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Proceedings Paper

Effects of CT dose and nodule characteristics on lung-nodule detectability in a cohort of 90 national lung screening trial patients
Author(s): Stefano Young; Pechin Lo; John M. Hoffman; H. J. Grace Kim; Matthew S. Brown; Michael F. McNitt-Gray
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Paper Abstract

Lung cancer screening CT is already performed at low dose. There are many techniques to reduce the dose even further, but it is not clear how such techniques will affect nodule detectability. In this work, we used an in-house CAD algorithm to evaluate detectability. 90348 patients and their raw CT data files were drawn from the National Lung Screening Trial (NLST) database. All scans were acquired at ~2 mGy CTDIvol with fixed tube current, 1 mm slice thickness, and B50 reconstruction kernel on a Sensation 64 scanner (Siemens Healthcare). We used the raw CT data to simulate two additional reduced-dose scans for each patient corresponding to 1 mGy (50%) and 0.5 mGy (25%). Radiologists’ findings on the NLST reader forms indicated 65 nodules in the cohort, which we subdivided based on LungRADS criteria. For larger category 4 nodules, median sensitivities were 100% at all three dose levels, and mean sensitivity decreased with dose. For smaller nodules meeting the category 2 or 3 criteria, the dose dependence was less obvious. Overall, mean patient-level sensitivity varied from 38.5% at 100% dose to 40.4% at 50% dose, a difference of only 1.9%. However, the false-positive rate quadrupled from 1 per case at 100% dose to 4 per case at 25% dose. Dose reduction affected lung-nodule detectability differently depending on the LungRADS category, and the false-positive rate was very sensitive at sub-screening dose levels. Thus, care should be taken to adapt CAD for the very challenging noise characteristics of screening.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850G (24 March 2016); doi: 10.1117/12.2217405
Show Author Affiliations
Stefano Young, Univ. of California, Los Angeles (United States)
Pechin Lo, Univ. of California, Los Angeles (United States)
John M. Hoffman, Univ. of California, Los Angeles (United States)
H. J. Grace Kim, Univ. of California, Los Angeles (United States)
Matthew S. Brown, Univ. of California, Los Angeles (United States)
Michael F. McNitt-Gray, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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