanies the illness easily recognisable outside the community of medical professionals.

For many years scientists wondered at the potential of developing systems which would act

as human noses do but with a much higher level of specificity. The first electronic nose was

developed in the Jet Propulsion Lab (JPL) in NASA in 1982. It was borne out of the necessity

to protect astronauts on space shuttle missions, where the build-up of ammonia and other

volatile organic compounds (VOCs) within the confined space of a space shuttle was

identified as having potentially lethal consequences for the team. VOCs are essentially the

fumes or vapours given off by compounds but they may not be detected by the human nose,

for example in the case of odourless carbon monoxide poisoning. In the case of the NASA

experiment, ammonia levels were being monitored. By the time that the levels had built up to

the extent to which the team could physically smell them, the saturation levels were already

dangerously high. The need for an alternative which could act as an early warning system

was clear. Engineers working in the JPL decided to experiment with sensors and attempted to

replicate the principles involved in a human olfaction system with them. The result was a

highly sensitive instrument which could detect tiny quantities of VOCs using a handheld

detector linked to a micro PC (NASA 2011, www.enose.jpl.nasa.gov).

As an increasing body of research into the development of electronic noses was published,

researchers involved in the study of Chronic Obstructive Pulmonary Disease (COPD) became

interested in adapting the technology in an attempt to identify those people at risk of chronic

and debilitating lung disease. Early intervention could lead to a positive outcome for patients,

but a non-invasive method for detection and monitoring of the condition had so far evaded

researchers. At that time Gas Chromatography (GC), either alone or coupled with Mass

Spectrometry (GC/MS), was used to test breath samples in the diagnosis of certain conditions

such as COPD, asthma and post-operative infections. Experimental data had already shown

that GC/MS could identify complex volatile compounds associated with particular bacteria

which could, in turn, be used to pin-point disease. However this technology is expensive so

researchers decided to investigate how the sensor technology used by NASA in developing

its JPL nose could be adapted for use in a clinical setting.


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Studies involving the use of dogs to sniff out cancer tumours were emerging. The dogs had

been used in studies which focused on melanomas (Pickel et al. 2004), bladder cancer (Willis

et al. 2004), breast cancer and lung cancer (McCullough et al. 2006). These studies indicated

the enormous potential of the use of so-called smellprints in the diagnosis of chronic illnesses

at an early stage without invasive tests. Smellprints, also known as biomarkers, are essentially

the fingerprints left by volatile organic compounds. Each VOC stimulates a different sensor

within a sensor array system. This in turn produces a characteristic pattern and when

compared with the patterns of known biomarkers in a library or database the readout

produced can be interpreted with ease (Hovarth 2010; Wilson & Baietto 2011).

Thirteen electronic noses have been developed by 10 different companies worldwide, with a

further two in development at March 2011. These e-noses have a variety of applications

across industries. For example, the Bloodhound BH-114 is used to detect toxins in water

samples and food spoilage; the Sacmi Imola is used to detect the presence of bacteria in corn

crops and the Fox 4000 is used to analyse flavours in the biopharmaceutical industry in order

to make medications more palatable (Wilson & Baietto 2011).

The purpose of this literature review is not to assess all of the applications of e-nose

technology. It will focus specifically on the detection of cancers with an emphasis on the use

of electronic noses to identify lung cancer. The actual technology involved in the

development of electronic noses will not be discussed in detail, but will be outlined and

compared with human olfaction systems in an attempt to describe how the technology works,

particularly in relation to its use in the diagnosis of cancer. The review will concentrate on

giving an overview of the research in this area to date and will attempt to identify areas which

could benefit from further investigation.


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Olfactory Mechanisms

Human Olfactory Mechanism

The human olfactory system is incredibly complex but, despite being the focus of major

research including a gene-mapping project (Buck & Axel 1991) which resulted in a Nobel

Prize for Linda Buck and Richard Axel, many aspects of the human olfactory process are still

not understood.

