The Role of Artificial Intelligence in Predicting Stage and Survival in Non-Small Cell Lung Cancer
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 156
- 主要终点
- Development of AI Model for Predicting Tumor Stage and Survival
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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
研究组 & 干预措施
NSCLC Surgery Cohort
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.
干预措施: AI-Based Predictive Modeling (Other)
结局指标
主要结局
Development of AI Model for Predicting Tumor Stage and Survival
时间窗: 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.
Development of AI Model for Predicting Tumor Stage and Survival
时间窗: 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.
次要结局
未报告次要终点
研究者
Hilkat Fatih Elverdi
Thoracic Surgery Resident
Ondokuz Mayıs University