NCT06810349招募中不适用
A Multicenter Study on Predicting Tumor Origin Based on Deep Learning of Lymph Node Puncture Cytology
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 10,000
- 试验地点
- 1
- 主要终点
- Model performance metrics
研究概览
简要总结
In this study, the investigators aimed to construct a deep learning diagnostic model that uses cytological images to predict primary unknown tumor origins in patients with tumors combined with lymph node metastases. After the model is constructed, the model will be validated by a large-scale test set to test the model performance. The investigators also propose to compare the performance of the constructed model in diagnosing cytology smears compared to human pathologists.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •From West China Hospital of Sichuan University (October 1, 2008-August 31, 2024) with corresponding clinical data, including age, sex, specimen puncture site, pathologic diagnosis, pathologic type, whether immunocytochemistry was added, clinical diagnosis, lesion site, co-morbidities, history of malignancy, treatment modality, occurrence of postoperative complications, total number of days of hospitalization postoperatively, and survival time;
- •From the Department of Pathology of the First Affiliated Hospital of Zhengzhou University, the Sichuan Provincial Cancer Hospital, and the Cancer Hospital of the Chinese Academy of Medical Sciences (January 1, 2020-August 31, 2024) with corresponding clinical data, including age, sex, specimen puncture site, pathologic diagnosis, pathologic type, whether immunocytochemistry was added, clinical diagnosis, lesion site, co-morbidities, history of malignancy, treatment modality, occurrence of postoperative complications, total number of days of hospitalization postoperatively, and survival time.
排除标准
- •Images lacking any supporting clinical or pathologic evidence to support a primary origin and its corresponding clinical information;
- •Blank, poorly focused, and low-quality images containing severe artifacts and their corresponding clinical information.
结局指标
主要结局
Model performance metrics
时间窗: 1 year
Model performance was evaluated by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Sensitivity and Specificity.
次要结局
未报告次要终点
研究者
Jianyong Lei
Professor
West China Hospital
研究点 (1)
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