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.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
SINGLE
Enrollment
200
Both the arms will undergo the use of "AI tool" developed in the group. The tool will be trained to detect outcomes.
Mammographic texture features
Aim: to evaluate how imaging parameters affect the mammographic texture features
Time frame: Through study completion, an average of 5 year
Biological features
Aim: To evaluate whether there is an interplay between mammographic texture feature parameters and pathological and biological features (e.g., breast cancer biomarkers)
Time frame: through study completion, an average of 10 year
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