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NCT07068139
AI-Based Prediction of Stage and Survival in Non-Small Cell Lung Cancer: A Retrospective Study
trial testing AI-Based Predictive Modeling in Non-Small Cell Lung Cancer in 150 participants. Participants enrolled and being followed up; not accepting new ones.
1 September 2025
Quick facts
| Lead sponsor | Hilkat Fatih Elverdi |
|---|---|
| Status | Active, enrolled |
| Study type | OBSERVATIONAL |
| Enrollment | 150 |
| Start date | 1 January 2010 |
| Primary completion | 1 September 2025 |
| Estimated completion | 1 September 2025 |
Drugs / interventions tested
- AI-Based Predictive Modeling
Conditions studied
- Non-Small Cell Lung Cancer — all drugs for Non-Small Cell Lung Cancer →
- Artificial Intelligence (AI) in Diagnosis — all drugs for Artificial Intelligence (AI) in Diagnosis →
Sponsor
Hilkat Fatih Elverdi
Who can join
18 and older, any sex, with Non-Small Cell Lung Cancer or Artificial Intelligence (AI) in Diagnosis. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
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.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
Verify or expand the search:
- PubMed search for NCT07068139
- Europe PMC full search
- ASCO Meeting Library
- ESMO Meeting Library
- bioRxiv preprints
- medRxiv preprints
- Google Scholar
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT07068139 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Hilkat Fatih Elverdi
- Last refreshed: 8 August 2025
Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07068139.
Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing