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Proceedings Paper

The Lung Image Database Consortium (LIDC): a quality assurance model for the collection of expert-defined truth in lung-nodule-based image analysis studies
Author(s): Samuel G. Armato; Rachael Y. Roberts; Geoffrey McLennan; Michael F. McNitt-Gray; David Yankelevitz; Ella A. Kazerooni; Edwin J. R. van Beek; Heber MacMahon; Denise R. Aberle; Charles R. Meyer; Anthony P. Reeves; Claudia I. Henschke; Eric A. Hoffman; Barbara Y. Croft; Laurence P. Clarke
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Paper Abstract

The development of computer-aided diagnostic (CAD) systems requires an initial establishment of "truth" by expert human observers. Potential inconsistencies in the "truth" data must be identified and corrected before investigators can rely on this data. We developed a quality assurance model to supplement the "truth" collection process for lung nodules on CT scans. A two-phase process was established for the interpretation of CT scans by four radiologists. During the initial "blinded read," radiologists independently assigned lesions they identified into one of three categories: "nodule ⩾ 3mm," "nodule < 3mm," or "non-nodule ⩾ 3mm." During the subsequent "unblinded read," the blinded read results of all radiologists were revealed. The radiologists then independently reviewed their marks along with their colleague's marks; a radiologist's own marks could be left unchanged, deleted, switched in terms of lesion category, or additional marks could be added. The final set of marks underwent quality assurance, which consisted of identification of potential errors that occurred during the reading process and error correction. All marks were visually grouped into discrete nodules. Six categories of potential error were defined, and any nodule with a mark that satisfied the criterion for one of these categories was referred to the radiologist who assigned the mark in question. The radiologist either corrected the mark or confirmed that the mark was intentional. A total of 829 nodules were identified by at least one radiologist in 100 CT scans through the two-phase process designed to capture "truth." The quality assurance process yielded 81 nodules with potential errors. The establishment of "truth" must incorporate a quality assurance model to guarantee the integrity of the "truth" that will provide the basis for the training and testing of CAD systems.

Paper Details

Date Published: 30 March 2007
PDF: 7 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 651429 (30 March 2007); doi: 10.1117/12.713227
Show Author Affiliations
Samuel G. Armato, The Univ. of Chicago (United States)
Rachael Y. Roberts, The Univ. of Chicago (United States)
Geoffrey McLennan, Univ. of Iowa (United States)
Michael F. McNitt-Gray, Univ. of California, Los Angeles (United States)
David Yankelevitz, Cornell Univ. (United States)
Ella A. Kazerooni, Univ. of Michigan (United States)
Edwin J. R. van Beek, Univ. of Iowa (United States)
Heber MacMahon, The Univ. of Chicago (United States)
Denise R. Aberle, Univ. of California, Los Angeles (United States)
Charles R. Meyer, Univ. of Michigan (United States)
Anthony P. Reeves, Cornell Univ. (United States)
Claudia I. Henschke, Cornell Univ. (United States)
Eric A. Hoffman, Univ. of Iowa (United States)
Barbara Y. Croft, National Cancer Institute (United States)
Laurence P. Clarke, National Cancer Institute (United States)


Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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