Improved pulmonary nodule classification utilizing quantitative lung parenchyma features

  • Dilger S
  • Uthoff J
  • Judisch A
  • et al.
N/ACitations
Citations of this article
65Readers
Mendeley users who have this article in their library.

Abstract

Current computer-aided diagnosis (CAD) models for determining pulmonary nodule malignancy characterize nodule shape, density, and border in computed tomography (CT) data. Analyzing the lung parenchyma surrounding the nodule has been minimally explored. We hypothesize that improved nodule classification is achievable by including features quantified from the surrounding lung tissue. To explore this hypothesis, we have developed expanded quantitative CT feature extraction techniques, including volumetric Laws texture energy measures for the parenchyma and nodule, border descriptors using ray-casting and rubber-band straightening, histogram features characterizing densities, and global lung measurements. Using stepwise forward selection and leave-one-case-out cross-validation, a neural network was used for classification. When applied to 50 nodules (22 malignant and 28 benign) from high-resolution CT scans, 52 features (8 nodule, 39 parenchymal, and 5 global) were statistically significant. Nodule-only features yielded an area under the ROC curve of 0.918 (including nodule size) and 0.872 (excluding nodule size). Performance was improved through inclusion of parenchymal (0.938) and global features (0.932). These results show a trend toward increased performance when the parenchyma is included, coupled with the large number of significant parenchymal features that support our hypothesis: the pulmonary parenchyma is influenced differentially by malignant versus benign nodules, assisting CAD-based nodule characterizations.

Cite

CITATION STYLE

APA

Dilger, S. K. N., Uthoff, J., Judisch, A., Hammond, E., Mott, S. L., Smith, B. J., … Sieren, J. C. (2015). Improved pulmonary nodule classification utilizing quantitative lung parenchyma features. Journal of Medical Imaging, 2(4), 041004. https://doi.org/10.1117/1.jmi.2.4.041004

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free