Spatiotemporal Mammography-based Deep Learning Model for Improved Breast Cancer Risk Prediction.

Breast cancer is one of the most prevalent cancers among women. It is the second leading cause of death in cancer-related deaths. Early detection and personalized risk assessment can reduce the mortality rate and improve survival rates.

Changes in breast cancer staging trends among Egyptian women after COVID-19: A retrospective single-center study.

Objectives: Since being declared a global pandemic, the SARS-CoV-2 virus had a significant impact on the entire globe. The pandemic has placed a heavy burden on healthcare systems worldwide, and cancer patients are particularly prone. Despite the fact that initial international reports suggest delays in breast cancer (BC) diagnosis and screening programs, the Egyptian context requires additional research on this topic.

Bilateral Analysis Boosts the Performance of Mammography-based Deep Learning Models in Breast Cancer Risk Prediction.

Breast cancer is one of the leading causes of death among women. Early prediction of breast cancer can significantly improve the survival rates. Breast density was proven as a reliable risk factor.

Body mass index, breast density, and the risk of breast cancer development in relation to the menopausal status; results from a population-based screening program in a native African-Arab country.

Background: Risk factors are traits or behaviors that have an influence on the development of breast cancer (BC). Awareness of the prevalent risk factors can guide in developing prevention interventions.

Purpose: To evaluate the correlation between the breast density, body mass index, and the risk of breast cancer development in relation to the menopausal status in a native African-Arab population.