NCT05736991招募中不适用
Deep Learning Signature Based on PET-CT Images for Predicting the Novel Grading System of Clinical Stage I Lung Adenocarcinoma
Shanghai Pulmonary Hospital, Shanghai, China4 个研究点 分布在 1 个国家目标入组 600 人开始时间: 2022年11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
- 600
- 试验地点
- 4
- 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 PET/ CT-based deep learning signature for predicting the grade 3 tumors based on the novel grading system in clinical stage stage I lung adenocarcinoma based on 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
- 20 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •(1) Participants scheduled for surgery for radiological finding of pulmonary lesions from the preoperative thin-section CT scans; (2) The maximum diameter of lesion less than 4 cm on CT scans; (3) The maximum short axis diameter of lymph nodes less than 1 cm on CT scan; (4) The SUVmax of hilar and mediastinal lymph nodes less than 2.5; (5) Pathological confirmation of primary lung adenocarcinoma; (5) Age ranging from 20-75 years; (6) Obtained written informed consent.
排除标准
- •(1) Multiple lung lesions; (2) Poor quality of PET-CT images; (3) Participants with incomplete clinical information; (4) Mucinous adenocarcinomas; (5) Participants who have received neoadjuvant therapy.
结局指标
主要结局
Area under the receiver operating characteristic curve
时间窗: 2022.11-2023.4
Area under the receiver operating characteristic curve
次要结局
- Sensitivity(2022.11-2023.4)
- Negative predictive value(2022.11-2023.4)
- Specificity(2022.11-2023.4)
- Positive predictive value(2022.11-2023.4)
- Accuracy(2022.11-2023.4)
研究者
Chang Chen
Professor
Shanghai Pulmonary Hospital, Shanghai, China
研究点 (4)
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