Spectral precision imaging for early diagnosis of colorectal lesions with CT colonography

摘要 结肠癌是美国男性和女性癌症死亡的第二大原因， 虽然它可以通过早期发现和切除其前驱病变来预防。计算机断层 结肠镜检查（CTC）可以大大增加结肠直肠检查的容量、安全性和患者依从性， 考试然而，目前用于CTC和光学结肠镜检查的泻药肠道准备标准 (OC)患者耐受性差，被认为是结肠直肠检查的主要障碍。 我们先进的非泻药多中心计算机辅助CTC试验表明，非泻药CTC很容易 患者可以耐受，使用计算机辅助检测（CADe）的放射科医生可以检测到大息肉， 在非泻药CTC中具有高灵敏度，与OC相当。然而，SF6损伤（锯齿状） 病变、高度<3 mm的扁平病变和大小为6 - 9 mm的息肉）是假阴性的重要来源 试验过程中非泻药CTC中这些SF6损伤的检测和可视化的挑战是由以下原因引起的： 这主要是由于目前的单能量CTC技术无法区分软组织、粪便 标记，以及它们与管腔空气的部分体积。我们建议采用多光谱CTC精密成像 和人工智能（AI）来克服非泻药CTC的这些固有局限性。我们的目标是 该项目旨在为多光谱多材料（MUSMA）开发一种新的深度学习AI（DEEP-AI）方案 精确成像，将使用高质量光谱CTC（spCTC）精确图像的深度超级学习 以提高非泻药CTC的诊断性能。我们假设（1）高质量的MUSMA 可以从超低剂量（<1 mSv）spCTC扫描重建精确图像，（2）DEEP-AI将产生 ≥6 mm SF6-病变的检测灵敏度与OC相当，（3）使用DEEP-AI作为首选 阅片员将显著提高放射科医生对SF6病变的检测性能， 与无辅助阅读相比，它将产生与OC相当的检测精度。我们 具体目标是：（1）建立非泻药spCTC和MUSMA精密图像数据库，（2）开发 DEEP-AI解释系统，用于SF6病变的可视化和检测，以及（3）评估临床 DEEP-AI解释系统对非泻药spCTC病例的益处。成功发展 拟议的DEEP-AI解释系统将大大提高人类读者在阅读方面的表现。 从非泻药CTC检查中检测SF6病变，解决患者依从性问题 结直肠癌筛查指南这样的方案将使非泻药CTC成为高度准确且 为大量人群提供可接受的筛查选择，提高结直肠筛查率，促进 结肠癌的早期诊断，并最终降低结肠癌的死亡率。

Abstract Colon cancer is the second leading cause of cancer deaths for men and women in the United States, even though it could be prevented by early detection and removal of its precursor lesions. Computed tomographic colonography (CTC) could substantially increase the capacity, safety, and patient compliance of colorectal examinations. However, the current standard of cathartic bowel preparation for CTC and optical colonoscopy (OC) is poorly tolerated by patients and has been recognized as a major barrier to colorectal examinations. Our advanced non-cathartic multi-center computer-assisted CTC trial showed that non-cathartic CTC is easily tolerated by patients and that radiologists who use computer-aided detection (CADe) can detect large polyps in size in non-cathartic CTC with high sensitivity, comparable to that of OC. However, SF6-lesions (serrated lesions, flat lesions <3 mm in height, and polyps 6 – 9 mm in size) were a significant source of false negatives in the trial. The challenges of detection and visualization of these SF6-lesions in non-cathartic CTC are caused largely by the inability of the current single-energy CTC technique to differentiate between soft tissues, fecal tagging, and their partial volumes with lumen air. We propose to employ multi-spectral CTC precision imaging and artificial intelligence (AI) to overcome these inherent limitations of non-cathartic CTC. Our goal in this project is to develop a novel deep-learning AI (DEEP-AI) scheme for multi-spectral multi-material (MUSMA) precision imaging, which will use deep super-learning of high-quality spectral CTC (spCTC) precision images to boost the diagnostic performance of non-cathartic CTC. We hypothesize that (1) high-quality MUSMA precision images can be reconstructed from ultra-low-dose (<1 mSv) spCTC scans, (2) DEEP-AI will yield a detection sensitivity for ≥6 mm SF6-lesions comparable to that of OC, and that (3) the use of DEEP-AI as first reader will significantly improve radiologists’ detection performance for SF6-lesions and reduce interpretation time compared with unaided reading, and that it will yield a detection accuracy comparable to that of OC. Our specific aims are (1) to establish a non-cathartic spCTC and MUSMA precision image database, (2) to develop a DEEP-AI Interpretation System for visualization and detection of SF6-lesions, and (3) to evaluate the clinical benefit of the DEEP-AI Interpretation System with non-cathartic spCTC cases. Successful development of the proposed DEEP-AI Interpretation System will substantially improve human readers’ performance in the detection of SF6-lesions from non-cathartic CTC examinations that address the problem of patient adherence to colorectal screening guidelines. Such a scheme will make non-cathartic CTC a highly accurate and acceptable screening option for large populations, leading to an increased colorectal screening rate, promoting early diagnosis of colon cancer, and ultimately reducing mortality due to colon cancer.