Early Detection of Breast Cancer: Advances in Clinical Screening and Diagnostic Strategies

Abstract

Breast cancer remains a major global health challenge, with approximately 2.3 million new cases and 670,000 deaths reported in 2022, and projections indicating a rise to 3.2 million cases and 1.1 million deaths annually by 2050. Disparities in mortality are stark, driven by late-stage diagnoses and limited access in low- and middle-income countries, where survival rates are significantly lower than in high-income settings. This review examines recent advances in early detection and diagnostic strategies, including the transition from 2D digital mammography to Digital Breast Tomosynthesis (DBT) and synthetic 2D mammography, which improve sensitivity (up to 86–90%) and reduce recall rates, particularly in dense breasts. Supplemental modalities such as Automated Breast Ultrasound (ABUS) and Contrast-Enhanced Mammography (CEM) enhance detection in dense tissue, with CEM offering comparable sensitivity to MRI (98.9%) but superior specificity. The shift toward personalized, risk-based screening, supported by trials like WISDOM, incorporates genetic testing, polygenic risk scores (PRS), and breast density to optimize screening protocols and reduce unnecessary interventions. Artificial intelligence (AI) applications, including deep learning for lesion detection and independent reading, demonstrate substantial improvements in cancer detection rates (up to 29% increase) and workflow efficiency. Emerging liquid biopsy techniques, utilizing circulating tumor DNA (ctDNA) and exosomes, enable non-invasive molecular detection with high AUC values (e.g., 0.96 for early-stage disease). In low-resource settings, portable AI-supported tools, mobile units, and task-shifting address access gaps. These integrated, multi-modal approaches, aligned with the Global Breast Cancer Initiative (GBCI) goal of 2.5% annual mortality reduction, hold promise for closing survival disparities and averting millions of deaths by 2040.