Breast cancer is one of the leading causes of morbidity and mortality in women worldwide, and early diagnosis is the hallmark of a more favorable prognosis. In the present review, an extensive discussion has been put on we have thoroughly discussed the front-line breast cancer screening modalities with the key role of mammography and ultrasonography. Mammography is embedded as the first-line screening test with established mortality-reduction benefits (number needed to screen: 190 women). Despite its compromised sensitivity in dense breasts, ultrasound becomes a significant adjunct in such a scenario. The review discusses innovations such as the use of artificial intelligence (AI) and deep learning, which enhance image interpretation accuracy and provide a focused genetic risk assessment. In addition, it also critically evaluates the socioeconomic and geographic determinants of access to fair screening and reaffirms the role of overarching healthcare policies. The aspects of patient-centered concern, including decision and compliance, are considered and prepared for optimum use of the screening. Economic analyses indicate that cost-effectiveness is optimized by risk-adapted screening models Cost-effectiveness analyses have shown that a standard mammogram may cost as much as 366,000 USD per extra life-year gained, well above the willingness-to-pay thresholds. Risk-adapted screening models incorporating genetic susceptibility and breast density factors optimize cost-effectiveness. New biomarkers like circulating tumor DNA (ctDNA) are quoted to have the promise to complement imaging for early detection and monitoring. Finally, evidence to inform selective screening intervals and starting ages by risk profile is synthesized, balancing benefit against harm. The resulting synthesis of evidence helps provide a multidisciplinary approach in making breast cancer screening accessible to and mortality-reducing for global populations. The graphical abstract illustrates the holistic strategy for personalized risk screening for breast cancer. Recent advancements, like the use of artificial intelligence and machine learning, also add to the accuracy of detection, risk analysis of genes, and the search for novel biomarkers. The graphical abstract presents a comparative overview of mammography and ultrasound for the diagnosis of breast cancer.
Chen et al. (Sun,) studied this question.