Study of Optellums AI Lung Cancer Prediction Tool to Improve Patient Care and Health Economics
for Patients with Solid or Part-Solid Pulmonary Nodules Detected by CT Scans
Led by Nottingham University Hospitals NHS Trust · Updated on 2025-04-08
2000
Participants Needed
10
Research Sites
N/A
Total Duration
AI-Summary
What this Trial Is About
Researchers are conducting a multi-centre prospective observational study to evaluate patients with solid and part-solid pulmonary nodules detected on routine CT chest scans. The goal is to assess whether the use of an AI-based Lung Cancer Prediction LCP tool influences physician decision-making and leads to clinical and health-economic benefits compared to the current standard care for these patients. This study focuses on nodules sized between 5 and 30 mm that meet specific imaging criteria.
Participants will be observed without any assigned treatments as this is an observational study. The study involves evaluating baseline CT scans that meet defined quality standards and include nodules with a solid component of at least 80%. Training will be provided on the CT scan requirements. No experimental interventions are administered, but the impact of the AI tool on clinical management, use of scans and procedures, adherence to guidelines, and patient outcomes will be studied over up to one year.
During the study, researchers will collect data on patient care and outcomes related to pulmonary nodules, including discharge rates and health economics. Assessments will include reviewing clinical decisions, imaging usage, and adherence to guidelines over a one-year period. Participants will be followed through their routine care with no additional treatments required, and their progress will be monitored to measure the potential effects of the AI tool on service utilization and patient outcomes.
CONDITIONS
Brief Title
DOLCE: Determining the Impact of Optellum's Lung Cancer Prediction Solution
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Have a baseline CT scan showing at least one solid or part-solid pulmonary nodule with a solid component of 80% or more
Nodule is not fully calcified
Nodule size between 5 and 30 mm in maximum axial diameter
Baseline CT includes at least one scan series meeting VNC instructions for use
Scan includes at least one full-inspiration breath-hold without high contrast media and no major quality issues
You will not qualify if you...
Diagnosis of cancer within the last 5 years
Presence of thoracic implants affecting nodule imaging
More than five pulmonary nodules reported, excluding fully calcified nodules
Additional nodules already under follow-up per standard care
Nodules with pure ground glass opacity 5 mm or larger
Nodules larger than 30 mm in maximum axial diameter
Your Study Journey
Screening
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person)
Long-term Monitoring
Duration - Up to 1 year
Participants are observed to assess the impact of the lung cancer prediction solution on clinical management, procedure use, and patient outcomes over time.
Visits as per routine clinical care up to 1 year
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
0
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Published Research Related To This Trial
Determining the impact of an artificial intelligence tool on the management of pulmonary nodules detected incidentally on CT (DOLCE) study protocol: a prospective, non-interventional multicentre UK study.