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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/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，它通过早期发现并去除其前体病变可以预防。 CT结肠成像（CTC）的使用将大幅提高结直肠癌的可及性、容量、安全性、成本效益和患者依从性考试。通过使用初级读者计算机来解释 CTC 考试将是最有效的辅助检测 (FR-CADe) 范例，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 CADe 系统可能会错过大量质量，放射科医生仍然需要对结肠的 CT 图像进行额外的二维 (2D) 审查，这阅读时间平均增加 40% 以上。此外，放射科医生有时也会错过某些类型的检查。 CTC 图像上的质量。该项目的目标是开发一个深度 RAdiomics 学习 (DERALE) 方案 CTC 图像上大质量的检测。该方案将用于整合深度学习方法和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 方案将能够以一定的灵敏度检测结直肠肿块与无辅助放射专家的诊断相当，并且它可以用来减少 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 与无辅助的专家放射科医生的研究，并进行观察者表现研究来比较 FR-CADe 范式中使用 DERALE 的检测准确性与独立专家放射科医生在 FR-CADe 范式中的检测准确性从 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促进早期、准确和具有成本效益的诊断，从而降低死亡率结肠癌是美国癌症死亡的最大威胁之一。