The human olfactory system is duplex in nature. The Primary Olfactory system is that which

we rely upon to detect odours which are in the gaseous or solid phase. The Accessory

Olfactory system is used to detect pheromones, or fluid-phase olfactory stimuli. This review

deals with those odours detected by the primary olfactory system, most notably VOCs.

When we breathe in, the molecules which produce odours pass through the nasal passages

into the nasal cavity, which is lined with the olfactory epithelium containing olfactory

sensory neurons. Odour molecules dissolve in mucus as they enter the cavity. The dendrites

of the sensory neurons have olfactory receptors which bind the odour molecules to

transmembrane proteins. This binding results in an action potential which stimulates the

receptor neuron. The receptor neurons stimulate axons in the olfactory nerve, sending a signal

through the cribriform plate to the olfactory bulb and on to the olfactory cortex in the brain.

There are some ten million sensory receptor cells in the nasal cavity. Receptors in the same

area seem to detect very similar odour molecules. Some receptors are specific to one odour

molecule, others respond to a group of molecules (Davide et al. 2003).However, within the

brain it appears that information from olfactory stimuli are passed to other sensory receptors,

particularly those in the hippocampus which are involved in emotional responses. Odour

information is stored within the brain as long-term memory, allowing each signal to be

interpreted as a specific smell which we associate with visual, gustatory, emotional and

other stimuli.

Artificial Olfaction Mechanisms

Artificial Olfaction systems, more commonly known as Electronic Noses, have been

designed to mimic the human olfactory system in a very simplistic manner. The systems are


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quite diverse in nature but have a common end-goal: the effective detection and interpretation

of odour molecules in order to recognise, or identify, a smell.

Comparing Human and Artificial Olfaction Mechanisms

A diagrammatic comparison between human and electronic olfaction systems has been

characterised by Turner (2010) and is shown below (Fig. 1):

The diagram above clearly shows the mimicry involved in the development of an electronic

nose. The odours produced by odour molecules are detected using a sensor array consisting

of a number of sensors, each programmed to recognise a particular odour molecule or cluster

of such molecules. When the molecule is present, the sensor is stimulated and sends an

electrical signal to a processor which is programmed algorithmically to recognise and

interpret patterns which, in turn, can be identified as a mixture of volatile compounds.

Unlike human noses, however, electronic noses will normally have fewer than 100 sensors,

significantly less than the 10,000,000 found in the olfactory epithelium. Overlap in the

molecules they detect is built into the system, just as there is overlap in a human olfactory

system. The effect of this overlap is much less than that in the human system however,

because the sensors can be chosen in a very selective manner (Davide et al. 2003). A

summary comparison of the features of human and electronic olfactory systems has been

produced by Davide et al. and is included below (Table 1).

Fig.1: Comparison between human and artificial olfaction systems.

(Turner et al., 2010 in Nature Reviews: Microbiology)


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Table 1: Summary of the Features of Human and Electronic Noses

Human Electronic

10 million receptors, self generated 5-100 chemical sensors, manually replaced

10-100 selectivity classes 5-100 selectivity pattern

Initial reduction of number of signals

(1000 to 1)

smart sensor arrays can mimic this?

Adaptive Perhaps possible

Saturates Persistent

Signal treatment in real time Pattern recognition hardware may do this

Identifies a large number of odours Has to be trained for each application

Cannot detect some simple molecules Can detect also simple molecules (H2, H2O, CO, CO2)

Detects some specific molecules Not possible in general at very low concentrations

Associative with sound, vision, experience, etc. Multisensor systems possible

Can get infected Can get poisoned

From this table, it is clear that each system has advantages over the other. In particular, the

issue of saturation can be overcome using an Electronic Nose. Humans quickly become

accustomed to smells at particular levels and they may no longer be interpreted by the brain

as significant. However Electronic Noses will always interpret the signal received so

saturation does not arise as an issue. It should also be noted that advances in technology since

the Davide paper (2003) was published has meant that some of the constraints of theelectronic nose have been overcome, particularly the detection of specific molecules and the

development of pattern recognition algorithms which allow real-time analysis of signals.

