Using an electronic nose to diagnose ovarian cancer

Some background on gas analysis for diagnosis w Physicians are taught to smell breath. w Sweet rotten apple smell → Diabetes w Fishy smell → Liver disease w Linus Pauling used gas chromatography / mass spectroscopy on breath in the 1970s. w Many experiments with dogs since then. w Lots of different e-nose experiments the past 20 years.

Pros and cons of different gas analysis methods w GC-MS measures about 250 volatile organic compounds, but is time-consuming and expensive. w Dogs just indicate healthy / sick and do not provide additional insight. w Human noses have too low sensitivity. w Electronic noses are fast and cheap, making them suitable for mass screening.

Our suggested methodology for designing diagnostic e-noses 1. 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 w 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. w 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 NOSE The ”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)

ELECTRONIC NOSE The results were surprisingly good in most cases Bayes Network83/9266/7092 Naive Bayes84/9263/7091 Multilayer Perceptron82/9261/7088 RBF Network81/9257/7085 SimpleLogistic86/9264/7093 SMO84/9262/7091 IB175/9250/7077 KNN75/9250/7077 KStar81/9261/7088 LWL82/9258/7086 ClassificationVia Regression83/9259/7088 ThresholdSelector77/9257/7083 VFI72/9261/7082 ADTree84/9266/7093 J4879/9253/7081 JRip78/9262/7086 NNge83/9261/7089 PART79/9265/7089 Bayes Network83/9266/7092

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 ADATE 1.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 w Can reasonably effectively synthesize recursive programs and invent recursive help functions. w Specifications need not contain specific outputs and can be simulations, for example. w Generalizing ability of models is unusually good. w The resulting programs are analysable, that is not totally opaque. w Run times are long as for most evolution, natural as well as artificial. w The system is not as well documented and user friendly as it should be. w Evolution can give totally unexpected results and be difficult to “harness”

Conclusions w There clearly is a correlation between the gasses emitted from a tissue sample and whether or not the sample contains a cancer tumour. w Even an old and primitive nose designed for explosives can differentiate between sick and healthy samples. w The number of samples is too small to say which machine learning techniques that are most accurate.

Future work w Identify which form of disease diagnosis based on gas analysis that gives the best cost / benefit ratio. w Run GC-MS on hundreds of samples. w Run feature extraction / dimensionality reduction on the resulting data. w Redesign an electronic nose from the ground up so that it is tailor made to detect the extracted features.