An Exosome-Based Liquid Biopsy for the Differential Diagnosis of Primary Liver Cancer
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 400
- 试验地点
- 6
- 主要终点
- Sensitivity
研究概览
简要总结
It is sometimes difficult to precisely understand whether a primary liver cancer is a hepatocellular carcinoma or a cholangiocarcinoma. The researchers will develop and validate a liquid biopsy, based on exosomal content analysis and powered by machine learning, to help clinicians differentiate these two cancers before surgery.
详细描述
Primary liver cancers (PLCs) encompass a diverse group of malignancies originating from the liver, collectively ranking as the third leading cause of cancer-related mortality worldwide in 2020. Among PLCs, intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) represent the most predominant subtypes. Despite their collective grouping as PLCs, ICC and HCC patients exhibit distinct etiologies, pathologies, and clinical characteristics, necessitating different treatment approaches. Accurate differentiation between ICC and HCC is paramount to optimize patient outcomes and guide personalized treatment decisions. However, a definitive diagnosis is often obtained only after the pathological review of the resected neoplastic tissue, which requires invasive tumor sampling and poses risks of complications such as hemorrhage and tumor cell seeding. Consequently, there is a pressing clinical need to develop noninvasive diagnostic approaches to achieve an accurate differential diagnosis for patients with these distinct forms of PLCs.
This study involves the development and validation of a liquid biopsy, assessing circulating exosomal microRNAs (exo-miRNA) for indirect sampling of tumor tissue in the bloodstream. The researchers intend to harness machine learning and bioinformatics to create a cost-efficient, non-invasive, clinic-friendly assay with high sensitivity and specificity, aiding the differential diagnosis between ICC and HCC.
The researchers intend to do so in three phases:
- To perform comprehensive small RNA-Seq from exo-miRNA from patients with ICC and HCC.
- To develop and train a differential diagnosis panel based on advanced machine-learning models to obtain a final differential diagnosis biomarker.
- To validate the findings in an independent cohort of ICC and HCC.
In summary, this proposal promises to improve patient care and help clinicians perform a more reliable differential diagnosis between ICC and HCC in patients with primary liver cancer.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •A histologically confirmed diagnosis of hepatocellular carcinoma
- •A histologically confirmed diagnosis of intrahepatic cholangiocarcinoma
- •Received standard diagnostic and staging procedures as per local guidelines
- •Availability of at least one blood-derived sample, drawn before receiving any curative-intent treatment
排除标准
- •Lack of or inability to provide informed consent
- •Synchronous hepatocellular carcinoma and intrahepatic cholangiocarcinoma
- •Primary liver cancer other than hepatocellular carcinoma or intrahepatic cholangiocarcinoma
- •Secondary liver cancer
结局指标
主要结局
Sensitivity
时间窗: Through study completion, an average of 1 year
True Positive Rate: the probability of a positive test result, conditioned on the individual truly being positive
次要结局
- Specificity(Through study completion, an average of 1 year)
- Proportion of correct predictions (true positives and true negatives) among the total number of cases (i.e., accuracy)(Through study completion, an average of 1 year)