(Wilson & Baietto 2011).

Electronic Noses have been developed based on different technologies. The following table

summarises the types of sensor currently available, the sensitive component of the sensor and

the detection principle upon which each is based (Davide et al. 2003).


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Table 2: Electronic Nose Technologies

Type Acronym Sensitive component Detection Principle

Semiconducting metal

oxides

MOS, Taguchi Doped semiconducting

metal oxides e.g. SnO2

Resistance change

Quartz Crystal

Microbalance

Surface Acoustic Wave

QMB

SAW

Organic or inorganic layers

(GC)

Frequency change due to

mass change

Conducting Polymers CP Modified conducting

polymers

Resistance change

Catalytic field-effect

sensors

MOSFET Catalytic metals Work-function change

Pellistor Catalysts Temperature change due

to chemical reactions

Fluorescence sensors Organic dyes Light intensity changes

Electrochemical cells Solid or liquid electrolytes Current or voltage change

Infrared sensors - IR absorption

These sensors are combined in sensor arrays which function collectively as an electronic

nose.


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Volatile Organic Compounds (VOCs) and human breath

VOCs are gaseous molecules based on carbon and hydrogen. Only those which are

physiological in nature and contained in human breath will be examined in this review.

Historical Perspective

From the earliest times the smell associated with particular illnesses has been recognised and

documented. The French chemist Lavoisier was the first to analyse human breath, proving

that it contained CO2. His work led others to investigate human breath and the first alcohol

breath test was devised in the 1800s, followed by the development of a breath test for

diabetes based on acetone concentration.

Initially, research into VOCs was based on the knowledge that pathogens produced odours

and also caused or were the result of disease. Roscioni et al. (1968) postured that pathogens

could be identified by analysing microbial metabolites and that this could, in turn, be used as

a diagnostic tool.

In 1971 Linus Pauling captured a sample of his own breath in a frozen tube. The sample was

analysed using Gas Chromatography and found to contain several hundred volatile organic

compounds in various proportions, mainly in picomolar concentrations (Pauling et al. 1971).

In the 1970s Gas Chromatography was the latest tool in the arsenal of science. It was used

alone and later in tandem with Mass Spectrometry to analyse complex mixtures of VOCs

from a variety of sources.

Diagnostic Potential

Manolis (1983) published a review based on the diagnostic potential of breath analysis. Hiswork was based on the use of GC, GC/MS and MS/MS as analytical methods. From the data

available at that time, clear evidence existed of a link between microbial and cellular

metabolism and the production of VOCs. Manolis also identified the factors which cause

either elevation or suppression of certain VOCs, including weight, gender, exercise and

menstruation. VOCs which would later be identified as biomarkers for cancerparticularly

isopreneare identified, as is the need for further research into the nature of the biochemical

pathways which cause the production of VOCs within the body. The cost of GC/MS as


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analytical tools and the need to develop mechanisms for differentiating between tidal and

alveolar breath samples were also highlighted by Manolis.

In 1992 Dr. Michael Phillips published a review in the Scientific American journal outlining

contemporary thinking in relation to the detection and use of VOCs in the diagnosis of

disease. Philips had previously published papers on carbon disulphide (Phillips et al. 1986),

endogenous acetone (Phillips & Greenberg 1987a), endogenous ethanol and other compounds

(Phillips & Greenberg 1987b), elevated levels of acetone (Phillips et al. 1989) the links

between carbon disulphide and coronary disease (Phillips 1992) and the detection of VOCs in

alveolar air (Phillips & Greenberg 1992). In his review paper, Philips clearly refers to the

need for further research into the issue of the origin of exhaled VOCs in human breath as

either exogenous or endogenous. He also points to the need to develop statistical or other

tools to determine whether the identification of one of the biomarkers of any particular illness

in a patients breath sample is due to chance or to actual illness. He continued to conduct

research into VOCs and, in 1999 published a paper on VOCs as biomarkers in the detection

of lung cancer (Phillips et al. 1999).


