NCT07068139

AI-Based Prediction of Stage and Survival in Non-Small Cell Lung Cancer: A Retrospective Study

The Role of Artificial Intelligence in Predicting Stage and Survival in Non-Small Cell Lung Cancer

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 150 (estimated)
• Sponsor: Hilkat Fatih Elverdi · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Non-Small Cell Lung Cancer
• Artificial Intelligence (AI) in Diagnosis

Interventions

Type	Name	Description
OTHER	AI-Based Predictive Modeling	This is not a therapeutic or diagnostic intervention. The study uses a retrospective dataset of radiologic and pathological records to train and validate a deep learning model designed to predict tumor stage and survival in patients with non-small cell lung cancer (NSCLC). No experimental procedure is applied to participants.

Timeline

• Start date: 2010-01-01
• Primary completion: 2025-09-01
• Completion: 2025-09-01
• First posted: 2025-07-16
• Last updated: 2025-08-08