AI-Based Prediction of Stage and Survival in Non-Small Cell Lung Cancer: A Retrospective Study (NCT07068139) | Clinical Trial Compass
CompletedNot Applicable
AI-Based Prediction of Stage and Survival in Non-Small Cell Lung Cancer: A Retrospective Study
156 participantsStarted 2010-01-01
Plain-language summary
This study aims to evaluate the role of artificial intelligence (AI) in predicting disease stage and survival in patients diagnosed with non-small cell lung cancer (NSCLC). Using a retrospective design, the research will analyze radiologic imaging data (PET-CT and chest CT) and corresponding histopathological results of patients who underwent lung cancer surgery at Ondokuz Mayis University Hospital.
The goal is to develop and validate a deep learning-based AI model that can automatically assess preoperative radiologic features and estimate postoperative tumor stage and survival outcomes. By integrating radiologic data with confirmed pathological diagnoses, the AI system is expected to provide clinical decision support that can improve diagnostic speed, reduce human error, and help clinicians predict prognosis more accurately.
This study does not involve any experimental treatment or prospective follow-up of patients. All data will be collected from existing medical records. The findings may contribute to the digital transformation of healthcare and promote the use of AI tools in thoracic oncology.
Who can participate
Age range
18 Years
Sex
ALL
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Inclusion Criteria:
* Age ≥ 18 years
* Diagnosed with non-small cell lung cancer (NSCLC)
* Underwent surgical treatment for NSCLC at Ondokuz Mayis University Hospital
* Available preoperative PET-CT and chest CT imaging
* Available postoperative histopathological diagnosis and staging
* Signed informed consent form for data use in research
Exclusion Criteria:
* Age \< 18 years
* No available PET-CT or chest CT imaging in hospital records
* No available histopathological diagnosis in hospital records
* Diagnosed with a type of lung cancer other than NSCLC
* Patients who did not undergo surgery
* Patients who did not provide informed consent for retrospective data use
Questions worth asking your doctor
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
1Based on my diagnosis and history, is this trial worth exploring for me — or is there a standard treatment we should try first?
2What does this trial's phase tell us about how much is already known about its safety and benefit?
3What would taking part actually involve for me — visits, tests, time, and travel?
4What are the known and possible risks or side effects I should weigh, and how would they be monitored?
5If this trial isn't the right fit, what other options or trials would you suggest I look into?
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
Questions for the trial coordinator
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
1What does taking part actually involve week to week — how many visits, where, and how long does each one take?
2What costs are covered by the study, and what might I have to pay for myself, including travel, parking, or time off work?
3What happens during screening, and what happens if the study team confirms I don't meet the criteria after those tests?
4Who pays for the scans, blood work, and other tests the trial requires — the study, my insurance, or me?
5How will being in the trial affect my regular care, and will my own doctor stay informed and involved?
6Can I leave the trial at any point if I change my mind, and what would happen to my care if I do?
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
What they're measuring
1
Development of AI Model for Predicting Tumor Stage and Survival
Timeframe: From data extraction to completion of model training and validation (estimated by September 2025)