NCT04270799UnknownNot Applicable
Lung Nodule Imaging Biobank for Radiomics and AI Research
Royal Marsden NHS Foundation Trust5 sites in 1 country1,000 target enrollmentStarted: June 1, 2020Last updated:
Conditions
Trial Snapshot
- Phase
- Not Applicable
- Enrollment
- 1,000
- Locations
- 5
- Primary Endpoint
- Development of an imaging biobank
Study Overview
Brief Summary
This study will collect retrospective CT scan images and clinical data from participants with incidental lung nodules seen in hospitals across London. The investigators will research whether machine learning can be used to predict which participants will develop lung cancer, to improve early diagnosis.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- •Age > 18
- •Baseline CT thorax imaging reported as having pulmonary nodule(s) between 5 and 30mm in the last 10 years.
- •Ground truth known (either scan data showing stability for 2 years (based on diameter) or one year (based on volumetry), complete resolution, or biopsy-proven malignancy.
- •Slice thickness < 2.5mm.
Exclusion Criteria
- •• Absence of at least one technically adequate CT thorax imaging series (defined by visual inspection of presence of imaging data of the thorax in the DICOM record).
- •Slice thickness > 2.5mm.
- •Imaging > 10 years old.
- •Ground truth unknown.
Outcomes
Primary Outcomes
Development of an imaging biobank
Time Frame: 1 year
The primary endpoint will be met if we are able to store baseline CT scans and the minimum clinical data set for 1000 patients.
Secondary Outcomes
- Discovery of a CT-thorax based radiomics profile to predict cancer risk.(1 year)
Investigators
Study Sites (5)
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