Performance of the Aeonose™ as a diagnostic tool to detect lung cancer: an external validation study
Recruiting
- Conditions
- - presence or absence of lung cancer
- Registration Number
- NL-OMON20226
- Lead Sponsor
- Medisch Spectrum Twente Enschede
- Brief Summary
Kort S, Brusse-Keizer M, Gerritsen JW, Van Der Palen J. Data analysis of electronic nose technology in lung cancer: Generating prediction models by means of Aethena. J Breath Res [Internet]. 2017;11(2):26006.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Recruiting
- Sex
- Not specified
- Target Recruitment
- 800
Inclusion Criteria
To be eligible as a patient to participate in this study, subjects need to match the following inclusion criteria:
•Referred for a CT scan and/or a histological biopsy due to suspicion for lung cancer.
Exclusion Criteria
Potential subjects will be excluded from participation in this study when meeting the following criterion:
•Known to have an active malignancy.
Study & Design
- Study Type
- Observational non invasive
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method the comparison of outcomes obtained from the Aeonose™ (21) with the outcomes obtained from the gold standard, which is histopathology. The outcomes will be presented as sensitivity, specificity, positive predictive value, negative predictive value and the AUC for the Aeonose™ as a diagnostic test to predict the probability of lung cancer.
- Secondary Outcome Measures
Name Time Method Secondary study parameters are smoking status including amount of pack years, comorbidities, medication use, age and sex to describe the study population. In case of presence of lung cancer, clinical parameters such as tumour type and TNM stadium are noted.