Artificial intelligence and tomosynthesis for breast cancer detection
Breast cancer is the most common cancer in women, with almost 2 million new cases diagnosed every year around the globe. However, despite important improvements in awareness, detection, diagnosis, and treatment, breast cancer is still a major cause of mortality, accounting for approximately 500,000 annual deaths worldwide. Breast cancer mortality has decreased in the past decades, primarily because of the introduction of population-based screening programs with mammography and improvements in therapy. By imaging asymptomatic women periodically, breast cancer can be detected early, improving prognosis. However, these screening programs are far from perfect. The use of mammography -a 2D technique- to image the 3D volume of the breast leads to cancers being missed and for many false positive assessments. Another issue is the current labor-intensive screening workflow, in which radiologists must assess millions of exams yearly, of which actually only less than 1% result in a cancer diagnosis. This screening process is heavily threatened by the increasing scarcity of radiologists.
Artificial intelligence (AI) has blossomed in recent years, with new algorithms able to boost computers to accuracy levels similar to specialized humans for many medical imaging tasks. Digital breast tomosynthesis (DBT) is a new technique based on mammography principles, which can provide a pseudo-3D image of the breast, therefore improving the visualization of breast cancer. These two technologies, AI and DBT, when combined in an optimal way, might hold the promise of both improving screening outcomes and increasing the efficiency of screening. The main objective of this thesis was to investigate, assess, and optimize AI and DBT. A summary of the main findings of this thesis is described in this section.