Next Generation Chest X-Ray Tomosynthesis for Screening of Lung Cancer
University of California, San Diego · with AIxSCAN, Inc. · NCT06577883
Opens the official ClinicalTrials.gov record, with the study team’s contacts. Bored Studies doesn’t enroll anyone.
Key facts
- Compensation
- Not posted. Ask the study team before screening.
- Who can join
- Ages 30–85 · All sexes
- Healthy volunteers
- Accepted
- Study type
- Observational
- Target enrollment
- 1000
- US sites recruiting
- 1
- Study start
- November 2, 2023
- Record last updated
- August 29, 2024
Status re-checked against ClinicalTrials.gov within the last 12 hours.
What the study is about
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.
Being studied: Chest X-ray Tomosynthesis
Conditions: Lung Cancer
Eligibility criteria
Copied from the official record. The study team makes the final decision at screening.
Show full criteria
Inclusion Criteria
- undergoing lung cancer screening
- undergoing evaluation of suspicious pulmonary nodule
- newly diagnosed lung cancer
Exclusion Criteria
- prior history of lung cancer treatment
Recruiting US locations
University of California San Diego
San Diego, CA 92093
Site contact: Alexander Cypro, MD · 858-246-2196 · aidaresearch2023@gmail.com
Study contacts (from the official record)
- Alexander Cypro, MD · 858-2462196 · aidaresearch2023@gmail.com
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