UCLA

Low-dose computed tomography (CT) screening has been widely used to detect and diagnose early stage lung cancer. Clinical trials have shown that low-dose CT reduced lung cancer mortality by 20% relative to plain chest radiography; however, challenges exist in current low-dose CT screening programs including high over-diagnosis rates, high cost and increased radiation exposure. This dissertation attempts to overcome these challenges by developing machine and deep learning models for automated lung cancer diagnosis and disease progression estimation. A novel lung segmentation approach was first developed using a bidirectional chain code method and machine learning framework. This method is designed to include the lung nodules attached to lung wall while minimizing over-segmentation error. Second, a hybrid ensemble convolutional neural network has been developed to classify lung nodule vs. non-nodule objects. The ensemble model combines the VGG, residual and densely connected module designs to improve the model classification robustness for external datasets collected with different acquisition parameters. Third, a hierarchical semantic convolutional neural network (HSCNN) has been described to classify lung nodule malignancy. Semantic characteristic features, predicted in parallel with the malignancy for each nodule, enable the interpretation of the model and improvement of malignancy prediction. Finally, a Bayesian framework combined with a continuous-time Markov model was developed to estimate the multi-state disease progression of lung cancer. The resulting model estimates individual lung cancer state transition information, providing the basis for personalized screening recommendations. Extensive experiments and results have proved the effectiveness of these methods paving the way to optimize and improve the effectiveness of existing low-dose CT screening programs.