Jump to content

Colormetric Sensor- Detect early LC!

Recommended Posts

Read about this in my Nursing2007 magazine.

Experts Detect Lung Cancer Using Color Sensor Breath

News Author: Allison Gandey

CME Author: Désirée Lie, MD, MSEd

Release Date: March 2, 2007

March 2, 2007 — Lung cancer patients appear to have a unique chemical makeup to their breath and researchers are working to tap into this to develop a simple, inexpensive, and noninvasive new cancer screening method. Still in development, a group published in the February 26 Early Online issue of Thorax, report that their color sensor breath test detected lung cancer with more than 70% accuracy. "This method is showing promise," lead investigator Peter Mazzone, MD, of the department of pulmonary allergy and critical care medicine at the Cleveland Clinic in Ohio said during an interview. "But I don't want to fool anyone into thinking this is ready for clinical practice. We'd want to see 90 to 95% accuracy rates for that," he said.

But the fact that the new colorimetric sensor array may be effective in detecting very early lung cancer is garnering attention and Dr. Mazzone told Medscape that already he has received a number of media inquiries about the test. In their article, the researchers point out that current screening programs have yet to lead to a reduction in lung-cancer–specific mortality or overall mortality and an advance to current diagnostic tools would be a welcome addition.

The researchers tested the ability of the new sensor system to detect a pattern of volatile organic compounds unique to lung cancer. They suggest that metabolic changes within cancer cells can lead to changes in the production and processing of these compounds, which may be detected in samples of the exhaled breath of patients.

The colorimetric sensor array has 36 spots composed of different chemically sensitive compounds on a disposable cartridge. The colors of these spots change based on the chemicals with which they come into contact.

More than 140 individuals participated in the study. Of these, 49 had non–small-cell lung cancer, 18 had chronic obstructive pulmonary disease, 15 had idiopathic pulmonary fibrosis, 20 had pulmonary arterial hypertension, another 20 had sarcoidosis, and 21 people were included as controls.

Identified Cancer With "Moderate Accuracy"

Participants were asked to breathe room air for 12 minutes while exhaling into a device designed to draw their breath across the colorimetric sensor array. The color changes that occurred for each individual were converted into a numerical vector. The researchers statistically analyzed the vectors using a random forests technique to determine whether lung cancer could be predicted from the responses of the sensor.

The group developed a prediction model using observations from 70% of the subjects. This model was able to predict the presence of lung cancer in the remaining 30% of subjects with a sensitivity of 73.3% and a specificity of 72.4% (P = .01).

The researchers conclude that the breath test can detect the unique pattern of volatile organic compounds in the breath of patients with lung cancer with moderate accuracy. The results were not affected by age, sex, or stage of disease. They write, "Further work may clarify the nature of the distinct breath constituents. This would help to guide refinement of the sensor array and breath collection system to maximize the diagnostic accuracy of the test."

"The problem we have now is an issue of numbers," Dr. Mazzone told Medscape. "We will need to study this method in larger patient groups with the same condition and stage of disease. We need more testing within each category," he emphasized.

Thorax. Published online February 26, 2007.

Clinical Context

Lung cancer is often diagnosed at an advanced stage when treatment is less successful, and although advances in imaging allow earlier examination of lung nodules, there is a need for low-cost, accurate, and noninvasive diagnostic tests. Metabolic changes within cancers can lead to changes in production of volatile organic compounds, and studies have evaluated the use of gas chromatography and mass spectroscopy for detecting these changes, but these systems are expensive, require expert interpretation, and are not point-of-care tests.

This is a study using a new colorimetric sensor array with chemically sensitive compounds that detect volatile organic compound changes with color changes and that can be easily administered to patients. The goal was to assess the sensitivity and specificity of the method for normal healthy controls vs patients diagnosed with non–small-cell lung cancer vs those with other lung diseases. The authors noted that this study differs from previous studies using gas sensors in the devices used, the method of breath collection, type of analysis, and study population.

Study Highlights

143 subjects older than 18 years were enrolled, including those with lung disease such as sarcoidosis, idiopathic pulmonary fibrosis, pulmonary arterial hypertension (PAH), and chronic obstructive pulmonary disease (COPD), those with lung cancer, and healthy controls.

Smokers and nonsmokers were included, and a group with indeterminate and undiagnosed pulmonary nodules (n = 29) was also asked to participate and later diagnosed using biopsy.

Patients with COPD were required to meet the Global Initiative for Chronic Obstructive Lung Disease criteria for at least mild COPD.

Those with PAH were diagnosed by right heart catheterization.

Sarcoidosis was clinically or radiologically diagnosed.

Patients with lung cancer had been diagnosed with non–small-cell lung cancer.

Treatment of any disease was not an exclusion factor.

Each subject performed tidal breathing of unfiltered room air for 12 minutes and exhaled into a mouth piece.

The exhaled breath was drawn over the sensor array using a pump, and at the end of the 12-minute breath collection, air tubing and the sensor array were changed and a sample of room air drawn across the system for 12 minutes.

The sensor array contained 36 chemically sensitive spots with different sensitivities to volatile organic compounds, and color changes were converted to numerical values for the change in red, blue, and green components, resulting in a 108-dimensional vector (3 values per spot).

The difference between the exhaled breath and room air results was used in the analysis.

The random forest method was used to develop a model for discriminating patients with lung cancer from those without lung cancer.

Overall, mean age reflected the conditions being tested with a range from 47 to 65 years.

Of those with lung cancer, 14% were stage IA, 14% were stage IB, 4% were stage IIA, 4% were stage IIB, 24% were stage II B, and 33% stage IV.

55% of those with lung cancer had well-defined adenocarcinoma, and 27% had well-defined squamous cell carcinoma.

70% of subjects had an error rate of 14.1%, and the method was validated using the remaining 30% (49).

Validation showed a sensitivity of 73.3% and a specificity of 72.4% for the diagnosis of lung cancer (P = .01).

The sensitivity and specificity were not influenced by age, sex, histology, tumor size, cancer stage, or smoking history.

Of the 29 subjects with indeterminate lung nodules, 21 turned out to have lung cancer.

Using the same model for diagnosis of lung cancer, the sensitivity was 100% and the specificity was 60% for this group of patients.

Pearls for Practice

The colorimetric assay for lung cancer diagnosis consists of detection of volatile organic compounds comparing patients' exhaled breath with room air.

The sensitivity and specificity of the color sensor array for lung cancer diagnosis using healthy controls and those with lung disease are 73.3% and 72.4%, respectively.

Link to comment
Share on other sites

Join the conversation

You can post now and register later. If you have an account, sign in now to post with your account.

Reply to this topic...

×   Pasted as rich text.   Restore formatting

  Only 75 emoji are allowed.

×   Your link has been automatically embedded.   Display as a link instead

×   Your previous content has been restored.   Clear editor

×   You cannot paste images directly. Upload or insert images from URL.

  • Create New...

Important Information

By using this site, you agree to our Terms of Use.