Deciphering the morphological features underlying breast cancer behaviour using computational pathology and Artificial intelligence (with focus on the role of the tumour microenvironment) - Ghannam, Suzan (2025)
Background: Breast cancer (BC) is a heterogeneous disease with variable presentations, morphologies, and behaviours. The tumour microenvironment (TME) plays a key role in tumour progression. Molecular and phenotypic alterations in the neoplastic cells and their behaviour usually attracts more attention. However, there is evidence that the stroma of BC plays an important role in tumour behaviour. The basement membrane (BM), which comprises a physical barrier that separates the proliferating epithelial cells from the surrounding stroma is used to distinguish malignant in situ and invasive lesions. However, exceptions exist. The stroma of BC also varies widely with a variable degree of fibrosis, collagenisation, and elastosis in addition to other microenvironment components such as immune cells. The stromal cells are relatively genetically stable compared to the tumour cells, therefore they may be targets for therapeutic agents for cancer. The revolution of using digitalised whole slide images (WSI) in pathology developed a way of investigating BC cases. In addition, using artificial intelligence (AI) enables the integration of knowledge beyond human limits. However, this is a recently introduced technology, so further assessment of its uses and applications in the clinical setting is of paramount importance. Therefore, this study integrates digital pathology (DP) and AI to decipher the morphological features of TME in BC and identify novel biomarkers for improved BC prognosis.