NCT05542992招募中不适用
Deep Learning Model Supplementary PET-CT as a More Effectively Diagnostic Method for Pure Solid Nodules Classification: a Multicenter Observational Study
Chang Chen5 个研究点 分布在 1 个国家目标入组 260 人开始时间: 2022年1月1日最近更新:
适应症
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 260
- 试验地点
- 5
- 主要终点
- AUC
研究概览
简要总结
The purpose of this study is to compare the predictive performance of a CT-based deep learning model for pure-solid nodules classification and compared with the tumor maximum standardized uptake value on PET in a multicenter prospective cohort.
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
- 18 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Participants scheduled for surgery for radiological finding of pulmonary pure-solid lesions from the preoperative thin-section CT scans;
- •The maximum short-axis diameter of lymph nodes less than 3 cm on CT scan;
- •Age ranging from 18-75 years;
- •definied pathological examination report available;
- •Obtained written informed consent.
排除标准
- •Multiple lung lesions;
- •Poor quality of CT images;
- •Participants with incomplete clinical information;
- •Participants who have received neoadjuvant therapy before initial CT evaluation.
结局指标
主要结局
AUC
时间窗: 2022.01-2023.12
Area under the curve of the receiver operating characteristic
次要结局
- Specificity(2022.01-2023.12)
- PPV(2022.01-2023.12)
- NPV(2022.01-2023.12)
- Accuracy(2022.01-2023.12)
- sensitivity(2022.01-2023.12)
研究者
Chang Chen
Professor
Shanghai Pulmonary Hospital, Shanghai, China
研究点 (5)
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