Study on Female Patients' Mammographic Texture Features

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.

NCT Number	NCT06469606
Lead Sponsor	Tampere University Hospital Collaborators: Tampere University, Kuopio University Hospital, University of Eastern Finland, University of Turku, University of Oulu
Conditions	Breast Cancer, Artificial Intelligence, Mammography
Enrollment	200 participants
Start Date	2024-06-17
Primary Completion	2035-12-15 (estimated)
Study Completion	2038-12-15 (estimated)
Updated on ClinicalTrials.gov	2026-08-05