Book Title: AI Horizons – Exploring Multidisciplinary Frontiers (Volume 4)
Editors: Dr. Shweta A. Bansal and Dr. Charu Chhabra (PT)
ISBN: 978-81-984464-6-6
Chapter: 3
DOI: https://doi.org/10.59646/aihc3/305
Authors: Syed Adeeba Ali, Sahar Zaidi, Huma Parveen
Abstract
Breast cancer is the second leading cause of death among women in the United States, with over 40,000 estimated fatalities in 2016. Early detection plays a crucial role in improving survival rates, as the overall prognosis is closely linked to the stage of the disease at diagnosis. Mammography screening has been shown to reduce breast cancer mortality by 16–40% in women aged 40–74. However, mammography alone may not detect all cancers, particularly in women with dense breast tissue. This limitation underscores the need for more effective screening methods for high-risk women.
To address these limitations, newer imaging techniques such as dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI), digital breast tomosynthesis (DBT), and automated whole breast ultrasound (AWBUS) have been developed. These advanced imaging modalities provide radiologists with a broader range of diagnostic tools and datasets. Artificial intelligence (AI) is poised to enhance this evolving landscape by streamlining diagnostic processes. AI can assist in recognizing and analyzing complex patterns in imaging data, translating tumoral characteristics to genetic profiles, and predicting outcomes to inform therapeutic and prognostic decisions.
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