Breast cancer recurrence after treatment is a significant clinical challenge, often driven by a small subset of cancer cells that adapt to survive therapy. Our research focuses on Poly-Aneuploid Cancer Cells (PACCs), which are larger, genomically unstable cells that may persist and thrive post-treatment. PACCs have historically been considered near collapse, but recent studies suggest they can remain dormant, eventually generating resistant progeny. We hypothesize that PACCs contribute significantly to tumor recurrence by reseeding tumors with adapted cells. Our research aims to investigate the correlation between PACCs and treatment outcomes in Swedish breast cancer patients using the SweBCG 91 RT tumor microarray (TMA). Preliminary data using manual analysis, show that the presence of PACCs in primary tumor is correlated to a worst overall survival and a higher chance of recurrence. We want to further investigate the impact of PACCs on local and distant recurrence, as well as the impact of radiotherapy in inducing the PACCs. We will evaluate if the presence of PACCs, identified by their large, misshapen nuclei, correlates with poor outcomes. Since it is know that PACCs can generate progeny, we also want to investigate the presence of “different” morphology on neighboring cells and/or a field effect from the PACCs. Since these details could be difficult to visualize by human eye, and considering the amount of data, we want to develop a pipeline using machine learning, to identify the nuclear area of cancer cells in TMA images. That will provide us many parameters, including spatial analysis.