Lung Cancer
Conditions
Brief summary
The goal of this observational clinical trial is to learn if chest tomosynthesis is a potential alternative to computed tomography for the detection of lung cancer. It will also develop artificial intelligence tools to aid in the diagnosis of lung cancer on chest tomosynthesis images. The main questions it aims to answer are: * What is the accuracy of chest X-ray tomosynthesis in diagnosing lung cancer in a population of individuals undergoing lung cancer screening or evaluation of a suspicious lung nodule? * Can artificial intelligence help us detect lung cancer on chest tomosynthesis images? Researchers will compare chest tomosynthesis images to computed tomography scans for each participant to see how they compare in diagnosing lung cancer. Participants will a chest tomosynthesis scan in addition to their routine clinical computed tomography scan.
Detailed description
Lung cancer remains the most common cause of cancer death in the United States for which low-dose CT has proven benefit for early detection and survival from lung cancer. However, adoption remains low. Furthermore, \>95% of nodules detected on low-dose CT, especially those smaller than 6 mm, do not represent cancer. We have partnered to develop a novel chest x-ray tomosynthesis (CXRT) device with the hypothesis that this device might be an alternative to CT for detection of lung cancer. We seek to recruit a cohort of patients to undergo CXRT, composed of patients concurrently undergoing lung cancer screening CT and diagnostic CT for new lung cancer. We will determine the effectiveness of CXRT for detecting lung cancer in this population, evaluating its sensitivity and specificity for detecting cancer and lung nodules at multiple size thresholds in a multireader study. We will additionally develop artificial intelligence algorithms and evaluate their efficacy to further enhance cancer detection.
Interventions
DIAGNOSTIC_TESTChest X-ray Tomosynthesis
An imaging device that uses X-rays projected from multiple angles to reconstruct a three-dimensional images of the chest
Sponsors
AIxSCAN, Inc.
University of California, San Diego
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
30 Years to 85 Years
Healthy volunteers
Yes
Inclusion criteria
* undergoing lung cancer screening * undergoing evaluation of suspicious pulmonary nodule * newly diagnosed lung cancer
Exclusion criteria
* prior history of lung cancer treatment
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Chest X-ray tomosynthesis detection of lung cancer | through 12/31/2026 | Diagnostic accuracy of chest tomosynthesis in identifying biopsy-proven lung cancer |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Chest X-ray tomosynthesis detection of suspicious nodules | through 12/31/2026 | Diagnostic accuracy of chest tomosynthesis in identifying pulmonary nodules seen on chest CT at multiple minimum size thresholds: 6-mm, 8-mm, 10-mm |
Countries
United States
Contacts
Primary ContactAlexander Cypro, MD
Outcome results
None listed