Mammography, a cornerstone of cancer detection, employs low-dose X-rays to examine breast tissue for abnormalities. Its efficacy in detecting early-stage breast cancer is well-established, significantly enhancing survival rates. However, challenges persist, notably false positives leading to unnecessary anxiety and interventions, and false negatives potentially delaying treatment. Advanced technologies like digital mammography and 3D tomosynthesis offer improved accuracy, particularly in dense breast tissue. Additionally, emerging AI algorithms aid in interpreting mammograms, augmenting radiologists' expertise. Despite these advancements, accessibility and adherence to screening remain barriers, especially in underserved communities. Continued research focuses on refining screening protocols, integrating AI further, and exploring adjunctive techniques like molecular imaging for more precise diagnoses, striving towards more effective breast cancer detection and management.



Title : Multiplexed biosensor detection of cancer biomarkers
Michael Thompson, University of Toronto, Canada
Title : Nanomedicine in over 45,000 patients and no cancer
Thomas Jay Webster, Brown University, United States