Pushing the boundaries: feature extraction from the lung improves pulmonary nodule classification

Lung cancer is the leading cause of cancer death in the United States. While low-dose computed tomography (CT) screening reduces lung cancer mortality by 20%, 97% of suspicious lesions are found to be benign upon further investigation. Computer-aided diagnosis (CAD) tools can improve the accuracy of CT screening, however, current CAD tools which focus on imaging characteristics of the nodule alone are challenged by the limited data captured in small, early identified nodules. We hypothesize a CAD tool that incorporates quantitative CT features from the surrounding lung parenchyma will improve the ability of a CAD tool to determine the malignancy of a pulmonary nodule over a CAD tool that relies solely on nodule features. Using a higher resolution research cohort and a retrospective clinical cohort, two CAD tools were developed with different intentions. The research-driven CAD tool incorporated nodule, surrounding parenchyma, and global lung measurements. Performance was improved with the inclusion of parenchyma and global features to 95.6%, compared to 90.2% when only nodule features were used. The clinically-oriented CAD tool incorporated nodule and parenchyma features and clinical risk factors and identified several features robust to CT variability, resulting in an accuracy of 71%. This study supports our hypothesis that the inclusion of parenchymal features in the developed CAD tools resulted in improved performance compared to the CAD tool constructed solely with nodule features. Additionally, we identified the optimal amount of lung parenchyma for feature extraction and explored the potential of the CAD tools in a clinical setting.