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临床试验/NCT03553589
NCT03553589Unknown不适用

Minimally and Non-invasive Methods for Early Detection and Progression of Endometrial Cancer

Andrea Romano5 个研究点 分布在 3 个国家目标入组 400 人开始时间: 2018年10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
400
试验地点
5
主要终点
Creation of a diagnostic algorithm

研究概览

简要总结

Endometrial cancer (EC) is the most frequent gynecological malignancy but there is currently lack of both non-invasive diagnostic tools and novel markers to stratify patients based on their risk of future recurrence. Patient care could be improved by advances in these two aspects.

In the present study, the investigators aim to identify diagnostic serum metabolite and protein biomarker signatures for early detection of cancer in asymptomatic high-risk population and prognostic biomarkers for selection of patients with poor prognosis.

详细描述

Rationale: Endometrial cancer (EC) is the most frequent gynaecological malignancy in the developed world. Optimal treatment of EC depends on early diagnostics and pre-operative stratification to appropriately select the extent of surgery and to plan further therapeutic approach. Currently, invasive endometrial histology is the gold standard for diagnosis, as there are no valid non-invasive methods available, and patient stratification is based on histopathology and surgical findings. There is a great need for efficient and reliable screening test for asymptomatic women with high risk of EC including Lynch syndrome patients and tamoxifen treated patients. In addition, a prognostic test is needed to stratify pre-operatively EC patients with high risk of progression in need of radical surgery together with adjuvant chemo/ratio therapy from EC patients with good prognosis. In this project the investigators are addressing this lack of non-invasive diagnostic and prognostic biomarkers of EC.

Objective

the investigators aim to identify diagnostic serum metabolite and protein biomarker signatures for early detection of cancer in asymptomatic high-risk population and (secondary objective) prognostic biomarkers for selection of patients with poor prognosis.

研究设计

研究类型
Observational
观察模型
Case Control
时间视角
Cross Sectional

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
Female
接受健康志愿者

入选标准

  • 未提供

排除标准

  • 未提供

结局指标

主要结局

Creation of a diagnostic algorithm

时间窗: 2020-2021

Blood metabolome and proteome will be analysed and bioinformatics/biostatistical analysis will be used to derive diagnostic algorithms based on blood metabolites, proteins and clinical data. Algorithms in the biomarker discovery study will be developed by comparing EC and patients with benign uterine pathologies.

次要结局

  • Creation of a prognostic algorithm(2021)

研究者

发起方
Andrea Romano
申办方类型
Other
责任方
Sponsor Investigator
主要研究者

Andrea Romano

PhD. Assistant Professor

Academisch Ziekenhuis Maastricht

研究点 (5)

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