AI reads CT scans to predict lung cancer risk years in advance
NCT ID NCT07647692
First seen Jun 19, 2026 · Last updated Jun 19, 2026
Summary
This study is developing an artificial intelligence model that analyzes a patient's CT scans over time to predict whether a lung nodule will grow or turn into cancer. Researchers will look at scans from nearly 5,000 people to train the AI to spot patterns that humans might miss. The goal is to help doctors decide which nodules need treatment and which can be safely watched.
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Summaries may miss details or leave out important information. Before applying or accepting participation, make sure you have read and understood the full study. Curemydisease.com takes no responsibility whatsoever for anything missed, misunderstood, or acted upon as a result of our summary — we know it does not capture everything.
This is a summary of the original study . Summaries may miss details or leave out important information. Before applying or accepting participation, make sure you have read and understood the full study. Curemydisease.com takes no responsibility whatsoever for anything missed, misunderstood, or acted upon as a result of our summary — we know it does not capture everything.
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Contacts and locations
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Study contacts
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Contact
Phone: •••-•••-•••• Email: •••••@•••••
Locations
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Peking University People's Hospital
Beijing, Beijing Municipality, 100044, China
Contact Phone: •••-•••-•••• Email: •••••@•••••
What this could mean
Our plain-language read of the trial. This is informational only — not medical advice or a prediction.
What this could lead to
If successful, this AI model could help doctors better predict which lung nodules are likely to turn into cancer, allowing earlier treatment and reducing unnecessary procedures.
What could go wrong
This is an observational study, not a treatment trial. The AI model is still being developed and tested, so it may not be accurate enough for widespread use. Results may vary across different patient groups.
Conditions
The condition(s) this trial relates to.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.