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AI trained to spot aggressive lung cancer before surgery

NCT ID NCT07820423

Diagnosis Sponsor: Central South University Source: ClinicalTrials.gov ↗

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Recruitment status, easiest to join first

Recruiting now This study
This trial is taking on new participants right now.
Not yet recruiting
Registered, but not yet taking participants.
By invitation only
Not open to general applications. Only people the study team invites can take part.
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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 Results may not be published yet.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.
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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
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Status unknown
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First seen Sep 15, 2026 · Last updated Sep 16, 2026 · Updated 1 time

Summary

Researchers are developing an artificial intelligence model that reads preoperative CT scans and clinical data to predict whether lung cancer associated with cystic airspaces has high-risk features. The study includes 600 patients with non-small cell lung cancer who had surgery and complete imaging. The goal is to see if combining imaging and clinical information improves risk prediction compared with using either alone.

What this could mean

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

Active substance
an artificial intelligence model that combines CT imaging and clinical data
What this could lead to
If it works, this could give surgeons a non-invasive way to spot aggressive lung cancer before operating, helping them plan the right treatment sooner.
What could go wrong
This is an observational study that only looks at existing scans and records, so the model may not predict accurately in real time or in different hospitals. It also cannot prove that using the model improves patient outcomes.

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 600 people

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

Started

May 2025

Expected to finish

Dec 2026

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

This observational study will include patients with pathologically confirmed non-small cell lung cancer (NSCLC) who underwent pulmonary tumor resection and had preoperative CT features consistent with lung cancer associated with cystic airspaces (LCCA). Patients with complete imaging, clinical, and pathological data will be included for model development and validation.

Ages

18 years and older

Sex

Anyone

Healthy volunteers

Not accepted

This study is not open to healthy volunteers. The entry requirements below say who it is open to.

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\. Patients with non-small cell lung cancer (NSCLC) confirmed by biopsy or postoperative pathological examination. 2\. Patients who underwent surgical resection of a pulmonary tumor, including lobectomy, segmentectomy, or wedge resection. 3\. Patients with complete preoperative chest CT imaging data. 4. Patients whose preoperative chest CT showed a well-defined air-containing cystic component within the tumor, consistent with the radiological features of lung cancer associated with cystic airspaces (LCCA). 5\. Patients with available clinical and pathological data required for analysis. Exclusion Criteria: * 1\. Patients with a history of pulmonary diseases that may cause cystic lung lesions, such as pulmonary tuberculosis, pulmonary fungal infection, lymphangioleiomyomatosis (LAM), or Birt-Hogg-Dubé (BHD) syndrome. Emphysema will not be considered an exclusion criterion; however, patients with severe emphysema will be excluded if it significantly affects the identification, boundary delineation, or imaging feature assessment of the target lesion. 2\. Patients who received systemic antitumor therapy before enrollment, including chemotherapy, radiotherapy, targeted therapy, or immunotherapy. 3\. Patients with other primary malignancies. 4. Patients with missing preoperative chest CT images or CT images of insufficient quality for analysis. 5\. Patients without a definite pathological diagnosis or with incomplete pathological results. 6\. Patients with missing clinical data.

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Conditions

The condition(s) this trial relates to.

lung cancer non-small cell lung carcinoma

As listed by the trial registrant

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

Lung cancer associated with cystic airspaces

How to take part

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  1. The places running it

    1 site. The list below names each one and where it is.

  2. The official record

    ClinicalTrials.gov lists the study team's own contact details, including names and phone numbers. We don't republish those.

    Open the record ↗

  3. A doctor treating you

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Contacts and locations

Locations

  • The Second Xiangya Hospital of Central South University

    RECRUITING

    Changsha, Hunan, 410000, China

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