CT-based Radiomic Signature Can Identify Adenocarcinoma Lung Tumor Histology
Lung cancer remains the leading cause of cancer related mortality worldwide, with more than 1.5 million related deaths annually. Lung cancer is divided into two main groups: Small Cell Lung Carcinoma (SCLC) and Non-Small Cell Lung Carcinoma (NSCLC), with prevalence of ~20% and 80% respectively. NSCLC is further subdivided into adenocarcinoma (the most common), squamous cell carcinoma (SCC), and large cell carcinoma. Furthermore, each subtype is likely to have specific mutations, which could be targeted for treatment.
Medical imaging and radiomics feature extraction represent a candidate alternative to conventional tissue biopsy, a theory that is investigated in this study.
研究概览
研究类型
注册 (预期的)
联系人和位置
学习地点
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Limburg
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Maastricht、Limburg、荷兰、6229ER
- Maastricht University
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参与标准
资格标准
适合学习的年龄
- 孩子
- 成人
- 年长者
接受健康志愿者
有资格学习的性别
取样方法
研究人群
描述
Inclusion Criteria:
- Availability of diagnostic non-contrast enhanced CT scan.
- Availability of histologic tumor analysis results
Exclusion Criteria:
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学习计划
研究是如何设计的?
设计细节
队列和干预
团体/队列 |
干预/治疗 |
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Maastro (Lung1)
Open source dataset available at TCIA.org.
The cohort includes CT scans of 422 patients diagnosed with NSCLC.
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Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
其他名称:
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UCSF
A cohort of patients diagnosed with NSCLC at UCSF medical center.
It includes CT scans of 165 patients.
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Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
其他名称:
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Radboud
A cohort of patients diagnosed with NSCLC at Radboud medical center.
It includes CT scans of 255 patients.
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Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
其他名称:
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Stanford
Open source dataset available at TCIA.org.
The cohort includes CT scans of 211 patients diagnosed with NSCLC.
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Radiomics -the high throughput extraction of quantitative features from medical imaging- extract features that might potentially decode biologic tumor information, which might ultimately reduce the need to use invasive procedure, such as tissue biopsy.
其他名称:
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研究衡量的是什么?
主要结果指标
结果测量 |
措施说明 |
大体时间 |
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Lung histology
大体时间:December 2019
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Is the tumor under investigation an adenocarcinoma of the lung?
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December 2019
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合作者和调查者
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (预期的)
研究完成 (预期的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
此信息直接从 clinicaltrials.gov 网站检索,没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求,请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改,我们的网站上也会自动更新.
Virtual biopsy的临床试验
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Hospital Universitari Vall d'Hebron Research Institute主动,不招人
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IRCCS Eugenio Medea招聘中
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