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

CMA：标记物辅助预防和风险分层 (MAPRS)：人工智能内窥镜预防结直肠癌 (CMA1)

• 批准号: 10084234
• 负责人: SATISH K SINGH
• 金额: --
• 依托单位: VA BOSTON HEALTH CARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10084234
• 关键词: Accounting; Address; Adoption; Algorithms; American; Antineoplastic Agents; Artificial Intelligence; Award; Benchmarking; Biological; Biological Markers; Biophotonics; Blood; Classification; Clinical; Clinical Data; Colonoscopes; Colonoscopy; Colorectal Cancer; Computer Assisted; Computer Models; Computer-Assisted Diagnosis; Computers; Cost Savings; Data; Data Set; Detection; Development; Diagnosis; Distant; Early Diagnosis; Endoscopes; Endoscopy; Ensure; Excision; Explosion; Gastrointestinal Endoscopy; Genomics; Healthcare; Histologic; Histology; Image; Image Enhancement; Infrastructure; Intervention; Knowledge; Label; Link; Machine Learning; Malignant Neoplasms; Methods; Modeling; Modernization; Mucins; Neoplasms; Neoplastic Polyp; Optics; Pathway interactions; Patients; Performance; Pharmaceutical Preparations; Polyps; Population; Precancerous Polyp; Precision therapeutics; Prevention; Procedures; Prognosis; Recurrence; Reporting; Research; Research Personnel; Risk; Sampling; Site; Societies; Technology; Testing; Therapeutic; Time; Tissues; Training; Translational Research; Tumor-Derived; Veterans; Work; algorithm development; base; biomarker development; biomarker panel; cancer risk; chromoscopy; classification algorithm; clinical biomarkers; clinical data repository; clinical imaging; clinical practice; colon cancer patients; colorectal cancer prevention; colorectal cancer risk; colorectal cancer screening; colorectal cancer treatment; combinatorial; cost; data repository; design; drug response prediction; evidence base; experience; falls; image archival system; improved; innovation; meetings; novel marker; personalized management; predictive modeling; preservation; pressure; randomized trial; repository; response; risk stratification; screening; skills; stem; success; synergism; tool; treatment strategy; tumor; virtual

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.

此协作功绩审查应用程序（CMA）旨在提高 癌症，特别是标记辅助的预防和风险分层（MAPRS） 癌症（CRC）。 CRC是美国第三大常见的癌症，占所有癌症的近10％ 退伍军人中的癌症。 MAPRS源自VA结直肠的一组研究人员 在基于VA现场会议的支持下创建的癌症细胞组合协作（VA4C） 奖。 VA4C旨在推进有关预防，早期检测， CRC的诊断，预后和治疗。拟议的CMA旨在破坏这些限制 并通过 推进MAPR。 MAPRS-CMA的目的是：CMA1）开发人工智能增强 预防结直肠癌的内窥镜检查； CMA2）检查基于粘蛋白的标记以改进 内窥镜检测，切除，组织学分类和肿瘤息肉的监测； CMA3）验证源自功能的组织和基于血液的组合生物标志物面板 途径特定的研究以改善风险分层；和CMA4）检查 细胞基因组药物反应分析，用于精确CRC治疗。 我们项目CMA1的主要目的是在VA中创建和建立基础架构 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。过去的十年中，生物流通技术发生了爆炸 更精确地诊断和治疗结肠肿瘤。然而，结果越来越多 信息密集的成像在过程中解释和互动。毫不奇怪， 技术的增强实践仍然仅限于学术中心的专家。我们的 假设是，可以通过计算机为任何操作员启用可靠的实时息肉组织学 使用ML/AI辅助诊断。这种能力最终将打开广泛采用的大门 节省成本的，ASGE批准的恢复和束缚，并为小小的范式 息肉。因此，该项目的具体目的是：目标1：创建一个大型，可扩展的标签 用于ML/AI研究的内窥镜数据库，该研究由来自多个的临床图像数据组成 VA中心。目标2：利用此图像存储库来开发和验证ML/AI模型 启用息肉的实时组织学以及目标3：为计算机辅助开发ML模型 息肉检测与基于粘蛋白的荧光生物标志物共同检测，用于广场检测。目的 4：使用ML/AI来帮助预测基于临床组合因素和的CRC药物反应 细胞基因组数据。