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。但 可以通过早期发现和切除其前驱病变来预防。CT结肠成像（CTC） 将大大增加结肠直肠癌的可及性、容量、安全性、成本效益和患者依从性， 考试使用第一阅读器计算机将最有效地解释CTC考试- 辅助检测（FR-CADe）范例，其中放射科医师仅审查自动检测到的病变候选者 计算机辅助检测（CADe）系统。然而，由于CADe系统可能会错过大的质量， 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， 平均增加阅读时间超过40%。此外，放射科医生偶尔也会错过某些类型的 CTC图像上的肿块。该项目的目标是开发一个深度放射学学习（DERALE）计划， CTC图像上大肿块的检测。该计划将用于整合深度学习方法， 放射组学生物标志物，对CTC图像进行完整的自动审查，以可靠地检测结直肠癌 群众我们假设DERALE方案将能够检测结直肠肿块的敏感性 与独立的放射科专家相当，可用于减少FR的解释时间， CADe不会降低CTC的诊断准确性。我们将评估和比较分类性能 DERALE与独立放射科专家的结果进行比较，并进行观察者表现研究， 在FR-CADe范例中使用DERALE的检测准确性与在 从CTC图像中检测肿块。DERALE在FR-CADe中的成功开发和广泛采用 范式将有助于早期，准确和具有成本效益的诊断，因此它将降低死亡率， 结肠癌是美国癌症死亡的最大威胁之一。