PURPOSE: In @entity2 screening, pulmonary nodules are first identified in low-dose chest CT images. Costly follow-up procedures could be avoided if it were possible to establish the @entity5 status of these nodules from these initial images. Preliminary computer methods have been proposed to characterize the @entity5 status of pulmonary nodules based on features extracted from a CT image. The parameters and performance of such a computer system in a @entity2 screening context are addressed. METHODS: A computer system that incorporates novel 3D image features to determine the @entity5 status of pulmonary nodules is evaluated with a large dataset constructed from images from the NLST and @entity2 studies. The system is evaluated with different data subsets to determine the impact of class size distribution imbalance in datasets and to evaluate different training and testing strategies. RESULTS: Results show a modest improvement in @entity5 prediction compared to prediction by size alone for a traditional size-unbalanced dataset. Further, the advantage of size binning for classifier design and the advantages of a size-balanced dataset for both training and testing are demonstrated. CONCLUSION: Nodule classification in the context of low-resolution low-dose whole-chest CT images for the clinically relevant size range in the context of @entity2 screening is highly challenging, and results are moderate compared to what has been reported in the literature for other clinical contexts. Nodule class size distribution imbalance needs to be considered in the training and evaluation of computer-aided diagnostic systems for producing @entity1 -relevant outcomes.

Automated pulmonary nodule CT image characterization in XXXX screening.

@entity5 :: ('MESH:D009369', 'Disease') :: ['malignancy'] @entity1 :: ('9606', 'Species') :: ['patient'] @entity2 :: ('MESH:D008175', 'Disease') :: ['lung cancer', 'ELCAP lung cancer']
@entity2 :: (MESH:D008175,Disease) :: ['lung cancer']
