Deep radiomic colon cleansing for laxative-free CT colonography

项目总结/摘要 结肠癌是美国男性和女性癌症死亡的第二大原因， 通过早期发现和切除其前驱病变来预防。ct结肠成像 (CTC)，也称为虚拟结肠镜检查，可以大大提高容量，安全性和患者 结肠直肠检查的依从性。然而，FDA的一个小组最近发现了两个仍然存在的问题， 关于CTC：患者依从性，以及小息肉和扁平病变的检测。我们的临床多中心试验 显示通过口服造影剂（碘）指示粪便物质的无泻药制剂 电子清洗（EC），其次是计算机辅助检测（CADe），使CTC容易容忍， 患者，同时能够检测≥10 mm病变，灵敏度与光学 结肠镜检查然而，小息肉和扁平病变是假阴性的重要来源，因为EC 产生模仿这种病变的图像伪影。因为不含泻药的CTC解决了 患者依从性，关于CTC唯一剩下的问题是小息肉和扁平病变的检测。的 该项目的目标是开发一种新的多材料深度学习方案，以下简称为Deep- ECAD，集成了EC和CADe，用于在无泻药光谱中检测小息肉和扁平病变。 CTC（spCTC），其中光谱成像和深度学习将用于克服上述局限性， 常规CTC。我们的具体目标是（1）建立一个无泻药的超低剂量spCTC图像数据库， (2)开发用于EC的多材料深度学习方法，（3）开发小息肉的深度放射组学检测 和平坦病变，以及（4）评价无泻药病例的Deep-ECAD的临床受益。成功 拟议的Deep-ECAD方案的开发将大大提高人类读者的性能， 检测小息肉和扁平病变，同时最大限度地减少肠道准备的不便， 对患者的辐射风险。这种方案将使无泻药的spCTC成为高度准确和可接受的 为大量人群，特别是医疗保险人群提供筛查选项，从而增加筛查 提高结肠癌的发病率，促进结肠癌的早期诊断，并最终降低结肠癌的死亡率。

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.