Quantitative computed tomography classification of lung nodules: initial comparison of 2- and 3-dimensional analysis

David S. Gierada, David G. Politte, Jie Zheng, Kenneth B. Schechtman, Bruce R. Whiting, Kirk E. Smith, Traves Crabtree, Daniel Kreisel, Alexander S. Krupnick, G. Alexander Patterson, Varun Puri, Bryan F. Meyers

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Objective The aim of this study was to compare the performance of 2- (2D) and 3-dimensional (3D) quantitative computed tomography (CT) methods for classifying lung nodules as lung cancer, metastases, or benign. Methods Using semiautomated software and computerized analysis, we analyzed more than 50 quantitative CT features of 96 solid nodules in 94 patients, in 2D from a single slice and in 3D from the entire nodule volume. Multivariable logistic regression was used to classify nodule types. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) using leave-one-out cross-validation. Results The AUC for distinguishing 53 primary lung cancers from 18 benign nodules and 25 metastases ranged from 0.79 to 0.83 and was not significantly different for 2D and 3D analyses (P = 0.29-0.78). Models distinguishing metastases from benign nodules were statistically significant only by 3D analysis (AUC = 0.84). Conclusions Three-dimensional CT methods did not improve discrimination of lung cancer, but may help distinguish benign nodules from metastases.

Original languageEnglish
Pages (from-to)589-595
Number of pages7
JournalJournal of computer assisted tomography
Volume40
Issue number4
DOIs
StatePublished - Jul 1 2016

Keywords

  • 3D
  • CT
  • computer-aided diagnosis
  • lung nodule
  • quantitative

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