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1 September 2015 Improved pulmonary nodule classification utilizing quantitative lung parenchyma features
Samantha K. Dilger, Johanna Uthoff, Alexandra Judisch, Emily Hammond, Sarah L. Mott, Brian J. Smith, John D. Newell Jr., Eric A. Hoffman, Jessica C. Sieren
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Journal of Medical Imaging, Vol. 2, Issue 4, 041004 (September 2015). https://doi.org/10.1117/1.JMI.2.4.041004
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Samantha K. Dilger, Johanna Uthoff, Alexandra Judisch, Emily Hammond, Sarah L. Mott, Brian J. Smith, John D. Newell Jr., Eric A. Hoffman, and Jessica C. Sieren "Improved pulmonary nodule classification utilizing quantitative lung parenchyma features," Journal of Medical Imaging 2(4), 041004 (1 September 2015). https://doi.org/10.1117/1.JMI.2.4.041004
Published: 1 September 2015
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Cited by 51 scholarly publications and 4 patents.
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KEYWORDS
Lung
Computer aided diagnosis and therapy
Feature extraction
Lung cancer
Computed tomography
Computer aided design
Diagnostics
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