Making PET-like images from CT to improve lung cancer diagnosis and care
Official title: Synthetic PET From CT Improves Precision Diagnosis and Treatment of Lung Cancer: a Prospective, Observational, Multicenter Study
Run by Shanghai Pulmonary Hospital, Shanghai, China · NCT07243873 · Data updated 2026-11-12
This project will test whether PET-like images generated from routine diagnostic CT scans can help doctors diagnose, stage, and plan treatment for people with non-small cell lung cancer who are having PET-CT and pathology tests.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 85 Years |
| Sex | All |
| Enrollment | 10000 (estimated) |
| Sponsor | Shanghai Pulmonary Hospital, Shanghai, China Academic / other |
| Locations | 5 sites (Zunyi, Guizhou and 4 other locations) |
| Status | Recruiting |
| Started | December 1, 2025 |
| Main results expected | June 1, 2026 |
| Study ends | December 1, 2026 |
| Registry record updated | November 24, 2025 |
| Trial ID | NCT07243873 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults with suspected or confirmed non-small cell lung cancer who are scheduled for FDG-PET-CT and pathological examination and can give informed consent are the intended participants.
✕Probably not a fit
Patients without paired CT and FDG-PET scans, those with significant imaging artifacts, or patients with a history of other malignancies are unlikely to benefit from the model.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This prospective, observational multicenter effort develops a method to synthesize FDG-PET–style images from diagnostic CT by learning an anatomical-to-metabolic mapping from paired CT and PET scans. The model is trained on paired diagnostic CT and FDG-PET images and then applied prospectively to patients with non-small cell lung cancer undergoing standard PET-CT and pathological workup. Investigators will compare synthetic PET outputs with real FDG-PET images and with pathology and clinical outcomes to test whether the synthetic images add diagnostic or prognostic value. Because the design is observational, patients receive standard clinical care while the study focuses on image generation and predictive performance across participating hospitals.
Why it matters
Potential benefit: If successful, this approach could provide PET-like metabolic information from routine CT scans, potentially reducing the need for separate PET exams, shortening time to diagnosis, and improving treatment planning.
How similar studies have gone: Previous retrospective and single-center studies of AI-generated synthetic PET from CT have shown promising accuracy in pilot datasets, but large prospective multicenter clinical validation remains limited.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Patients with non-small cell lung cancer scheduled to undergo PET-CT and pathological examinations; 2. Voluntarily participate and sign an informed consent form; Exclusion Criteria: 1. History of other malignant tumours; 2. Image artefacts; 3. Without pathological diagnostic information; 4. Without paired CT and FDG-PET scan images.
Where it's running
-
Affiliated Hospital of Zunyi Medical University
Zunyi, Guizhou, China Recruiting -
The First Affiliated Hospital of Nanchang University
Jiangxi, Nanchang, China Recruiting -
Shanghai East Hospital
Shanghai, Shanghai Municipality, China Recruiting -
Shanghai Pulmonary Hospital
Shanghai, Shanghai Municipality, China Recruiting -
Ningbo No.2 Hospital
Ningbo, Zhejiang, China Recruiting
Study contacts
- Study coordinator: Xinchen Shen
- Email: shenxinchen9@163.com
- Phone: 17366690957
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.