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

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.

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Key information

Conditions

Non-small Cell Lung Cancer Artificial Intelligence (AI) in Diagnosis Bronchial Neoplasms Carcinoma, Bronchogenic Carcinoma, Non-Small-Cell Lung Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

AI-Based Predictive Modeling

Other

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.

Other names: Deep Learning Algorithm, Retrospective Imaging Analysis

Primary outcomes

Development of AI Model for Predicting Tumor Stage and Survival

Time frame: 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.

Sponsors and collaborators

Lead sponsor

Hilkat Fatih Elverdi

Other

Registry information

Official study title

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

Important dates

Study start: 2010
Primary completion: 2026
Study completion: 2026
First posted: Jul 16, 2025
Registry last updated: Jul 15, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed