title: Automated pulmonary nodule CT image characterization in lung cancer screening.
abstract: OBJECTIVE
  In lung cancer 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 malignancy status of these nodules from these initial images. Preliminary computer methods have been proposed to characterize the malignancy status of pulmonary nodules based on features extracted from a CT image. The parameters and performance of such a computer system in a lung cancer screening context are addressed.
  
  
  METHODS
  A computer system that incorporates novel 3D image features to determine the malignancy status of pulmonary nodules is evaluated with a large dataset constructed from images from the NLST and ELCAP lung cancer 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 malignancy 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.
  
  
  CONCLUSIONS
  Nodule classification in the context of low-resolution low-dose whole-chest CT images for the clinically relevant size range in the context of lung cancer 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 patient-relevant outcomes.
authors.affiliation: School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA. reeves@cornell.edu.
authors.forename: Anthony P
authors.initials: AP
authors.lastname: Reeves
authors.affiliation: School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA. yx269@cornell.edu.
authors.forename: Yiting
authors.initials: Y
authors.lastname: Xie
authors.affiliation: Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. artit.jirapatnakul@mountsinai.org.
authors.forename: Artit
authors.initials: A
authors.lastname: Jirapatnakul
mesh_terms: D055088:Early Detection of Cancer / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods
keywords: Automated computer method; Low-dose CT; Lung cancer screening; Pulmonary nodule characterization