The purpose of this paper was to investigate the effects of integrating nodule 3D morphological features, texture features and functional dynamic contrast-enhanced features in differentiating between benign and malignant solitary pulmonary nodules (SPNs). In this study, 42 cases with solitary lung nodules were examined in this study. The dynamic CT helical scans were acquired image at five time intervals: prior to contrast injection (baseline) and then at 45, 90, 180, 300 seconds after administrating the contrast agent. The nodule boundaries were contoured by radiologists on all series. Using these boundaries, several types of nodule features were computed, including: 3D morphology and Shape Index of the nodule contrast intensity surface; Dynamic contrast related features; 3D texture features. AdaBoost was performed to select the best features. Logistic Regression Analysis (LRA) and AdaBoost were used to analyze the diagnostic accuracy of features in each feature category. The performance when integrating all feature types was also evaluated. For 42 patients, when using only six SI and 3D structural features, the accuracy of AdaBoost was 81.4%, with accuracies of AdaBoost using functional contrast related features (include 8 features) and texture features(include 18 features) were 65.1% and 69.1% respectively. After combining all types' features together, the overall accuracy was improved to over 88%. In conclusion: Combining 3D structural, textural and functional contrast features can provide a more comprehensive examination of the SPNs by coupling dynamic CT scan techniques with image processing to quantify multiple properties that relate to tumor geometry and tumor angiogenesis. This integration may assist radiologists in characterizing SPNs more accurately.
Studies have shown that vascular structure of a solitary pulmonary nodule (SPN) can give insight into the diagnosis of the nodule. The purpose of this study is to investigate the utility of texture analysis as a quantitative measure of the vascular structure of a nodule. A contrast CT study was conducted for 29 patients with an indeterminate SPN. For each patient, the post-contrast series at maximum enhancement was volumetrically registered to the pre-contrast series. The two registered series were subtracted to form difference images of the nodule and each voxel was color-coded into 7 bins. Initially, a representative image of each nodule was subjectively rated on a five-point by a radiologist as to the magnitude, extent, and heterogeneity of the enhancement. From the initial analysis the heterogeneity of the nodule was found to be significantly different for benign versus malignant nodules (p<0.01), while the other two ratings were found not to be significant. We then attempted to quantify this subjective rating of heterogeneity by calculating 14 textural features based on co-occurrence matrices. These features included various measures of contrast, entropy, energy, etc. Dimension reduction techniques such as principal component and factor analysis were applied to the features to reduce the 14 variables to one factor. The mean of this factor was significantly different for malignant versus benign nodules (p=0.010). Texture analysis of contrast enhancement maps appears to be useful tool to characterize SPNs.
The accurate characterization of pulmonary airways on CT is potentially very useful for diagnosis and evaluation of lung diseases. The task is challenging due to their small size and variable orientation. We propose a probabilistic modeling technique and a set of measurement tools to quantitate airway morphology. We extract the airway tree structure from high resolution CT scans with a seeded region growing algorithm. Individual airway branches are identified by reducing the airway tree to a set of central axes. Properties such as lumen diameter and branch angle are measured from these central axes. The structure of the Bayesian model is inferred from a set of equations representing the parent-daughter relationships between branches, such as equations of air flow ratio and flow conservation. The CT measurements are used to instantiate the conditional probability tables of the Bayesian model. To evaluate the model, it was used to predict the airway diameter for the 2nd, 3rd, 4th, 5th, and 6th generations of the airway tree. We show that the model can reasonably predict the diameter of a particular airway branch, given information of its parent.