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DIOPTRA - EARLY DYNAMIC SCREENING FOR COLORECTAL CANCER VIA NOVEL PROTEIN BIOMARKERS REFLECTING BIOLOGICAL INITIATION MECHANISMS

DIOPTRA - 通过反映生物起始机制的新型蛋白质生物标志物对结直肠癌进行早期动态筛查

基本信息

批准号：
10056682
负责人：
金额：
$ 33.97万
依托单位：
CAMBRIDGE MEDICAL ACADEMY LTD
依托单位国家：
英国
项目类别：
EU-Funded
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=10056682
关键词：
DIOPTRA EARLY DYNAMIC SCREENING COLORECTAL

项目摘要

DIOPTRA aims to be the driving force for creating change in colorectal cancer screening and everyday medical practice by incorporating new technologies for colorectal cancer (CRC) risk assessment, screening, and progression. It aspires to provide accessible and noninvasive risk estimation while offering robust evidence-based indications of the relationship between undiagnosed cancer patients and CRC, known as Cancer of Unknown Primary (CUP). DIOPTRA's objective is to build a strong evidence base for a unified holistic protocol for primary CRC screening, which can be applied to both undiagnosed cancer patients (through scheduled screening recommendations) and CUP patients with the CRC phenotype. To achieve this goal, we will incorporate lifestyle and environmental factors and evaluate the predictive power of investigated biomarkers and gene-expression profiling, aiming to uncover the molecular mechanisms of CRC development and CUP incidence. In this context, we will also examine the efficacy of network modeling and AI-based Decision Support Systems (DSS) for early primary CRC identification. We will train a set of models using large volumes of primary CRC data, including in situ and metastatic carcinomas, using the latter as a knowledge base for CUP analysis.

DIOPTRA旨在通过整合用于结直肠癌（CRC）风险评估、筛查和进展的新技术，成为结直肠癌筛查和日常医疗实践变革的驱动力。它致力于提供可访问和非侵入性的风险评估，同时提供未诊断的癌症患者与CRC之间关系的强大循证指征，称为未知原发性癌症（CUP）。DIOPTRA的目标是为初级CRC筛查的统一整体方案建立强有力的证据基础，该方案可应用于未确诊的癌症患者（通过计划的筛查建议）和具有CRC表型的CUP患者。为了实现这一目标，我们将结合生活方式和环境因素，并评估所研究的生物标志物和基因表达谱的预测能力，旨在揭示CRC发展和CUP发病率的分子机制。在这种情况下，我们还将研究网络建模和基于AI的决策支持系统（DSS）对早期原发性CRC识别的有效性。我们将使用大量的原发性CRC数据（包括原位癌和转移癌）训练一组模型，将后者用作CUP分析的知识库。