A Hierarchical Multi-modal AI Framework for Pathological and Genetic Subtyping of Lung Cancer Based on PET/CT Imaging
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 5,500
- 试验地点
- 9
- 主要终点
- Accurate differentiation between small cell lung cancer and non-small cell lung cancer
研究概览
简要总结
PET/CT imaging and clinical information (age, gender, smoking history, family history of cancer, history of present illness, and several tumor biomarkers, etc.) were used to establish a hierarchical multi-modal AI framework for pathological and genetic subtyping of lung cancer
详细描述
The multi-modal AI framework is developed to facilitate a hierarchical and precise stratification process. The first level involves the accurate differentiation between small cell lung cancer and non-small cell lung cancer (NSCLC) in patients diagnosed with lung cancer. The second level entails the further categorization of NSCLC patients into adenocarcinoma, squamous cell carcinoma, and other less prevalent subtypes. The third level involves predicting the mutation status of the EGFR driver gene, which is most-commonly observed in patients with lung adenocarcinoma. The whole cohort was divided into the training cohort (retrospective), validation cohort (retrospective), test cohort (retrospective), and prospective cohort.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
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
- •Newly diagnosed NSCLC confirmed pathologically
- •Age ≥18 y
- •Underwent pre-treatment 18F-FDG PET/CT scan
- •No prior anti-tumor treatments
- •No history of other malignancies
排除标准
- •▪ Pure ground-glass nodules with no FDG uptake
研究组 & 干预措施
Training Cohort
All patients underwent pre-treatment 18F-FDG PET/CT scan.
干预措施: PET imaging analysis, data mining, and AI model developing (Other)
Validation Cohort
All patients underwent pre-treatment 18F-FDG PET/CT scan.
干预措施: PET imaging analysis, data mining, and AI model developing (Other)
Test cohort
All patients underwent pre-treatment 18F-FDG PET/CT scan.
干预措施: PET imaging analysis, data mining, and AI model developing (Other)
Prospective cohort
All patients underwent pre-treatment 18F-FDG PET/CT scan.
干预措施: PET imaging analysis, data mining, and AI model developing (Other)
结局指标
主要结局
Accurate differentiation between small cell lung cancer and non-small cell lung cancer
时间窗: 1 year
次要结局
- Histological subtyping of NSCLC, including adenocarcinoma, squamous cell carcinoma, and other NSCLC subtypes(1 year)