Lung Cancer Detection Using an Electronic Nose Anna Folinsky California Institute of Technology March 10, 2005 Abstract Electronic nose systems have been used both in and out of laboratory settings for a wide v arie ty of appli cati ons. One of the larger gener al initiativ es is to war ds using the m in biomedica l app lic ati ons , not abl y the detections of ana lyt es which may be corr elat ed with disease states. This proposa l outlines a syste m to detect biomark ers found in the breath which are associated with lung cancer, one of the leading causes of death in the USA. Use of standard polymer/carbon black sensors from our lab will be augmented with novel sensing technologies from our lab, in an effort to obtain the nece ssar y sensi tivit y and discrimin ator y power needed for the task. These should be able to differentiate diseased and healthy states, and potentially separate different stage cancer patients. Introduction Lung Cancer It has long been known that certain diseases produce volatile compounds that can be smelled on the breath, or elsewhere on the body. Two of the oldest, most common examples are the scents of ketones on the breath of diabetic patients, and the smell of “freshly baked bread” on I
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8/13/2019 Lung Cancer Detection Using an Enose Ifprop
Electronic nose systems have been used both in and out of laboratory settings fora wide variety of applications. One of the larger general initiatives is towards usingthem in biomedical applications, notably the detections of analytes which may becorrelated with disease states. This proposal outlines a system to detect biomarkersfound in the breath which are associated with lung cancer, one of the leading causesof death in the USA. Use of standard polymer/carbon black sensors from our lab willbe augmented with novel sensing technologies from our lab, in an effort to obtain thenecessary sensitivity and discriminatory power needed for the task. These should beable to differentiate diseased and healthy states, and potentially separate different stagecancer patients.
Introduction
Lung Cancer
It has long been known that certain diseases produce volatile compounds that can be smelled
on the breath, or elsewhere on the body. Two of the oldest, most common examples are the
scents of ketones on the breath of diabetic patients, and the smell of “freshly baked bread” on
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The vapor sensing method we pursue in our lab involves the use of arrays of sensors. No sensor
is designed to respond specically towards an individual compound. Instead, each sensor is
broadly responsive to a variety of odorants. Each analyte produces a distinct ngerprint from
the array of broadly cross-reactive sensors (Fig.1). Pattern recognition algorithms can then
be used to obtain information on the identity, properties, and concentration of the exposed
vapor 15–18 . In this respect, our system resembles that used in the mammalian olfactory
system, in which each olfactory receptor responds to a wide variety of odorants 19 , and our
array of sensors may be seen as analogous to the array of receptors in the nasal epithelium.
Due to this similarity, our system is sometimes designated as an “electronic nose”.
Figure 1: Differentiation between odorants: (a) an array of broadly-cross reactive sensorsin which each individual sensor responds to a variety of odors; (b) pattern of differentialresponses across the array produces a unique pattern for each odorant or odor.
A variety of signal transduction mechanisms have now been implemented to construct
electronic nose systems. Surface acoustic wave devices (SAWs) 20 , metal oxide sensors 21 ,
Figure 3: Principal components data from a 20-detector array exposed 5 times to of thelabeled analytes, each at 0.005 - 0.03 P/P o , containing 99% of the total variance. Theellipsoids contain 99% of the data for each analyte. All presentations were in each setrandomized over all repetitions
inherently conducting polymer sensors (made from inherently conductive materials, such
as polyaniline, polypyrrole, polythiophene, etc) are highly sensitive to water vapor, our
approach of using composites of inorganic conductors and sorptive insulating organic phases
allows development of chemiresistive sensors that are relatively insensitive to water vapor 19 .
There are also a pair of newer sensing substrates being tested, expected to yield improved
sensor classication. The rst of these is based on composites of homogenous or blended
organic nonvolatile molecules with conductors such as carbon black 34 . These sensors have
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Figure 4: Principal componenent plots indicating the ability to distinguish between “healthy”
and “cancerous” patients. (a) 6 sensors of poly(vinylstearate),poly(ethylene-co-vinyl ac-etate), and poly(ethylene vinyl alcohol), all 40 wt% carbon black (CB). (b) 4 sensors of 2-5nm Au colloids, capped with hexanethiol or 6-mercapto-1-hexanol. (c) 8 sensors of lauricacid, tetracosane, and tetracosanoic acid, with 30 wt% dioctyl phthalate, and tetracosanoicacid, all with 75 wt% CB
can be attributed either to the larger number of those sensors, or to the unique properties
of these sensors. For the other two classes, increasing the diversity of chemiresistors (in an
array of sensors) and modifying the physical properties of their building blocks is expectedto give better discrimination. Since our sensors operate solely by detecting changes, we will
be able to detect these analytes even in the presence of a constant background of other
components of an analyte mixture (as has been previously demonstrated in a variety of sit-
uations for nerve agent simulants at low concentrations in the presence of many different
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implemented using commercial software packages, such as MATLAB, with algorithms that
already exist and have been extensively previously used in our lab. Although these methods
have proven to be quite useful for analyte discrimination, more sophisticated algorithms
(e.g. supervised or unsupervised neural networks, or a variety of non-linear methods) will
be employed if necessary. However, if, as suggested, the biomarkers are entirely due to static
levels of production due to oxidative stress, that is also of great interest. While we won’t
be able to differentiate, we will still be able to detect at early stages, which would still be
enormously benecial as an early detection screen.
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