ArticleJournal of medical imaging (Bellingham, Wash.)2020
Influence of background lung characteristics on nodule detection with computed tomography.
Article in Journal of medical imaging (Bellingham, Wash.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors at 4 institutions in 2 countries.
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Abstract
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 (
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