Improved pulmonary nodule classification utilizing lung parenchyma texture features

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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