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肺部影像数据库联盟(LIDC):对放射科医生在CT扫描中识别肺结节的变异性评估

The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans.

作者信息

Armato Samuel G, McNitt-Gray Michael F, Reeves Anthony P, Meyer Charles R, McLennan Geoffrey, Aberle Denise R, Kazerooni Ella A, MacMahon Heber, van Beek Edwin J R, Yankelevitz David, Hoffman Eric A, Henschke Claudia I, Roberts Rachael Y, Brown Matthew S, Engelmann Roger M, Pais Richard C, Piker Christopher W, Qing David, Kocherginsky Masha, Croft Barbara Y, Clarke Laurence P

机构信息

The University of Chicago, Department of Radiology, MC 2026, The University of Chicago, 5841 S. Maryland Avenue, Chicago, IL 60637, USA.

出版信息

Acad Radiol. 2007 Nov;14(11):1409-21. doi: 10.1016/j.acra.2007.07.008.

Abstract

RATIONALE AND OBJECTIVES

The purpose of this study was to analyze the variability of experienced thoracic radiologists in the identification of lung nodules on computed tomography (CT) scans and thereby to investigate variability in the establishment of the "truth" against which nodule-based studies are measured.

MATERIALS AND METHODS

Thirty CT scans were reviewed twice by four thoracic radiologists through a two-phase image annotation process. During the initial "blinded read" phase, radiologists independently marked lesions they identified as "nodule >or=3 mm (diameter)," "nodule <3 mm," or "non-nodule >or=3 mm." During the subsequent "unblinded read" phase, the blinded read results of all four radiologists were revealed to each radiologist, who then independently reviewed their marks along with the anonymous marks of their colleagues; a radiologist's own marks then could be deleted, added, or left unchanged. This approach was developed to identify, as completely as possible, all nodules in a scan without requiring forced consensus.

RESULTS

After the initial blinded read phase, 71 lesions received "nodule >or=3 mm" marks from at least one radiologist; however, all four radiologists assigned such marks to only 24 (33.8%) of these lesions. After the unblinded reads, a total of 59 lesions were marked as "nodule >or=3 mm" by at least one radiologist. Twenty-seven (45.8%) of these lesions received such marks from all four radiologists, three (5.1%) were identified as such by three radiologists, 12 (20.3%) were identified by two radiologists, and 17 (28.8%) were identified by only a single radiologist.

CONCLUSION

The two-phase image annotation process yields improved agreement among radiologists in the interpretation of nodules >or=3 mm. Nevertheless, substantial variability remains across radiologists in the task of lung nodule identification.

摘要

原理与目的

本研究旨在分析经验丰富的胸部放射科医生在计算机断层扫描(CT)图像上识别肺结节的变异性,进而研究在基于结节的研究中作为对照的“真相”的确立过程中的变异性。

材料与方法

四位胸部放射科医生通过两阶段图像标注过程对30例CT扫描进行了两次评估。在初始的“盲读”阶段,放射科医生独立标记他们认为是“结节≥3毫米(直径)”、“结节<3毫米”或“非结节≥3毫米”的病变。在随后的“非盲读”阶段,向每位放射科医生展示所有四位放射科医生的盲读结果,然后他们独立复查自己的标记以及同事的匿名标记;放射科医生自己的标记随后可以删除、添加或保持不变。这种方法旨在尽可能全面地识别扫描中的所有结节,而无需强行达成共识。

结果

在初始盲读阶段后,至少有一位放射科医生将71个病变标记为“结节≥3毫米”;然而,所有四位放射科医生仅将其中24个(33.8%)病变标记为此类。在非盲读之后,至少有一位放射科医生将总共59个病变标记为“结节≥3毫米”。其中27个(45.8%)病变被所有四位放射科医生标记为此类,3个(5.1%)被三位放射科医生标记为此类,12个(20.3%)被两位放射科医生标记为此类,17个(28.8%)仅被一位放射科医生标记为此类。

结论

两阶段图像标注过程提高了放射科医生在解释≥3毫米结节方面的一致性。然而,在肺结节识别任务中,不同放射科医生之间仍存在显著变异性。

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本文引用的文献

1
Evaluation of lung MDCT nodule annotation across radiologists and methods.
Acad Radiol. 2006 Oct;13(10):1254-65. doi: 10.1016/j.acra.2006.07.012.
2
Pulmonary nodule detection with low-dose CT of the lung: agreement among radiologists.
AJR Am J Roentgenol. 2005 Oct;185(4):973-8. doi: 10.2214/AJR.04.1225.
3
Lung nodule detection and characterization with multi-slice CT.
J Thorac Imaging. 2005 Aug;20(3):196-209. doi: 10.1097/01.rti.0000171625.92574.8d.
5
Lung image database consortium: developing a resource for the medical imaging research community.
Radiology. 2004 Sep;232(3):739-48. doi: 10.1148/radiol.2323032035.
10
3-D imaging with MDCT.
Eur J Radiol. 2003 Mar;45 Suppl 1:S37-41. doi: 10.1016/s0720-048x(03)00035-4.

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