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25 January 2020 Influence of background lung characteristics on nodule detection with computed tomography
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We sought to characterize local lung complexity in chest computed tomography (CT) and to characterize its impact on the detectability of pulmonary nodules. Forty volumetric chest CT scans were created by embedding between three and five simulated 5-mm lung nodules into one of three volumetric chest CT datasets. Thirteen radiologists evaluated 157 nodules, resulting in 2041 detection opportunities. Analyzing the substrate CT data prior to nodule insertion, 14 image features were measured within a region around each nodule location. A generalized linear mixed-effects statistical model was fit to the data to verify the contribution of each metric on detectability. The model was tuned for simplicity, interpretability, and generalizability using stepwise regression applied to the primary features and their interactions. We found that variables corresponding to each of five categories (local structural distractors, local intensity, global context, local vascularity, and contiguity with structural distractors) were significant (p  <  0.01) factors in a standardized model. Moreover, reader-specific models conveyed significant differences among readers with significant distraction (missed detections) influenced by local intensity- versus local-structural characteristics being mutually exclusive. Readers with significant local intensity distraction (n  =  10) detected substantially fewer lung nodules than those who were significantly distracted by local structure (n  =  2), 46.1% versus 65.3% mean nodules detected, respectively.

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2020/$28.00 © 2020 SPIE
Boning Li, Taylor B. Smith, Kingshuk Roy Choudhury, Brian Harrawood, Lukas Ebner, Justus E. Roos, and Geoffrey D. Rubin "Influence of background lung characteristics on nodule detection with computed tomography," Journal of Medical Imaging 7(2), 022409 (25 January 2020).
Received: 24 September 2019; Accepted: 26 December 2019; Published: 25 January 2020

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