The use of surrounding lung parenchyma for the automated classification of pulmonary nodules

Samantha Kirsten Nowik Dilger

doi:10.17077/etd.h0sm16e6

Lung cancer is the leading cause of cancer-related death for both men and women in the United States, despite being the second-most frequent cancer diagnosis for both sexes. This high mortality rate is due to the majority of cases being diagnosed after the primary lung cancer has metastasized. In an effort to reduce mortality associated with lung cancer by diagnosing lung cancer at an earlier stage, screening of high-risk populations has been employed. One screening tool, computed tomography (CT), has been shown to reduce mortality by 20%, compared to screening for lung cancer by chest x-ray. This was achieved by earlier stage diagnosis of lung cancer in participants screened with CT. The use of chest CT in lung cancer screening has also led to increased numbers of false-positives - benign lung nodules that are marked as suspicious for lung cancer. These false-positives result in unnecessary invasive follow-up procedures and costs while incurring additional emotional stress on the patient. In an effort to reduce the number of false-positives, a computer-aided diagnostic (CAD) tool can be designed to determine the probability of malignancy of a lung nodule based on objective measurements. While current CAD models characterize the pulmonary nodule's shape, density, and border, analyzing the parenchyma surrounding the nodule is an area that has been minimally explored. By quantifying characteristics, or features, of the surrounding tissue, this project explores the hypothesis that textural differences in both the nodule and surrounding parenchyma exist between malignant and benign cases. By incorporating these features, performance in the measures of sensitivity, specificity and accuracy can be improved over CAD tools that rely on nodule characteristics alone. A CAD program was developed for the computation of features from a pulmonary nodule. A region of interest containing a nodule and surrounding parenchyma was extracted from a CT scan. Several novel feature extraction techniques were developed, including a three-dimensional application of Laws' Texture Energy Measures to quantify the textures of the parenchyma surrounding the nodule and the nodule itself. In addition, the densities of the nodule and surrounding parenchyma were summarized through metrics such as mean, variance, and entropy of the intensities within each region. Finally, the margins of the nodule were characterized by analyzing mean and variance of border irregularity. A total of 299 features were extracted. To illustrate proof of concept, the CAD program was applied to 27 regions of interest - 10 benign and 17 malignant. Through feature selection, 36 significant features were recognized (p-values < 0.05), including many textural and parenchymal features. These features were further reduced by forward feature selection to two features that summarized the dataset. A neural network was used to classify the cases in a leave-one-out method. Preliminary results yielded 92.6% accuracy in classification of test cases, with two benign nodules incorrectly classified as malignant. The significance of texture and parenchymal features supports the hypothesis that features extracted from the parenchyma have the potential to improve classification of nodules, aiding in the reduction of false-positives identified through CT screening. As more cases are incorporated into the database, these textural features will play a larger role.

The use of surrounding lung parenchyma for the automated classification of pulmonary nodules

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