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AI reads lung cancer scans to predict best treatment

NCT ID NCT07449858

Knowledge-focused Sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University Source: ClinicalTrials.gov ↗

What the study statuses mean

This study's is highlighted.

Recruitment status, easiest to join first

Recruiting now
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.
Paused
Paused for now. It may or may not start again.
Ongoing This study
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 Results may not be published yet.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This status has not been confirmed recently, so it may be out of date.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 Jun 27, 2026 · Last updated Jun 27, 2026

Summary

This study looks at whether computer analysis of PET/CT scans can help doctors more accurately stage non-small cell lung cancer and predict genetic mutations. Researchers will use data from 500 patients to build and test machine learning models. The goal is to provide better, personalized treatment guidance without needing extra invasive tests.

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 could lead to more accurate, non-invasive ways to stage lung cancer and predict gene mutations, helping doctors choose the best treatment for each patient.
What could go wrong
This is a retrospective study using existing data, not a clinical trial testing a new treatment. The models may not work as well in real-world settings or for all patient groups.

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 500 people

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

Started

Jul 2025

Expected to finish

Dec 2027

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

Patients with pathologically confirmed Non-Small Cell Lung Cancer (NSCLC) who underwent standard preoperative whole-body 18F-FDG PET/CT examinations at The Second Affiliated Hospital of Zhejiang University School of Medicine and other participating tertiary hospitals.

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: * Age \>= 18 years. * Underwent standard whole-body 18F-FDG PET/CT scan within 30 days before surgery. * Histopathologically confirmed Non-Small Cell Lung Cancer (NSCLC) with clear histological subtyping and complete postoperative TNM staging. * Primary tumor SUVmax \> 2.5 and maximum diameter \> 1.0 cm on CT. * Complete clinical, pathological, and imaging data available. * (For Gene Sub-study) Known EGFR gene mutation status. * (For Prognosis Sub-study) Complete follow-up data available (minimum 12 months or until endpoint event). Exclusion Criteria: * History of other malignancies. * Received any anti-tumor treatment (chemotherapy, radiotherapy, targeted therapy, immunotherapy) prior to PET/CT. * Severe image artifacts or indistinct tumor boundaries affecting ROI delineation. * Missing key clinical or pathological data. * Baseline PET/CT evaluated recurrent or metastatic tumors instead of primary NSCLC. * Extremely short life expectancy due to severe comorbidities.

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Conditions

The condition(s) this trial relates to.

non-small cell lung carcinoma

As listed by the trial registrant

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

Non-small cell lung cancer

Contacts and locations

Locations

  • Guangdong Second Provincial General Hospital

    Guangzhou, Guangdong, 510317, China

  • The First Hospital of China Medical University

    Shenyang, Liaoning, 110001, China

  • The Second Affiliated Hospital, Zhejiang University School of Medicine

    Hangzhou, Zhejiang, 310009, China

  • West China Hospital of Sichuan University

    Chengdu, Sichuan, 610041, China

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