CMA: Marker-assisted prevention and risk stratification (MAPRS): Artificial Intelligence Endoscopy for Colorectal Cancer Prevention (CMA1)

这款协作式绩效审查应用程序(CMA)旨在推进 癌症，特别是结直肠癌的标志物辅助预防和风险分层(MAPRS) 癌症(CRC)。结直肠癌是美国第三常见的癌症，占所有癌症的近10% 退伍军人中的癌症。MAPRS来自退伍军人事务部结直肠癌的一组研究人员 癌症细胞基因组学协作(VA4C)，在退伍军人事务部实地会议的支持下创建 奖品。VA4C旨在推进预防、早期发现、 大肠癌的诊断、预后和治疗。拟议的CMA旨在打破这些限制 并通过以下方式显著提高CRC的预防、检测、风险分层和精准处理 推进中的MAPRS。MAPRS-CMA旨在：CMA1)发展人工智能-增强 用于结直肠癌预防的内窥镜检查；CMA2)检查基于粘蛋白的标志物以改善 肿瘤性息肉的内窥镜检查、切除、组织学分类和监测； CMA3)验证基于组织和血液的组合生物标志物面板 用于改善风险分层的特定路径研究；以及CMA4)检查 用于精确结直肠癌治疗的细胞基因组药物反应图谱。 我们项目CMA1的主要目标是在退伍军人事务部内创建和建立基础设施 使我们能够开发、验证和部署机器学习(ML)/人工智能(AI) 增强内窥镜检查的模型。过去的十年见证了生物光子学技术的爆炸性发展。 以便更准确地诊断和治疗结肠肿瘤。然而，结果是，越来越多的人 信息密集型成像，用于在操作过程中进行解释和交互。不足为奇的是， 实践的技术增强仍然局限于学术中心的专家。我们的 假设通过计算机可以为任何操作员提供可靠的实时息肉组织学检查- 使用ML/AI辅助诊断。这种能力最终将为广泛采用打开大门。 成本节约、ASGE批准的切除并丢弃和留下的小的范例 息肉。因此，该项目的具体目标是：目标1：创建一个大型的、可扩展的标签 用于ML/AI研究的内窥镜数据库，由从多个 退伍军人管理局中心。目标2：利用这个图像库来开发和验证ML/AI模型 实现息肉的实时组织学以及目标3：开发用于计算机辅助的ML模型 息肉检测与基于粘蛋白的荧光生物标记物相结合进行广泛领域的检测。目标 4：使用ML/AI结合临床因素和辅助预测结直肠癌药物反应 细胞基因组数据。

This collaborative merit review application (CMA) aims to advance the precision management of cancers, specifically marker-assisted prevention and risk stratification (MAPRS) of colorectal cancers (CRCs). The third most common cancer in the USA, CRC accounts for nearly 10% of all cancers among Veterans. MAPRS stems from a group of investigators from the VA Colorectal Cancer Cellgenomics Collaborative (VA4C), created with the support of a VA Field-based Meeting Award. The VA4C aims to advance basic/translational research on the prevention, early detection, diagnosis, prognosis and treatment of CRCs. The proposed CMAs aim to disrupt these limitations and significantly advance CRC prevention, detection, risk stratification and precision treatment by advancing MAPRS. MAPRS-CMA aims to: CMA1) develop artificial intelligence-enhanced endoscopy for colorectal cancer prevention; CMA2) examine mucin-based markers to improve endoscopic detection, resection, histological classification and surveillance of neoplastic polyps; CMA3) validate tissue and blood-based combinatorial biomarker panels derived from functional pathway-specific studies to improve risk stratification; and CMA4) examine the potential of cellgenomic drug-response profiling for precision CRC treatment. The main objective of our project, CMA1, is to create and establish within the VA an infrastructure to enable us to develop, validate, and deploy machine learning (ML) /artificial intelligence (AI) models to enhance endoscopy. The past decade has seen an explosion in biophotonic technologies to more precisely diagnose and treat colonic neoplasia. The result is, however, increasingly information-dense imaging to interpret and interact with during procedures. Not surprisingly, technological enhancement of practice has remained restricted to experts at academic centers. Our hypothesis is that reliable real-time polyp histology can be enabled for any operator by computer- assisted diagnosis using ML/AI. This capability would finally open the door to widespread adoption of cost-saving, ASGE-sanctioned resect-and-discard and leave-behind paradigms for diminutive polyps. Thus, the specific aims of this project are: Aim 1: To create a large, scalable labeled endoscopic databank for ML/AI research comprised of clinical image data uploaded from multiple VA centers. Aim 2: To utilize this image repository to develop and validate ML/AI models that enable real-time histology of polyps as well as Aim 3: To develop ML models for computer assisted polyp detection in conjunction with mucin-based fluorescent biomarkers for widefield detection. Aim 4: Use ML/AI to help predict CRC drug response based on combined clinical factors and cellgenomic data.