Lung Cancer Indicator Detection
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Enrollment
- 2,603
- Locations
- 19
- Primary Endpoint
- Area Under the Curve for the diagnostic algorithm for lung cancer with optimal point sensitivity, specificity negative and positive predictive values.
Study Overview
Brief Summary
The Lung Cancer Indicator Detection (LuCID) study investigates the the diagnostic accuracy of FAIMS for diagnosis of lung cancer by analysis of exhaled Volatile Organic Compounds.
Detailed Description
Rationale Approximately 75% of patients with lung cancer present with advanced disease. For those with stage 1 disease, the chance of cure is up to 70%. Therefore, diagnostics which may aid identification of those with early stage lung cancer will play an important role in future screening programs. Because all cancer cells are characterized by a change in their metabolism related to their uncontrolled growth, detection of the resulting metabolites may be a novel diagnostic tool for early stage lung cancer. Subsets of these metabolites are volatile and are exhaled as so-called volatile organic compounds (VOCs). Analysis of exhaled VOCs suggests they differ between patients with advanced lung cancer and healthy controls. The Lung Cancer Indicator Detection (LuCID) study aims to validate the use of a high-throughput breath analysis technique in a population of patients whom are clinically suspected of having lung cancer.
Methods LuCID is an international, multi-center case-control study. Patients referred by their GP or treating specialist for a diagnostic work-up for lung cancer will be invited to participate in the study. A maximum of two thousand five hundred patients whom consent to partake in this study will be asked to provide a breath sample prior to any diagnostic procedures. This is a non-invasive procedure that will require the patient to breath normally into a facemask to collect 2.5L of breath amounting to approximately 10 minutes of breathing. The resulting samples will be analyzed for VOCs by Gas Chromatography coupled to Mass Spectrometry and Gas Chromotography coupled to Field Assymetrical Ion Mobility Spectrometry. The resulting VOC profiles will be used to generate a diagnostic algorithm in order to try to differentiate between patients with and without lung cancer in the intention to diagnose population. This study will not interfere in any with the standard care offered at the clinical sites.
Outcomes The results of this study will provide detailed insights into the accuracy of the test for the detection of lung cancer in the intention to diagnose population. This will form the foundation for a subsequent study in a population at risk for the development of lung cancer. If sufficiently accurate for early stage disease, analysis of breath VOCs could help implement large-scale screening for lung cancer, significantly decreasing the morbidity and mortality of the disease.
Study Design
- Study Type
- Interventional
- Allocation
- Na
- Intervention Model
- Single Group
- Primary Purpose
- Diagnostic
- Masking
- None
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Recruitment for these patients will be done from NHS hospitals whom identify or follow-up on patients suspected of having lung cancer.
- •Inclusion criteria:
- •Older than 18 years at time of consent
- •Referred for investigation due to suspicion of lung cancer
- •Referral based on suspicious symptoms
- •Referral based on suspicious finding on imaging, including CTscan with indeterminate nodule requiring follow-up evaluation.
- •Capable of understanding written and/or spoken language
- •Able to provide informed consent
Exclusion Criteria
- •(Anticipated) inability to complete breath sampling procedure due to e.g. hyper- or hypo-ventilation, respiratory failure or claustrophobia when wearing the sampling mask
- •Participating in a Clinical Trial Investigational Medicinal Product (CTIMP)
- •Pulmonary function test with metacholine or beta-2-sympatico mimetic in last 2 hours.
- •Any lung biopsy in the past 48 hours
- •Currently undergoing anti-cancer treatment for lung cancer
Outcomes
Primary Outcomes
Area Under the Curve for the diagnostic algorithm for lung cancer with optimal point sensitivity, specificity negative and positive predictive values.
Time Frame: 2 years
Diagnostic accuracy of VOC analysis for Lung Cancer diagnosis based on pattern recognition analysis of raw VOC-spectra generated by Lonestar analysis of breath.
Secondary Outcomes
- Fraction of within group variability in exhaled VOCs explained by factors not primarily related to disease proces(2 years)
- Identified exhaled biomarkers associated with tumor stage and size.(2 years)