Mammography is the most common method for breast imaging, and it provides information for model building and analysis. Radiomics applied to mammography has the potential to revolutionize clinical decision-making by providing valuable insights into risk assessment and disease detection. Despite this, the influence of imaging parameters and clinical and biological factors on radiological texture features remains poorly understood. There is a pressing need to overcome the obstacle of system-inherent effects on mammographic images to facilitate the translation of radiological texture features into routine clinical practice by enabling reliable and robust AI-based or AI-aided decision-making. Furthermore, understanding the relationship between imaging parameters, textural features, and clinical and biological information supports the clinical use of AI. The objective of this study is to evaluate AI methods for clinical practice and to study how it relates to clinical factors and biological features.

Inclusion Criteria:

• Candidate is a biological female aged 18 years or above;
• Candidate is willing and able to give informed consent and gives their written consent for the participation in the study;
• There is a clinical indication for a uni- or bilateral mastectomy

Exclusion Criteria:

• Candidate lacks the capacity to provide informed consent;
• Candidate has breast implants