Deep-radiomics-learning for mass detection in CT colonography

用于 CT 结肠成像中质量检测的深度放射组学学习

• 批准号: 9167836
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9167836
• 关键词: Abdomen; Adoption; Advisory Committees; American Cancer Society; American College of Radiology; Anatomy; Biological Markers; Biological Neural Networks; Cancer Etiology; Categories; Cessation of life; Characteristics; Classification; Clinical; Collection; Colon; Colon Carcinoma; Colonoscopy; Colorectal; Colorectal Cancer; Computed Tomographic Colonography; Databases; Detection; Development; Diagnosis; Early Diagnosis; Evaluation; Excision; Fatigue; Goals; Guidelines; Heating; Image; Image Analysis; Learning; Lesion; Location; Malignant Neoplasms; Maps; Measurement; Methods; Performance; Polyps; Population; Prevention; Reader; Reading; Reporting; Research; Safety; Scheme; Societies; System; Testing; Time; United States; Validation; Woman; X-Ray Computed Tomography; abstracting; accurate diagnosis; base; compliance behavior; computer aided detection; cost effective; cost effectiveness; design; diagnostic accuracy; innovation; learning strategy; men; mortality; novel; prevent; radiologist; radiomics; screening; two-dimensional

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.

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。但是，它 可以通过早期检测和去除其前体病变来预防。 CT结肠造影（CTC）的使用 将大大提高结直肠的访问，容量，安全性，成本效益和患者依从性 考试。通过使用第一阅读器计算机 - 辅助检测（FR-CADE）范式，放射科医生仅自动检查病变候选者 通过计算机辅助检测（CADE）系统。但是，由于Cade系统可能会错过大型群众，所以 放射科医生仍然需要对结肠的CT图像进行额外的二维（2D）评论，该图像 平均增加40％以上的阅读时间。此外，放射学家偶尔也会错过某些类型的 CTC图像上的质量。该项目的目的是为开发深入的放射线学习（DERALE）方案 在CTC图像上检测大量肿块。该计划将用于整合深度学习方法和 放射性生物标志物对CTC图像进行完整的自动评论，以可靠检测结直肠 群众。我们假设Derale方案将能够以灵敏度检测结直肠肿块 与独立的专家放射科医生相媲美，可以用来减少fr-的解释时间 CADE在CTC中不降低诊断精度而不会降低。我们将评估和比较分类性能 Derale与无辅助专家放射科医生的作品，并进行观察者绩效研究以比较 在FR-CADE范式中使用Derale的检测准确性与无助的专家放射科医生 从CTC图像中检测质量。在FR-Cade中成功发展和广泛采用Derale 范式将有助于早期，准确和具有成本效益的诊断，因此将降低死亡率 结肠癌是美国癌症死亡最大的威胁之一。