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The Link between VOCs and Lung Cancer

In his Lancet paper (Phillips et al. 1999), Phillips reported the results of a clinical test

involving 108 patients who had been referred for bronchoscopy but without any history of

neoplasm. Samples of breath were taken and analysed using GC/MS. A total of sixty patients

were diagnosed with lung cancer and 22 VOCs were found to be common to them, regardless

of how advanced their tumour was. Further tests proved that a correct diagnosis of cancer

could have been predicted in 71.7% of those patients based solely on the results of the breath

analysis using the VOCs as biomarkers, referred to by Phillips as the fingerprint of cancer.

The results were the first to give conclusive evidence of the diagnostic potential of VOCs and

built on the work of ONeill et al. (1988) which identified 28 potential biomarkers from the

exhaled breath of lung cancer patients but made no attempt to compare these results to those

of patients without cancer.

Those biomarkers identified by Phillips are included in Table 3, below:

Table 3: Biomarkers of Lung Cancer

Compounds Identified as Biomarkers of Lung Cancer

Styrene Cyclopentane, methyl Octane, 3-methyl Haxanal

Heptane

2,2,4,6,6-pentamethyl

Cyclopropane, 1-methyl-

2-pentyl

1-hexene Cyclohexane

Heptane 2-methyl Methane, trichlorofluoro Nonane, 3-methyl Benzene, 1-

methylethenyl

Decane Benzene 1-heptene Heptanal

Benzene propyl- Benzene 1,2,4-trimethyl Benzene, 1,4-dimethyl

Undecane 1,3-butadiene, 2-methyl

(isoprene)

Heptane, 2,4-dimethyl

Phillips asserted that alkanes, mono-methylated alkanes and aromatics formed the basis of the

biomarkers. This view would be validated by the work of Di Natale some four years later (Di

Natale et al. 2003). The validation in separate studies of these biomarkers was a major step

forward. However the identification of biomarkers alone did nothing to address the issues

pertaining to the use of the sampling and analytical methods which were available at the time.

The breath testing apparatus used by Phillips was cumbersome and the distinction between


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tidal and alveolar breath was made using a mathematical formula (Phillips et al. 1986). The

use of GC analysis required labour intensive sample preparation and GCs were found only in

analytical laboratories. Their cost was prohibitive for use within clinical settings and required

specialist training. Effectively, this meant that the use of breath testing using Phillips method

would not be suited to routine investigations in a hospital environment.


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Application of the Electronic Nose

As previously mentioned, the JPL e-nose was designed in 1982 based on the work of Persaud

and Dodd in Manchester (Wilson & Baietto 2011). Further Electronic Noses were developed

from that point, mainly focussing on the detection of pathogens in foodstuffs. A group of

engineers based in Romes Tor Vergata University led by Corrado Di Natale were the first to

suggest, in 2003, that an Electronic Nose could be used for the early detection of lung cancer

(Di Natale et al. 2003). A group of sixty patients was tested and a QMB sensor based

Electronic Nose detected 100% of lung cancer patients based on an analysis of exhaled breath

compared to a library of known biomarkers of lung cancer.

The work of Di Natale was furthered by Machado et al. (2005). This study involved an initial

group of 59 people, 45 of whom were healthy and 14 of whom had lung cancer. It showed

that a Cyranose 320 Electronic Nose could discriminate between health individuals and those

with lung cancer. A further validation study reinforced the results of the initial study,

showing that the Electronic Nose had a specificity of 91.9% in the detection of the patients

with tumours. Although the study concluded that the Electronic Nose could be used in the

detection and management of lung cancer, it came with cautionary notes and suggested that

further studies were required amongst different populations and between populations with a

range of conditions rather than exclusively between healthy candidates and those with lung

cancer at different stages of development. In addition, Phillips referred to the need to

discriminate between the endogenous and exogenous sources of the VOCs, which was absent

from the study (Phillips 2005).

