Risk-adjusted Breast Cancer Screening Strategies
The incidence of breast cancer is increasing worldwide. Population-based screening is available in many countries but is often not the most efficient use of resources. Therefore, interest in risk-adjusted screening programs has increased in recent years. 1 Models to predict the risk of a woman to develop breast cancer in her lifetime are taking the of individual breast density as well as inherited genetic variants into consideration. 2 In the future, the goal is to be able to make personalized screening authoritative recommendations on when a woman should start and stop screening and how often it should be performed. 1
What do our clinical experts have to say?
In the following videos, clinical experts present the role of breast imaging and artificial intelligence for risk-adjusted breast screening. A special focus is put on breast density and how to measure it objectively.
Risk-adjusted Breast Cancer Screening Strategies (ECR Symposium)
Personalized Breast Cancer Screening and the Role of Breast Density
Dense Breast and How to Overcome the Radiologist’s “Problem Child” Video
Risk-based screening and the potential of AI for patient stratification
Ritse Mann; Nijmegen, The Netherlands
Once a women’s risk of developing breast cancer is known, defining an optimal screening strategy becomes important. Such a strategy includes questions on the techniques to use for screening, but should also take into account from what age to screen women, and at what age to stop; when to use highly sensitive screening technologies and when to rely on highly specific ones and so on. This should be placed in perspective in relation to the overall health status of the screened women, and the relative risk of dying from other causes. Moreover, it should take into account relevant costs to both the patient and society.
Recent research in various populations has shown that breast MRI outperforms mammography screening, detecting cancer earlier, and reducing the interval cancer rate. Still selecting patients for such a technique remains a challenge, and it is simply not possible for us to offer the technique to all women at risk. Imaging characteristics may in fact contain a lot of useful information to gauge a woman’s short-term risk of developing breast cancer. Subtle findings, not enough for recall, may be used as an argument for more intensive screening or the application of supplemental screening techniques. It is therefore conceivable that patient selection for supplemental or alternative screening techniques may be performed using characterization of findings present with relatively inexpensive, moderately sensitive, but highly specific screening tools such as mammography. Al applications aimed at automated image analysis may aid in such image-based personification of the screening regimen.
- To be aware of the questions that arise in clinical practice when implementing personalized screening
- To understand the relative advantages of common screening techniques
- To appreciate the potential of AI-assisted imaging-based stratification of women to screening cohorts