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

Hilkat Fatih Elverdi

Status: Active, not recruiting

Conditions: Artificial Intelligence (AI) in Diagnosis; Non-Small Cell Lung Cancer

Treatments: Other: AI-Based Predictive Modeling

Study type: Observational

Funder types: Other

Identifiers

NCT07068139

B.30.2.ODM.0.20.08/194-318

Details and patient eligibility

About

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.

Enrollment: 150 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

150 participants in 1 patient group

NSCLC Surgery Cohort

Description:

This cohort includes patients who were diagnosed with non-small cell lung cancer (NSCLC) and underwent surgical treatment at Ondokuz Mayis University Hospital. Preoperative PET-CT and chest CT images and corresponding postoperative histopathological data were retrospectively collected and analyzed to develop an artificial intelligence model for predicting tumor stage and survival.

Treatment:

Other: AI-Based Predictive Modeling