Uncovering the Role of Fat-Infiltrated Axillary Lymph Nodes in Obesity-Related Diseases with Statistical and Machine Learning Analyses

The link between obesity and pathogenesis is a complex and multifaceted area of research that is yet to be fully understood. Ample evidence exists to demonstrate the direct relationship between excessive internal fat and various health conditions such as cancer, and metabolic and cardiovascular diseases. The infiltration of ectopic fat into axillary lymph nodes, observable on breast cancer screening images, has been shown to be correlated with body mass index (BMI) in women undergoing screening. This study aimed to explore the relationship between fat-infiltrated axillary lymph nodes (FIN) and obesity-related diseases, with the goal of evaluating the clinical value of FIN as a potential prognostic image biomarker. In this thesis, we conducted a comprehensive examination of the potential association between FIN and breast cancer nodal metastasis in breast cancer patients with obesity. We also investigated the relationship between FIN and multiple cardiometabolic diseases in women who have received screening mammography. Additionally, we developed a deep-learning pipeline to automate the detection of FIN from screening mammography, with the aim of facilitating large-scale studies in the future. Lastly, by leveraging the deep-learning model, we identified potential morphological correlates of lymph node adiposity on the histology images, which helped to generate hypotheses for the underlying mechanisms that drive the FIN-obesity pathogenesis. Overall, this thesis underscores the significance of FIN in the development of obesity-related diseases and establishes a foundation for future research to gain a deeper understanding of the complex relationship between FIN and obesity-related diseases