By 2005 specific research into biomarkers for asthma and COPD was underway based on the

fundamental work conducted by Phillips and others in the early to mid 1980s on VOCs.Studies had pointed particularly to the levels of Nitric Oxide in the breath samples of patients

with both Asthma and COPD. In 2007 Dragonieri et al. published a study on patients with

asthma as part of a control group which referred to the ease with which asthma could be

detected using a Cyranose 320 Electronic Nose (Dragonieri et al. 2007). This was followed in

2009 by a further paper in which the same electronic nose was able to discriminate between

the smellprints of non-small cell cancer of the lung and COPD (Dragonieri et al. 2009). Their

work also led to the publication by Fens et al. (2009) of a paper in which an Electronic Nose


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was successfully used to discriminate between asthma and COPD. The study was also able to

discriminate between smokers and non-smokers who formed part of the control group.

Diagnostic Mechanism

Pioneering work on biomarkers, largely conducted using GC or GC/MS has led to the

development of libraries or databases of biomarker patterns which have been identified with a

particular illnesses. Engineers have been able to construct sensor arrays from different

materials which are designed to detect smellprints, or biomarker patterns. This implies that

Electronic Noses detect not individual odour molecules but clusters of these molecules

which, together, imply the presence of a particular condition. By reference with an electronic

database of biomarkers the particular smell print can be used to accurately recognise the

smell of a disease. This is possible even at the earliest stages of disease, giving the Electronic

Nose immense power in modern medical terms.

Unlike human noses, Electronic Noses can detect disease, differentiate between healthy and

diseased candidates and also differentiate between diseases even if they have very similar

biomarkers.

The collection of breath samples has been simplified greatly since the early work conducted

by Phillips. DAmico has recently described the use of two interconnected Tedlar bags.

Patients breathe into the first (smaller) of the bags through a mouthpiece. This collects the

initial tidal exhaled breath with the alveolar air being collected in the second bag by means of

a valve. (DAmico et al. 2009). The sampling system is highly portable, unlike Phillips

original apparatus.

The issue of endogenous and exogenous sources of VOCs in breath has also been addressed

to a large extent. It is now known that many of the alkanes and mono-methylated alkanes

which are biomarkers for lung cancer are formed mainly due to oxidative stress within the

tumour cells. Other biomarkers are the result of biochemical pathways at cellular level, many

of which are still not fully understood (Chan et al. 2010). What is clear, however, is that

disease diagnosis using Electronic Nose technology is well within reach.


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Conclusion

The Present Position

Over the past fifty years, since the development of Gas Chromatographic techniques, research

into odour molecules has flourished. This research has led to the development of biomarkers

for diseases including various cancers, lung conditions, schizophrenia, rheumatoid arthritis

and others (Wilson & Baietto, 2011). The development of libraries of biomarker smellprints

has led to the use of Electronic Noses in diagnostic research.

The Future

The potential of the Electronic Nose in disease diagnosis is immense. The research which has

led to its development spans at least three hundred years but has become intensive in the past

fifty years. This has led to the development of a highly cost effective, sensitive, specific

instrument which is portable. Electronic Noses can be designed simply to detect one

condition, to differentiate between conditions with similar symptoms but different smell

prints or they can be adapted by changing the sensors within the array to detect multiple

conditions, for example in a small hospital setting. There is considerable scope using the

internet and other technologies to use Electronic Noses in locations far removed from the

hospital setting, for example in remote communities. This can allow for early intervention

and minimise the need for hospital care, thus reducing costs in the healthcare system.

