Deep-radiomics-learning for mass detection in CT colonography

项目摘要/摘要 结肠癌是美国男性和女性癌症死亡的第二大原因。然而，它 可通过及早发现和清除其前驱病变来预防。CT结肠成像(CTC)的应用 将极大地提高结直肠手术的可及性、容量、安全性、成本效益和患者依从性 考试。CTC考试的口译最有效的办法是使用一台一读计算机-- 辅助检测(FR-CADE)范例，其中放射科医生仅检查自动检测到的候选病变 通过计算机辅助检测(CADE)系统。然而，由于凯德系统可能会错过大质量的物体， 放射科医生仍然需要对结肠的CT图像进行额外的二维(2D)检查，这 阅读时间平均增加40%以上。此外，放射科医生偶尔也会漏掉一些类型的 CTC图像上的肿块。该项目的目标是为以下项目开发深度辐射组学学习(DERALE)计划 CTC图像上大团块的检测。该方案将用于集成深度学习方法和 放射组学生物标记物对CTC图像执行完整的自动检查，以可靠地检测结直肠 群众。我们假设DERALE方案将能够敏感地检测到结直肠肿块 可与无人协助的专家放射科医生相媲美，并可用于减少FR- 在不降低CTC诊断准确性的情况下进行CADE检查。我们将对分类性能进行评估和比较 并进行一项观察员表现研究，以比较 在FR-CADE范例中使用DERALE与无辅助专家放射科医生在 从CTC图像中检测肿块。DERALE在FR-CADE中的成功开发和广泛采用 范型将有助于早期、准确和经济有效的诊断，从而将死亡率从 结肠癌是美国癌症死亡的最大威胁之一。

Project Summary/Abstract Colon cancer is the second leading cause of cancer deaths for men and women in the United States. However, it would be prevented by early detection and removal of its precursor lesions. The use of CT colonography (CTC) would substantially increase the access, capacity, safety, cost-effectiveness, and patient compliance of colorectal examinations. The interpretation of CTC examinations would be most effective by use of a first-reader computer- aided detection (FR-CADe) paradigm, where a radiologist reviews only the lesion candidates detected automatically by a computer-aided detection (CADe) system. However, because CADe systems can miss large masses, radiologists still need to perform an additional two-dimensional (2D) review of the CT images of the colon, which increases reading time over 40% on average. Furthermore, also radiologists can occasionally miss some types of masses on CTC images. The goal of this project is to develop a DEep RAdiomics LEarning (DERALE) scheme for the detection of large masses on CTC images. The scheme will be used to integrate deep learning methods and radiomic biomarkers to perform a complete automated review of CTC images for reliable detection of colorectal masses. We hypothesize that the DERALE scheme will be able to detect colorectal masses at a sensitivity comparable to that of unaided expert radiologists and that it can be used to reduce the interpretation time of FR- CADe without degrading diagnostic accuracy in CTC. We will evaluate and compare the classification performance of DERALE with that of unaided expert radiologists and conduct an observer performance study to compare the detection accuracy of the use of DERALE in the FR-CADe paradigm with that of unaided expert radiologists in the detection of masses from CTC images. Successful development and broad adoption of DERALE in the FR-CADe paradigm will facilitate early, accurate, and cost-effective diagnoses, and thus it will reduce the mortality rate from colon cancer, one of the largest threats of cancer deaths in the United States.