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Laser and AI join forces to spot aggressive lung cancer

NCT ID NCT07799038

Diagnosis Sponsor: Shanghai Zhongshan Hospital Source: ClinicalTrials.gov ↗

What the study statuses mean

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Recruitment status, easiest to join first

Recruiting now
This trial is taking on new participants right now.
Not yet recruiting This study
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
Paused
Paused for now. It may or may not start again.
Ongoing
Running, but no longer taking on new participants.
Completed
The trial has finished. Results may not be published yet.
Stopped early
Stopped early, before it reached the end. That can be for many reasons, including safety.
Cancelled
Cancelled before anyone took part.

Expanded access (not trials)

Expanded access
Not a trial. This treatment can be requested outside a study, case by case, for people who qualify.
Expanded access (paused)
Not a trial. The treatment can normally be requested outside a study, but is unavailable right now.
Expanded access (ended)
Not a trial. The treatment could once be requested outside a study, but no longer can.
Approved
The treatment has been approved, so it is available normally rather than through this programme.

When the status isn't known

Details not published
The full record has not been published yet, so there is little to show here.
Status unknown
This status has not been confirmed recently, so it may be out of date.

First seen Sep 02, 2026 · Last updated Sep 03, 2026 · Updated 1 time

Summary

This trial tests whether a new imaging method, using femtosecond laser light and artificial intelligence, can accurately predict high-risk features in lung cancer, such as spread to lymph nodes. Researchers will compare the AI's predictions with actual pathology results from surgical specimens. The study involves 333 adults with lung nodules who are scheduled for surgery. If the method works, it could help doctors make more informed treatment decisions without waiting for traditional lab tests.

What this could mean

Our plain-language read of the trial. This is informational only, not medical advice or a prediction.

Active substance
Femtosecond laser label-free imaging combined with artificial intelligence
What this could lead to
If successful, this could provide a faster, more accurate way to predict whether lung cancer has spread to lymph nodes, helping doctors tailor surgery and treatment.
What could go wrong
This is an early diagnostic study, and the technology may not prove accurate enough for routine use. Results are blinded and won't affect patient care during the trial.

This is an AI summary of the original study and may miss details. Read our disclaimer.

Study facts

What this study's own registry entry says, in plain language.

Participants

About 333 people

The number the study aims to enrol. It can still change while the study runs.

Expected to start

Sep 2026

An estimate. Start dates often move.

Expected to finish

Aug 2028

An estimate. End dates often move.

Lead sponsor

Other sponsor

The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.

Who can take part

This study's own entry requirements. Only the study team can say for certain whether you qualify.

Who is studied

Lung cancer patients undergoing pulmonary resection.

Ages

Children (under 18), adults (18 to 64) and older adults (65 and over)

Sex

Anyone

Show the full entry requirements

Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.

Inclusion Criteria: 1. Pulmonary nodules detected by clinical imaging with an indication for surgical resection. 2. Patient agrees to and is planned for pulmonary (partial) resection. 3. Resected specimens are suitable for FLI imaging. 4. Written informed consent obtained from the patient or legal representative. 5. Patients undergoing pulmonary (partial) resection at our hospital during this study. Exclusion Criteria: 1. Insufficient sample. 2. Specimen with crushing, contamination, or improper preservation, precluding valid imaging as judged by the investigator. 3. Inability to obtain matched pathology results corresponding to FLI images. 4. Inability to obtain final pathological diagnosis. 5. Other conditions deemed by the investigator as unsuitable for study participation.

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Conditions

The condition(s) this trial relates to.

disease lung cancer lung neoplasm

As listed by the trial registrant

The condition terms exactly as the trial's registrant entered them.

Lung cancer (diagnosis)

How to take part

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  1. The official record

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