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.

Current computer-aided diagnosis (CAD) models, developed to determine the malignancy of pulmonary nodules, characterize the nodule's shape, density, and border. Analyzing the lung parenchyma surrounding the nodule is an area that has been minimally explored. We hypothesize that improved classification of nodules can be achieved through the inclusion of features quantified from the surrounding lung tissue. From computed tomography (CT) data, feature extraction techniques were developed to quantify the parenchymal and nodule textures, including a three-dimensional application of Laws' Texture Energy Measures. Border irregularity was investigated using ray-casting and rubber-band straightening techniques, while histogram features characterized the densities of the nodule and parenchyma. The feature set was reduced by stepwise feature selection to a few independent features that best summarized the dataset. Using leave-one-out cross-validation, a neural network was used for classification. The CAD tool was applied to 50 nodules (22 malignant, 28 benign) from high-resolution CT scans. 47 features, including 39 parenchymal features, were statistically significant, with both nodule and parenchyma features selected for classification, yielding an area under the ROC curve (AUC) of 0.935. This was compared to classification solely based on the nodule yielding an AUC of 0.917. These preliminary results show an increase in performance when the surrounding parenchyma is included in analysis. While modest, the improvement and large number of significant parenchyma features supports our hypothesis that the parenchyma contains meaningful data that can assist in CAD development.

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