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.

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

This study includes patients who underwent surgical treatment for non-small cell lung cancer (NSCLC) at Ondokuz Mayis University Hospital in Samsun, Türkiye. Eligible patients are selected from the hospital's electronic medical records and radiologic imaging archive between January 2010 and March 2025. The population reflects a clinical sample from a tertiary referral center serving a diverse adult patient population in the Black Sea region of Türkiye.

De-identified individual participant data (e.g., imaging features, demographic variables, survival outcomes) may be shared for academic and research purposes upon reasonable request and following institutional data-sharing agreements. No personal identifiers will be included.