跳至主要内容
临床试验/NCT04000620
NCT04000620Unknown不适用

The Role of CNN Architecture-based Transfer Learning of Medical Imaging in Lung Cancer Diagnosis and Staging

Wuhan Union Hospital, China1 个研究点 分布在 1 个国家目标入组 500 人开始时间: 2018年5月1日最近更新:
适应症

试验速览

阶段
不适用
发起方
入组人数
500
试验地点
1
主要终点
pathologic result revealed cancer cell involvement in lesion

研究概览

简要总结

Lung cancer diagnosis and staging are two fundamental and critical issue in clinical lung cancer management and therapeutic decision-making. Invasive procedures for pathologic analysis are gold standard for diagnosis and staging, however, invasive procedures related-complications are inevitable. Noninvasive medical imaging is a powerful tool, however there is almost no room for improvement just according to the experience of radiologist and clinician. The researchers will investigate the role of computer based deep learning of medical imaging in the diagnosis of lesion of lung, lymph node and other sites suspected with metastasis.

详细描述

Radiologist

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

入选标准

  • Pathological diagnosis of lung cancer
  • PET/CT or CT examination before any cancer-specific treatment

排除标准

  • A history of other malignancies

结局指标

主要结局

pathologic result revealed cancer cell involvement in lesion

时间窗: 1 month after the pathologic test

次要结局

未报告次要终点

研究者

发起方
Wuhan Union Hospital, China
申办方类型
Other
责任方
Principal Investigator
主要研究者

Yang Jin

Professor

Wuhan Union Hospital, China

研究点 (1)

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