Mammographic Classification of Breast Lesions in Women: Advances and Challenges in Radiological Diagnostics

Abstract

Mammographic classification of breast lesions is a critical component of breast cancer screening and diagnosis, directly influencing clinical management and patient outcomes. Utilizing mammography, radiologists can identify and classify breast lesions into categories ranging from benign to highly suspicious for malignancy. The classification relies on the Breast Imaging Reporting and Data System (BI-RADS) which standardizes reporting and facilitates decision-making. Despite technological advancements, such as digital mammography and artificial intelligence (AI) integration, challenges persist, including variability in radiologist interpretation and limited sensitivity in dense breast tissue. The role of digital mammography and the impact of AI in enhancing diagnostic accuracy and reducing false positives. The incorporation of AI algorithms has shown promise in improving lesion detection, characterization, and consistency in classification, potentially addressing inter-observer variability. However, the adoption of AI in clinical practice requires robust validation and addressing ethical concerns regarding patient data privacy and algorithm transparency. The limitations of mammography, particularly in women with dense breast tissue, where the sensitivity of mammograms decreases, potentially leading to missed diagnoses. Alternative imaging modalities, such as ultrasound and magnetic resonance imaging (MRI), are examined as complementary tools in such cases. The integration of multi-modality imaging approaches can enhance diagnostic confidence and accuracy. Future directions in mammographic classification are highlighted, including the development of more sophisticated AI models, personalized screening protocols, and improved education and training for radiologists. Emphasizing a multidisciplinary approach and continued research in this field is essential for optimizing breast cancer screening programs and improving patient outcomes.

Keywords: Mammography, Breast Lesions, BI-RADS, Radiology, Digital Mammography, Artificial Intelligence, Dense Breast Tissue, Diagnostic Accuracy, Breast Cancer Screening, Multi-modality Imaging.