Using an electronic nose to diagnose ovarian cancer

Some background on gas analysis for diagnosis Physicians are taught to smell breath. Sweet rotten apple smell → Diabetes Fishy smell → Liver disease Linus Pauling used gas chromatography /

mass spectroscopy on breath in the 1970s. Many experiments with dogs since then. Lots of different e-nose experiments the

past 20 years.

Pros and cons of different gas analysis methods GC-MS measures about 250 volatile

organic compounds, but is time-consuming and expensive.

Dogs just indicate healthy / sick and do not provide additional insight.

Human noses have too low sensitivity. Electronic noses are fast and cheap, making

them suitable for mass screening.

Our suggested methodology for designing diagnostic e-noses1. Use GC-MS on a few hundred samples of sick

as well as healthy patients.2. Extract a set of features / markers using machine

learning techniques.3. Build e-nose sensors that are optimized for the

extracted features, e.g. aniline, tryptophan and pyrrole.

4. Use classification algorithms, e.g., C5.0, to analyze the e-nose output to screen or diagnose.

Extracting features / bio markers

The goal is to select a subset of compounds whose concentrations correlate well with the diagnosis, or more generally a dimensionality reduction of the compounds.

Dimensionality reduction methods• Principal components analysis (PCA).

• Partial least squares (PLS).

• Autoencoding neural networks.

• Isomap and extended Isomap.

Basic architecture for an autoencoder neural network

What are we up against in nature?

Dogs are extremely good at smelling. In fact olfaction, i.e. the process of smelling, is a dog's primary special sense. A dog's sense of smell is said to be a thousand times more sensitive than that of humans. In fact, a dog has more than 220 million olfactory receptors in its nose,

The dog is said to be able to smell 70 molecules per cubic metre of air.

The best gas chromatograph needs ten times that number

Gas chromatograph / mass spectrometer

Difference between the smell of a healthy tissue and a cancer tissue.

ELECTRONIC NOSE

The electronic nose mimics the human olfaction system

It include three major parts: a sample delivery system, a detection system, a computing system.

The sample delivery system is needed in order to introduce the odor ino the nose

The detection system, generally a sensor set, is the “reactive” part of the instrument.

The computing system is needed to take care of the signal from the electronic interface transforming the signal into a digital value. Recorded data are then processed using algorithms and various models

ELECTRONIC NOSEThe ”Technical” Sensors

The sensing material is metal oxide, typically SnO2. this is heated at a certain temperature in air, oxygen is adsorbed on the crystal surface with a negative charge. The electrical resistance of the sensor is attributed to a potential barrier. Thus, surface potential is formed to serve as a potential barrier against electron flow (Fig. 1).In the presence of a deoxidizing gas, the surface density of the negatively charged oxygen decreases, so the barrier height in the grain boundary is reduced (Fig. 3). The reduced barrier height decreases sensor resistance.

ELECTRONIC NOSE

So what can our nose with only 16 sensors do?

ELECTRONIC NOSE

When we use this nose to smell alcohol, we get 16 analog signals like this

ELECTRONIC NOSE

The signals are digitized and stored in a 16 x 500 matrix, where 500 is the number of time steps. The data is then reduced to a 16 x 3 matrix.

The reduction is made by calculating the three following quantities::

*** PDerBefore:- Maximum slope when sample is opened*** Step size:- Maximum height*** aDerAfter:- Maximum slope when sample is closed again

An example is shown here. In this figure we have made all the values positive by adding unity and then taken their logarithm (just for the figure) 0,95

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49

ELECTRONIC NOSE

The results were surprisingly good in most cases

Bayes Network 83/92 66/70 92

Naive Bayes 84/92 63/70 91

Multilayer Perceptron 82/92 61/70 88

RBF Network 81/92 57/70 85

SimpleLogistic 86/92 64/70 93

SMO 84/92 62/70 91

IB1 75/92 50/70 77

KNN 75/92 50/70 77

KStar 81/92 61/70 88

LWL 82/92 58/70 86

ClassificationVia Regression 83/92 59/70 88

ThresholdSelector 77/92 57/70 83

VFI 72/92 61/70 82

ADTree 84/92 66/70 93

J48 79/92 53/70 81

JRip 78/92 62/70 86

NNge 83/92 61/70 89

PART 79/92 65/70 89

Bayes Network 83/92 66/70 92

ELECTRONIC NOSE

Using the the ADATE automatic programing system we found that some values are more important than others.

In fact, during training ADATE found that the main rule for correctly classifying the sample is

if X23 < X16 then healthy else cancer

With this rule only we got 141 of 162 correct classifications

The next important rule is

if X0 - X7 < X16 then healthy else cancer

A further improvement of the accuracy with 3 percentage points.

Examples of programs automatically generated with ADATE1. Signal processing. Image segmentation.2. Control system. Driving an autonomous

car.3. Classification and regression algorithms,

for example credit rating, cancer diagnosis etc. Classical machine learning.

4. Sorting, searching, combinatorics, string processing and many other standard algorithms.

Some features of ADATE Synthesis of primitively or generally recursive

programs. Automatic invention of help functions where and

when needed. “Loose” specifications requiring only evaluation

(grading), not specific outputs. Kingdom based on size-evaluation value ordering

and diversification methods. Starts with one initial program and grows/shrinks

dynamically. ES / RP optimization of floating point constants

Pros and Cons of ADATE Can reasonably effectively

synthesize recursive programs and invent recursive help functions.

Specifications need not contain specific outputs and can be simulations, for example.

Generalizing ability of models is unusually good.

The resulting programs are analysable, that is not totally opaque.

Run times are long as for most evolution, natural as well as artificial.

The system is not as well documented and user friendly as it should be.

Evolution can give totally unexpected results and be difficult to “harness”

Conclusions

There clearly is a correlation between the gasses emitted from a tissue sample and whether or not the sample contains a cancer tumour.

Even an old and primitive nose designed for explosives can differentiate between sick and healthy samples.

The number of samples is too small to say which machine learning techniques that are most accurate.

Future work

Identify which form of disease diagnosis based on gas analysis that gives the best cost / benefit ratio.

Run GC-MS on hundreds of samples. Run feature extraction / dimensionality reduction

on the resulting data. Redesign an electronic nose from the ground up so

that it is tailor made to detect the extracted features.