Deep Learning-Assisted Histopathological Analysis Of Adipose Tissue Remodeling In Breast Cancer
Breast cancer is one of the most commonly diagnosed cancers in women and a leading cause of cancer-related deaths worldwide. While tumor cells drive disease progression, the surrounding stromal microenvironment—particularly changes in adipose tissue—plays a crucial role in tumor growth and metastasis. Emerging evidence suggests cancer-associated adipose tissue undergoes structural remodeling, potentially serving as an early indicator of tumor presence. However, current assessment methods are subjective and lack scalable computational tools. To address this gap, we developed a deep-learning-based computational framework to analyze histopathological images of mammary adipose tissue and identify tumor-induced changes. Our approach applies automated image segmentation and machine learning models to quantify key morphological features—such as adipocyte size, shape irregularity, and spatial distribution—distinguishing normal adipocytes from cancer-associated adipocytes. We trained this model on tissue samples from breast cancer patients, comparing tumor microenvironment and distal (healthy) regions to capture distinct adipose remodeling patterns. By applying artificial intelligence to breast cancer pathology, our framework enables objective, scalable detection of cancer-associated stromal remodeling. We have identified statistically significant differences in adipocyte morphology, aligning with previous manual analyses. This tool has potential clinical applications for improving risk assessment, prognosis, and treatment planning in breast cancer management.