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Metabolomx test detects lung cancer from breath
Date:12/15/2011

rmation that can help with treatment decisions, such as the type of lung cancer, its stage, and prognosis. The accuracy of these non-invasive tests can be further augmented when combined with existing clinical predictors, such as health status and age."

Metabolomx has begun trials of its next-generation sensor array system in an ongoing lung cancer study at the Cleveland Clinic. The Company expects their current generation system to be over 100 times more sensitive at detecting breath chemicals, which are often present in very low concentrations, than the system used in the published study.

About the Study

The study, conducted at the Cleveland Clinic and led by Dr. Peter Mazzone, was designed to develop an exhaled breath biosignature of lung cancer using Metabolomx' colorimetric sensor array and to determine the accuracy of breath biosignatures of lung cancer characteristics with and without the inclusion of clinical risk factors. Breath samples were drawn from 229 individuals, 92 with biopsy-proven, untreated lung cancer and 137 either at a risk for developing lung cancer or with indeterminate lung nodules. Logistic prediction models were developed and statistically validated based on the color changes of the sensor. Age, sex, smoking history, and chronic obstructive pulmonary disease were incorporated in the prediction models.

Key Study Results

  • Approximately 81% accuracy for the detection of lung cancer without regard for subtype
  • Approximately 83%, 85%, and 89% accuracy in discriminating patients with adenocarcinoma, squamous cell, and small cell lung cancer versus controls, demonstrating that the breath test can differentiate lung cancer subtype - the first diagnostic announced to do so.
  • An ability to differentiate Stage I/II from Stage III/IV (approximately 79% accuracy) indicating the ability to identify lung cancer at the earliest stages.


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Contact: Justin Jackson
jjackson@burnsmc.com
212-213-0006
Burns McClellan
Source:Eurekalert

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