Advances in Breast Imaging and Analytics

Dear Colleagues,

The last decade has recorded significant improvements in breast cancer survival rates and reductions in deaths from the disease. These achievements have been possible due to improvements in risk prediction; early detection; and treatment. Therefore; optimising risk prediction and early detection are crucial to further reducing breast cancer deaths. Breast imaging tools such as mammography; ultrasound; digital breast tomosynthesis; computed tomography; magnetic resonance imaging; and molecular breast imaging are constantly evolving to optimise breast cancer detection and assessment. Despite these technological advances; about 30% of breast cancer cases are missed; suggesting that strategies to improve the interpretation of breast images are needed. Another issue is that breast cancer post-treatment events such as recurrence and secondary cancer are major causal factors for breast-cancer-related deaths. Therefore; the accurate prediction of treatment outcomes is crucial to develop informed options for tailoring follow-up strategies and reducing breast cancer deaths. Current outcome prediction tools; which are based on clinicopathologic data; show moderate predictive powers at best. Recent evidence indicates that medical images contain covert information that can be modelled to improve the risk prediction; detection; and prognosis of breast cancer. Interestingly; novel technologies such as artificial intelligence and machine learning provide opportunities to extract and model image-based information and genomic and clinicopathologic data as well as data from medical health records to transform the prediction; detection; and prognosis of breast cancer.

The purpose of this Special Issue is to investigate how breast imaging hardware technologies and image interpreters (radiologists) can be further optimised to facilitate early detection; and how intelligent software technologies can be used to extract information from breast images and combine with genomic and clinicopathologic data as well as medical health records to improve the prediction; detection; and prognosis of breast cancer.

Dr. Ernest Usang Ekpo
Guest Editor

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