Deep radiomic colon cleansing for laxative-free CT colonography

深度放射组学结肠清洗，用于无泻药 CT 结肠成像

• 批准号: 9297792
• 负责人: Janne Johannes Nappi
• 金额: $ 25.65万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-05 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9297792
• 关键词: Address; Advisory Committees; Air; Area; Benefits and Risks; Cancer Etiology; Carcinoma; Cessation of life; Clinical; Colon; Colon Carcinoma; Colonoscopy; Colorectal; Colorectal Cancer; Computed Tomographic Colonography; Computer Assisted; Consensus; Contrast Media; Databases; Dehydration; Detection; Development; Diagnosis; Diarrhea; Dose; E-learning; Early Diagnosis; Excision; Feces; Goals; Guidelines; Height; Human; Image; Ingestion; Intestines; Iodine; Learning; Lesion; Low Dose Radiation; Malignant Neoplasms; Medical Societies; Medicare; Methods; Morphologic artifacts; Multi-Institutional Clinical Trial; Optics; Oral; Osmolar Concentration; Patients; Performance; Polypectomy; Polypoid Lesion; Polyps; Population; Preparation; Problem Solving; Radiation; Reader; Risk; Safety; Scheme; Societies; Source; Thinness; United States; Woman; base; compliance behavior; computer aided detection; cost; image processing; improved; laxative; learning strategy; men; minimally invasive; mortality; novel; older patient; prevent; radiation risk; radiologist; radiomics; screening; soft tissue; spectrograph; virtual

Project Summary/Abstract Colon cancer, the second leading cause of cancer deaths for men and women in the United States, can be prevented by early detection and removal of its precursor lesions. Computed tomographic colonography (CTC), also known as virtual colonoscopy, could substantially increase the capacity, safety, and patient compliance of colorectal examinations. However, an FDA panel has recently identified two remaining concerns about CTC: patient adherence, and the detection of small polyps and flat lesions. Our clinical multi-center trial showed that laxative-free preparation by oral ingestion of a contrast agent (iodine) to indicate fecal materials for electronic cleansing (EC), followed by computer-aided detection (CADe), makes CTC easy to tolerate for patients while enabling the detection of ≥10 mm lesions at sensitivity comparable to that of optical colonoscopy. However, small polyps and flat lesions were a significant source of false negatives, because EC produced image artifacts that imitated such lesions. Because laxative-free CTC addresses the concern of patient adherence, the only remaining concern about CTC is the detection of small polyps and flat lesions. The goal of this project is to develop a novel multi-material deep-learning scheme, hereafter denoted as Deep- ECAD, that integrates EC and CADe for the detection of small polyps and flat lesions in laxative-free spectral CTC (spCTC), where spectral imaging and deep learning will be used to overcome the above limitations of conventional CTC. Our specific aims are to (1) establish a laxative-free ultra-low-dose spCTC image database, (2) develop a multi-material deep-learning method for EC, (3) develop deep radiomic detection of small polyps and flat lesions, and (4) evaluate the clinical benefit of Deep-ECAD with laxative-free cases. Successful development of the proposed Deep-ECAD scheme will substantially improve human readers’ performance in the detection of small polyps and flat lesions while minimizing the inconveniences of bowel preparation and radiation risk to patients. Such a scheme will make laxative-free spCTC a highly accurate and acceptable screening option for large populations, in particular, Medicare population, leading to an increased screening rate, promoting early diagnosis of colon cancer, and ultimately reducing mortality due to colon cancer.

项目摘要/摘要 结肠癌是美国癌症死亡男性和女性的第二大原因，可以是 通过早期检测和去除其前体病变。 （CTC），也称为虚拟结肠镜检查，可以大幅提高，安全性和患者 结直肠检查的合规性。 关于CTC：患者的依从性，并检测小息肉和我们的临床多中心试验。 表明通过口服摄入的无泻药制剂 用于电子清洁（EC），然后进行计算机辅助检测（CADE），使CTC易于耐受 患者在对光学的敏感性下可检测≥10mM病变 结肠镜检查。 制作的图像伪像模仿这种病变。 患者依从性，唯一对CTC的关注是数据息肉和扁平病变 该项目的目标是制定一种新型的多物质深度学习计划，以下称为深度 - ECAD，将EC和CADE整合起来，以检测无泻药光谱中的小息肉和扁平病变 CTC（SPCTC），而光谱成像和深度学习将用于克服上述局限 常规的CTC。 （2）开发一种用于EC的多物质深度学习方法，（3）发展小息肉的深度放射原理检测 和（4）通过泻药训练病例评估深摄影的临床益处。 支撑深入的学校计划的发展将大大改善人类读者的表演者 检测小息肉和扁平病变，同时最大程度地减少了肠制剂的不便和 对患者的辐射风险。 尤其是Medicare人群的大型Poptions的筛选选项，导致筛查不足 速度，促进结肠癌的早期诊断，最终降低了因结肠癌而导致的死亡率。