Artificial Intelligence to Predict Nodule Disappearance in Lung Cancer Screening

This thesis investigates the application of machine learning and deep learning techniques to predict the disappearance of lung nodules using real-world imaging data. Chapter 1 introduces the research background and provides an overview of the thesis structure. Chapter 2 presents a practical, step-by-step AI workflow for working with a lung nodule dataset, offering coding examples and guidance for researchers, clinicians, and technicians.
Chapters 3 through 5 develop predictive models for the disappearance of indeterminate pulmonary nodules (IPNs). Chapter 3 explores machine learning approaches using demographic and radiological features from the NELSON dataset. Among the models tested, a random forest achieved the best performance with an AUC of 0.865. Feature importance analysis identified volume, maximum diameter, and minimum diameter as the most influential variables. Chapter 4 focuses on deep learning models trained with imaging and non-imaging features from the ImaLife dataset. Results showed that image-only models performed comparably to those integrating demographic information, suggesting limited added value from non-imaging data. Explainability tools confirmed that imaging features were the primary drivers of model performance. Chapter 5 introduces a multi-view deep learning model that incorporates multiple spatial perspectives of new IPNs. This model outperformed all single-view approaches on the NELSON dataset, achieving an AUC of 0.81. Explainable heatmaps further highlighted the most predictive image regions.
Finally, Chapter 6 summarizes the findings, discusses their clinical relevance, and outlines future research directions for advancing predictive modeling in pulmonary imaging.