An Integration of a Computed Tomography/Positron Emission Tomography/Whole Slide Image (CT/PET/WSI) Based Deep Learning Signature for Predicting Complete Pathological Response to Neoadjuvant Chemoimmunotherapy in Non-small Cell Lung Cancer: A Multicenter Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 100
- 试验地点
- 3
- 主要终点
- Area under the receiver operating characteristic curve
研究概览
简要总结
The purpose of this study is to evaluate the performance of a CT/PET/ WSI-based deep learning signature for predicting complete pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 20 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Age ranging from 20-75 years;
- •Patients who underwent curative surgery after neoadjuvant chemoimmunotherapy for NSCLC;
- •Obtained written informed consent.
排除标准
- •Missing image data;
- •Pathological N3 disease.
结局指标
主要结局
Area under the receiver operating characteristic curve
时间窗: 2023.5.1-2023.10.31
The area under the receiver operating characteristic curve (ROC) of the deep learning model in predicting complete pathological response (CPR). CPR was defined as no residual tumor in both resected primary tumor and lymph nodes. Patients with non-small cell lung cancer receiving neoadjuvant chemoimmunotherapy will achieve either CPR or non-CPR, which can be confirmed by pathological examination after surgical resection. And the model will output the predictive value (CPR/non-CPR) for each patient receiving neoadjuvant chemoimmunotherapy.
次要结局
- Sensitivity(2023.5.1-2023.10.31)
研究者
Chang Chen
Professor
Shanghai Pulmonary Hospital, Shanghai, China