Digital Twin Models for Enhanced Predictive Analysis and Treatment Optimization in Breast Cancer

Breast cancer ranks as the second most concerning cause of death for women worldwide, with an expected 2.3 million new cases by 2024. The disease’s development involves a mix of genetic mutations, hormonal elements, and environmental factors. Existing treatment methods struggle to address individual variations and the complexity of each case, underscoring the demand for new solutions. Digital twin technology emerges as a promising avenue to boost cancer detection, therapy, personalized medicine, and treatments by constructing an accurate digital replica of a patient’s body. By amalgamating genetic, biochemical, and lifestyle data, digital twins could assist in safe diagnoses and tailor-made treatment plans. Progress in math, computer science, and experimental technology has propelled the usability of digital twins in cancer research areas, enhancing virtual experiments, treatment accessibility, and patient empowerment. This review delves into the potential of digital twin technology in revolutionizing the management of breast cancer, emphasizing the significance of real-time data and simulation techniques for the broader audience’s awareness with the dawn of a new medical era, leveraging digital twins in cancer care holds increasing importance in enhancing patient outcomes.