Artificial Intelligence, Breast Cancer, Mammography
Conditions
Brief summary
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.
Interventions
DEVICEAI tool
Both the arms will undergo the use of "AI tool" developed in the group. The tool will be trained to detect outcomes.
Sponsors
Tampere University Hospital
Tampere University
Kuopio University Hospital
University of Eastern Finland
University of Turku
University of Oulu
Study design
Allocation
NON_RANDOMIZED
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Subject)
Eligibility
Sex/Gender
FEMALE
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Mammographic texture features | Through study completion, an average of 5 year | Aim: to evaluate how imaging parameters affect the mammographic texture features |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Biological features | through study completion, an average of 10 year | Aim: To evaluate whether there is an interplay between mammographic texture feature parameters and pathological and biological features (e.g., breast cancer biomarkers) |
Countries
Finland
Contacts
CONTACTOtso Arponen, MD, PhD
Outcome results
None listed