Department of Radiology, The University of Chicago, USA.
Med Phys. 2011 Feb;38(2):915-31. doi: 10.1118/1.3528204.
The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.
Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.
The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings.
The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.
通过建立一个特征良好的计算机断层扫描(CT)扫描数据库,可以促进肺部结节检测、分类和定量评估的计算机辅助诊断(CAD)方法的发展。肺部图像数据库联盟(LIDC)和图像数据库资源倡议(IDRI)完成了这样一个数据库,为医学成像研究社区建立了一个公开可用的参考。该数据库由美国国立癌症研究所(NCI)发起,由美国国立卫生研究院基金会(FNIH)进一步推进,并由美国食品和药物管理局(FDA)积极参与,这是一个基于共识的合作过程的成功范例。
七个学术中心和八个医学成像公司合作,共同确定、解决和处理具有挑战性的组织、技术和临床问题,为建立一个强大的数据库奠定了坚实的基础。LIDC/IDRI 数据库包含 1018 例病例,每例病例均包含来自临床胸部 CT 扫描的图像和一个相关的 XML 文件,该文件记录了由四名有经验的胸部放射科医生进行的两阶段图像注释过程的结果。在初始盲法阅读阶段,每位放射科医生独立地审查了每一次 CT 扫描,并标记属于三个类别之一的病变(“结节>=3 毫米”、“结节<3 毫米”和“非结节>=3 毫米”)。在随后的非盲法阅读阶段,每位放射科医生独立地审查了他们自己的标记以及其他三位放射科医生的匿名标记,以做出最终意见。这个过程的目的是尽可能完整地识别每一次 CT 扫描中的所有肺部结节,而不需要强制达成共识。
该数据库包含至少一位放射科医生标记的 7371 个“结节”病变。其中 2669 个病变被至少一位放射科医生标记为“结节>=3 毫米”,其中 928 个(34.7%)病变被所有四位放射科医生标记为“结节>=3 毫米”。这 2669 个病变包括结节轮廓和主观结节特征评分。
LIDC/IDRI 数据库有望成为一个重要的医学成像研究资源,推动 CAD 在临床实践中的开发、验证和传播。