Good Research Proposal About Method For Testing E-Nose To Diagnose Early Stage Lung Cancer

The following method chosen for the study is large-scale study with various lung cancer patients. The low cost of the electronic nose unit used in this study allowed gave us freedom to do a larger clinical trial to detect lung cancer in patients. Within this study we cooperated with a number of fields including: nanotechnology, biomedical designs & engineering, medical oncology as well as computational approaches. This multilevel approach allowed us to design a very sensitive, low cost, non-invasive nanometric electronic nose (e-nose) that has a very quick response time. Similar approaches were done in previous studies (Li et al, 2014; Wang et al, 2013). The quick response rate and low cost will allow for a larger sample of individuals to perform the study. The intended audience we are looking to test will be individuals who volunteer to participate in our study. The volunteers will come from the local cancer treatment centre located at the Northeast Cancer Center, Health Sciences North ( Sudbury, ON, Canada). This location can house a large variety of individuals coming from northern Ontario for cancer treatment. The location is also convenient as it is close to the University and easy to schedule around volunteers’ schedules. We will ask for permission to educate patients on the e-nose e.g. benefits, technical aspects, procedure and time commitment expectations for participating in a study. A consent form for individuals accepting the study will give us a thorough background of the patient including age, culture, sex and other factors which will help in our study.
As there are multiple e-nose options available we have selected two e-nose devices to study based on past research results and efficiency. The two types which we selected are the quartz crystal microbalance (QCM) sensor. The QCM is made up of gold electrodes, a very sensitive coat material as well as a quartz wafer. Materials that have gas sensitivity like metalloporphyrins serve as the sensitive coat material found in gas sensitive QCM sensors. Upon being exposed to a given breath sample, the QCM sensor absorbs all the molecules of Volatile Organic Compounds (VOCs) present in the sample. VOCs are released from the breath as a part of normal metabolic activity or due to pathological disorders like lung cancer (Philips). The VOC that we are measuring in this study with the e-nose will be (listed in order of probability as a biomarker for lung cancer): Styrene, Decane, Isoprene, Benzene, Undecane, 1-hexene, Hexanal, Propyl benzene, 1,2,4 Trimethyl benzene, Heptanal, Methyl cyclopentane (Chen, 2005).

Electronic Nose Technology

In this study we are using a Quartz Crystal Microbalance which measures the surface mass change on an voltage induced oscillating quartz. The change in mass on the quartz surface is directly related to the change in frequency of the oscillating crystal using the Sauerbrey equation delta m = -C x delta f (Kumar, 2011). The change in QCM frequency determines the mass of analyte adsorbed in ng/cm2 and is valid for inelastic subjects like VOCs. It is possible to detect specific vapours when a film is used. In this case we shall use poly(isobutylene) film-coated (Chen 2005). We will use the VOC biomarkers mentioned above to find breath patterns in the patients measured with the QCM (Wang et al, 2008).
The second form of e-nose that was selected works using gold nano-particle (GNP) sensors. This type of sensor has been used for the detection of lung cancer since 2009 (Wang et al, 2008; Barash et al, 2009). The GNP sensor is made up of a series of cross-reactive chemi-resistors which are on top of a circuit board made of polytetrafluoroethylene. Every resistor is made up of one silicon wafer, a thermal oxide layer, round inter-digitized gold electrodes and the chemiresistive layer. Past research conducted on this type of e-nose shows that the variation in the patterns of resistivity of all the sensors can be utilized in early identification of lung cancer (Wang et al, 2008; Hill and Binions, 2012). We chose these two e-noses, as past research has shown that the exhaled breath VOCs using an e-nose is a viable option that is effective in large-scale testing for the early detection of lung cancer. The other benefits of these options are the low cost associated to implementing the procedure and that the overall process is non-invasive in nature with high sensitivity, accurate results and it is simple to use.

Patient Groups

A total of 100 people within the ages of 50-70 years will be selected and placed into five sets of patient groups: smokers, ex-smokers, non-smokers, lung cancer patients, and patients with other respiratory disorders. It is suggested that there might be differences in the patterns we see from cancer patients from people with many years of smoking than from the persons gaining cancer from second hand smoke (Chen, 2005). As a control we will select people with healthy lungs. All patients will have a thorough pre-screen to show the existence of lung cancer and if so at what stage it might be present. As we are looking for early detection of lung cancer there might be specific markers that are present in early stages compared to late stages. The different patient groups will allow us to detect differences in the ‘smell pattern’ found of the patient groups.

Collection of Patient’s Lung Samples

For the study all outside influences like perfumes, and flowers should be removed from the testing space. Additionally, as we want only the lung air we will placing nose clips on the patients nose so there will not be any contamination from their nasal cavity. Briefly patients will inhale nitric oxide free air for 2.5 minutes from an oxygen mask set at ventilation rate 5-8 L/min. With an average lung capacity for healthy subjects this should be adequate for full lung ventilation. To assess the reproducibility of the experiment and possibly to see if there is a difference in the first exhaling and the second two bags will be filled. Samples will be labeled with a blind numbered sticker to protect identity only research team will have access to code. Samples will be analysed with enose equipped with active graphite filter to reduce moisture. Each e-nose will measure the bags in triplicate to make six displays per subject. A GC-MS will verify the results to tell the differences in VOCs from the different breath patterns.

Treatment of the Data and Analysis

The data from the two sensors will be processed with a Savizky-Golay filtering and baseline correction. For the two different sensors we will use different processing methods to learn about the sensor response data and discriminate nose from pertinent signals. We will plot the data in 2-D using the two different variables calculated similar to Machado et al. (2005). There will be a lot of different data that corresponds to different VOCs. We will use multivariable analysis rather than statistical analysis to understand the data and create a model that will show a understable distance (if possible) between the three different groups. This distance called Mahalanobis distance is a is a measure of the distance between a point P and a distribution. A distance of three or greater will mean that each group are discrete from each other (De Maesschalck, 2000). Within the lung cancer group we will further study whether there are early detectable cancers and do a similar analysis. We will make the sensor better over time by eliminating noise from appropriate measurable signal
Suitable endpoints would be to find specific VOCs that differ in the different groups of people with cancer e.g. early stage (stage 0), medium stage (stage I) and late stage Stage II & III). Analysis will determine the accuracy of the improved e-nose, which includes previously unused strategies to improve sample quality.

Follow-up patients and further outreach in the community for more sample data

Patients who participated in the study will be contacted and the results will be shared with them to tell them if they had lung cancer. To obtain more data and to make our sensor better we will conduct tests in the community and compare these results to our sample category for detection of early stage lung cancer.

Bibliography

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