详情页 - 四川省肿瘤医院知识库

当前位置：首页 > 详情页

China Early Esophageal Cancer Cohort

10| 认领 | 链接全文 |

文献详情

编号/登记号：

注册时间：

研究开始时间：

研究结束时间：

项目类型：

本院角色：

多中心研究：

招募状态：

研究疾病/适应症：

研究类型：

研究负责人：

研究单位： [1]Changhai Hospital,Shanghai,Shanghai,China,200433 [2]Lianshui People's Hospital [3]The second affiliated hospital of Xuzhou medical university [4]The second people's hospital of Nanyang [5]Nanchong Central Hospital [6]Tongliao City Hospital [7]Yancheng First People's Hospital [8]The Second Hospital of Hebei Medical University [9]The Second Affiliated Hospital of Baotou Medical College [10]Affiliated Hospital of Southwest Medical University [11]Sichuan Cancer Hospital and Research Institute [12]The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School [13]Zhongda Hospital [14]The First Affiliated Hospital with Nanjing Medical University [15]West China Hospital [16]The First Affiliated Hospital of Soochow University [17]Jiangsu Province Hospital of Chinese Medicine [18]Shandong Provincial Hospital [19]Shanxi Provincial People's Hospital [20]Henan Provincial People's Hospital [21]Anyang Cancer Hospital

研究目的：

With the development of endoscopy, more and more patients with superficial esophageal squamous cell carcinoma (ESCC) receive endoscopic resection rather than traditional surgery. However, there is still no complete consensus on the risk factors for lymph node metastasis (LNM) and recurrence of ESCC after endoscopic resection. The existing curative resection criteria are strict to a certain extent, and a considerable number of patients are overtreated according to the current standard of additional treatment. Thus, we aim to conduct a multi-center retrospective and prospective study to construct a large cohort of ESCC based on the previously established multi-center collaborative research network and clinical big data, analyze the risk factors of LNM and recurrence in patients with early esophageal squamous cell carcinoma after ESD, establish an accurate and simple risk prediction model, and evaluate the discrimination efficiency of this model.