There is a clear need for the research on Electronic Noses to focus on validation of methods

using clinical trials in order to take the technology into mainstream medical practice and this

is recommended as the primary focus of any immediate studies on Electronic Noses and

disease diagnostics.


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References

Buck, L., Axel, R., 1991. A novel multi-gene family may encode odorant receptors: a

molecular basis for odor recognition. Cell65, 175-187.

Chan, H.P., Lewis, C., Thomas, P.S., 2010. Oxidative stress and exhaled breath analysis: A

promising tool for detection of lung cancer. Cancers2 (1), 32-42.

Davide, F., Holmberg, M., Lundstrom, I., 2003. Virtual olfactory interfaces: electronic noses

and olfactory displays. In: Communications Through Virtual Technology: Identity,

Community and Technology in the Information Age. (Riva G., Davide, F. eds.) IOS Press,Amsterdam pp.193-220.

Di Natale, C., Macagnano, A., Martinelli, E., Paolesse, R., DArcangelo, G., Roscioni, C.,

Finazzi-Agro, A. D'Amico, A. 2003. Lung cancer identification by the analysis of breath by

means of an array of non-selective gas sensors.Biosens. Bioelectron.18, 1209-1218.

Dragonieri, S., Annema, J.T., Schot, R., van der Schee, M.P.C., Spanevello, A., Carratu, P.,

Resta, O., Rabe, K.F., Sterk, P.J., 2009. An electronic nose in the discrimination of patientswith non-small cell lung cancer and COPD.Lung Cancer64 (2), 166-170.

Dragonieri, S., Schot, R., Mertens, B., Le Cessie, S., Gauw, S., Spanevello, A., Resta, O.,

Willard, N., Vink, T., Rabe, K., Bell, E., Sterk, P., 2007. An electronic nose in the

discrimination of patients with asthma and controls.J. Allergy Clin. Immunol.120, 856-862.

Fens, N., Zwinderman, A.H., van der Schee, M.P., de Nijs, S.B., Dijkers, E., Roldaan, A.C.,

Cheung, D., Bel, E.H., Sterk, P.J. 2009. Exhaled breath profiling enables discrimination ofchronic obstructive pulmonary disease and asthma. Am. J. Respir. Crit. Care Med. 180,

1076-1082.

Gordon, S.M., Szidon, J.P., Krotoszynski, B.K., Gibbons, R.D., ONeill, H.J. 1985. Volatile

organic compounds in exhaled air from patients with lung cancer. Clin. Chem. 31, 1278-

1282.

Machado, R.F., Laskowski, D., Deffenderfer, D., Burch, T., Zheng, S., Mazzone, P.J.,Mekhail, T., Jennings, C., Stoller, J.K., Pyle, J., Duncan, J., Dweik, R.A., Erzurum, S.C.,


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2005. Detection of lung cancer by sensor array analyses of exhaled breath. American Journal

of Respiratory and Critical Care Medicine 171, 1286-1291.

Manolis, A., 1983. The diagnostic potential of breath analysis. Clin.Chem 29, 5-15.

Mc.Culloch, M., Jezierski, T., Broffman, M., Hubbard, A., Turner, K., Janecki, T., 2006.

Diagnostic accuracy of canine scent detection in early and late-stage lung and breast cancers.

Integr. Cancer Ther. 5, 1-10.

ONeill, H.J., Gordon, S.M., ONeill, M.H., Gibbons, R.D., 1988. A computerised

classification system for the presence of breath biomarkers in lung cancer. Clin. Chem 34 (8),

1613-1618.

Phillips, M. 1992. Breath tests in medicine. Scientific American267(1), 74-79.

Phillips, M., Gleeson, K., Hughes, J.M.B., Greenberg, J., Cataneo, R.N., Baker, L., McYay,

W.P., 1999. Volatile organic compounds in breath as markers of lung cancer: a cross-

sectional study. The Lancet353, 1930-1933.

