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Validation and Determination of Feasibility for Clinical Use of an Animal Biosensor Platform to Detect Non-Small Cell Lung Cancer-Specific Volatile Organic Compounds in Urine Samples
The researchers are doing this study to test the ability of an animal biosensor platform (ABP) to detect NSCLC. Participants in this study will either be diagnosed with NSCLC, suspected to have NSCLC, or have not been diagnosed or suspected to have NSCLC. The ABP test uses laboratory animals that are trained to detect (by smell) different chemicals in urine. Studies show that people with lung cancer have unique chemicals in their urine that are not present in people without lung cancer, and researchers think these chemicals can be used to identify people with lung cancer without the need for invasive procedures (like biopsy).
Age
18 - 90 years
Sex
ALL
Healthy Volunteers
Yes
Memorial Sloan Kettering Basking Ridge (All Protocol Activities)
Basking Ridge, New Jersey, United States
Memorial Sloan Kettering Monmouth (All Protocol Activities)
Middletown, New Jersey, United States
Memorial Sloan Kettering Bergen (All Protocol Activities)
Montvale, New Jersey, United States
Memorial Sloan Kettering Suffolk-Commack (All Protocol Activities)
Commack, New York, United States
Memorial Sloan Kettering Westchester (All Protocol Activities)
Harrison, New York, United States
Memorial Sloan Kettering Cancer Center (All Protocol Activities)
New York, New York, United States
Memorial Sloan Kettering Nassau (All Protocol Activities)
Uniondale, New York, United States
Start Date
August 24, 2023
Primary Completion Date
August 1, 2026
Completion Date
August 1, 2026
Last Updated
July 20, 2025
210
ESTIMATED participants
urine sample
DIAGNOSTIC_TEST
Lead Sponsor
Memorial Sloan Kettering Cancer Center
Collaborators
NCT07485114
NCT06066138
Data Source & Attribution
This clinical trial information is sourced from ClinicalTrials.gov, a service of the U.S. National Institutes of Health.
Modifications: This data has been reformatted for display purposes. Eligibility criteria have been parsed into inclusion/exclusion sections. Location data has been geocoded to enable distance-based search. For the authoritative and most current information, please visit ClinicalTrials.gov.
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