Artificial Intelligence (AI) in Diagnosis, Non-Small Cell Lung Cancer
Conditions
Keywords
Non-Small Cell Lung Cancer, Artificial Intelligence, Deep Learning, Radiology, Thoracic Surgery
Brief 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.
Interventions
OTHERAI-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.
Sponsors
Hilkat Fatih Elverdi
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Development of AI Model for Predicting Tumor Stage and Survival | From data extraction to completion of model training and validation (estimated by September 2025) | The primary outcome of this study is to develop and validate a deep learning-based artificial intelligence model that can predict postoperative tumor stage and survival in patients with non-small cell lung cancer using preoperative PET-CT and chest CT imaging data. The primary outcome will be considered achieved when at least 80% of the planned patient dataset (150 patients) has been successfully included and used for model development. |
Outcome results
None listed