Phillips, M., Greenberg, J. 1987b. Detection of endogenous ethanol and other compounds in

the breath by gas chromatography with on-column concentration of sample. Analytical

Biochemistry163, 165-169.

Phillips, M., Greenberg, J. 1992. Ion-trap detection of volatile organic compounds in alveolar

breath.Clin. Chem.38(1), 60-66.Phillips, M., Greenberg, J., Martinez, V. 1986. Measurement of breath carbon disulfide

during disulfiram therapy by gas chromatography with flame photometric detection. Journal

of Chromatography. Biomedical Applications381(1), 164-167.

Phillips, M., Greenberg. J. 1987a. Detection of endogenous acetone in normal human breath.

Journal of Chromatography. Biomedical Applications422, 235-238

Pickel, D.P., Manucy, G.P., Walker, D.B., Hall, S.B., Walker, J.C., 2004. Evidence of canine

olfactory detection of melanoma.Appl. Anim. Behav. Sci. 89, 107-116.

Roscioni, C., De Ritis, G. 1968. On the possibilities to using odors as a diagnostic test ofdisease (preliminary note).Ann. Carlo Forlanini28, 457-461.


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Turner, A.P.F., Magan, N., 2004. Electronic noses and disease diagnostics. Nature Reviews:

Microbiology2, 161-166.

Willis, C.M., Church, S.M., Guest, C.M., Cook, W.A., McCarthy, M., Bransbury, A.J.,

Church, M.R.T., Church, J.C.T., 2004. Olfactory detection of human bladder cancer by dogs:

Proof of principle study.BMJ329, 712-715.

Wilson, A.D., Baietto, M., 2011. Advances in electronic-nose technologies developed for

biomedical applications. Sensors 11, 1105-1176.

Bibliography

Bartolazzi, A., Santonico, M., Pennazza, G., Martinelli, E., Paolesse, R., D'Amico, A., Di

Natale, C., 2010. A sensor array and GC study about VOCs and cancer cells. Sensors and

Actuators B: Chemical146 (2), 483-488.

Chan, H.P., Lewis, C., Thomas, P.S., 2009. Exhaled breath analysis: Novel approach for

early detection of lung cancer.Lung Cancer63 (2), 164-168.

D'Amico, A., Pennazza, G., Santonico, M., Martinelli, E., Roscioni, C., Galluccio, G.,

Paolesse, R., Di Natale, C., 2010. An investigation on electronic nose diagnosis of lung

cancer.Lung Cancer68 (2), 170-176.

D'Amico, A., Di Natale, C., Paolesse, R., Macagnano, A., Martinelli, E., Pennazza, G.,

Santonico, M., Bernabei, M., Roscioni, C., Galluccio, G., Bono, R., Finazzi Agro, E., Rullo,

S., 2008. Olfactory systems for medical applications. Sensors and Actuators B: Chemical,

130 (1) 458-465.

Horvath, I., 2010. Smells like cancer.Lung Cancer68 (2), 127-128.

Kang, N.K., Sun Jun, T., La, D.D., Oh, J.H., Cho, Y.W., Kim, Y.S., 2010. Evaluation of the

limit-of-detection capability of carbon black-polymer composite sensors for volatile breath

biomarkers. Sensors and Actuators B: Chemical147 (1), 55-60.


20/20

Kateb, B., Ryan, M.A., Homer, M.L., Lara, L.M., Yin, Y., Higa, K., Chen, M.Y., 2009.

Sniffing out cancer using the JPL electronic nose: A pilot study of a novel approach to

detection and differentiation of brain cancer.NeuroImage47 T5-T9.

Wang, P., Liu, Q., Xu, Y., Cai, H., Li, Y., 2007. Olfactory and taste cell sensor and its

applications in biomedicine. Sensors and Actuators A: Physical139 (1-2), 131-138.

104473454 Smells Like Cancer Electronic Noses and Disease Diagnostics

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