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Abstract

Breast cancer is one of the most prevalent forms of cancer among women worldwide, with significant implications for both individual health and public health systems. Early detection plays a critical role in improving survival rates and treatment outcomes. Consequently, the development of accurate and efficient diagnostic methods is paramount. This abstract explores various techniques and technologies used in breast cancer diagnosis, including mammography, ultrasound, magnetic resonance imaging (MRI), biopsy, and emerging technologies such as molecular imaging and artificial intelligence (AI). Each method has its advantages and limitations, and their selection often depends on factors such as patient age, breast density, risk factors, and clinical presentation. Moreover, the integration of multiple modalities and the advancement of AI-driven decision support systems hold promise in enhancing diagnostic accuracy and personalized treatment strategies. This abstract aims to provide an overview of the current landscape of breast cancer diagnosis, highlighting recent advancements, challenges, and future directions in the field. Breast cancer remains one of the most prevalent forms of cancer worldwide, posing significant health challenges and socioeconomic burdens. Early detection plays a pivotal role in improving survival rates and treatment outcomes. Over the years, advancements in diagnostic techniques have revolutionized the landscape of breast cancer diagnosis, offering more precise, efficient, and less invasive methods. This abstract provides an overview of the current state-of-the-art diagnostic modalities for breast cancer, highlighting their principles, advantages, limitations, and future prospects. From traditional mammography to cutting-edge molecular imaging and biomarker analysis, each approach contributes uniquely to the early detection and characterization of breast cancer, facilitating personalized treatment strategies and improved patient care.