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Proceedings Paper

Volumetric measurements of pulmonary nodules: variability in automated analysis tools
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Paper Abstract

Over the past decade, several computerized tools have been developed for detection of lung nodules and for providing volumetric analysis. Incidentally detected lung nodules have traditionally been followed over time by measurements of their axial dimensions on CT scans to ensure stability or document progression. A recently published article by the Fleischner Society offers guidelines on the management of incidentally detected nodules based on size criteria. For this reason, differences in measurements obtained by automated tools from various vendors may have significant implications on management, yet the degree of variability in these measurements is not well understood. The goal of this study is to quantify the differences in nodule maximum diameter and volume among different automated analysis software. Using a dataset of lung scans obtained with both "ultra-low" and conventional doses, we identified a subset of nodules in each of five size-based categories. Using automated analysis tools provided by three different vendors, we obtained size and volumetric measurements on these nodules, and compared these data using descriptive as well as ANOVA and t-test analysis. Results showed significant differences in nodule maximum diameter measurements among the various automated lung nodule analysis tools but no significant differences in nodule volume measurements. These data suggest that when using automated commercial software, volume measurements may be a more reliable marker of tumor progression than maximum diameter. The data also suggest that volumetric nodule measurements may be relatively reproducible among various commercial workstations, in contrast to the variability documented when performing human mark-ups, as is seen in the LIDC (lung imaging database consortium) study.

Paper Details

Date Published: 21 March 2007
PDF: 7 pages
Proc. SPIE 6516, Medical Imaging 2007: PACS and Imaging Informatics, 651613 (21 March 2007); doi: 10.1117/12.711642
Show Author Affiliations
Krishna Juluru M.D., VA Maryland Health Care System (United States)
Johns Hopkins Univ. School of Medicine (United States)
Woojin Kim M.D., VA Maryland Health Care System (United States)
Hospital of the Univ. of Pennsylvania (United States)
William Boonn M.D., VA Maryland Health Care System (United States)
Hospital of the Univ. of Pennsylvania (United States)
Tara King, Univ. of Maryland School of Medicine (United States)
Khan Siddiqui M.D., VA Maryland Health Care System (United States)
Eliot Siegel M.D., VA Maryland Health Care System (United States)


Published in SPIE Proceedings Vol. 6516:
Medical Imaging 2007: PACS and Imaging Informatics
Steven C. Horii; Katherine P. Andriole, Editor(s)

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