AI-enabled D-dimeromics in precision breast oncology: a transformative framework for the identification,...

Breast cancer is a highly diverse ailment marked by various molecular subtypes, differing clinical paths, and unique treatment reactions. Notwithstanding considerable progress in precision oncology, the prompt detection of patients with ultra-high-risk breast cancer phenotypes continues to be a significant clinical hurdle. Growing evidence suggests that hypercoagulability linked to cancer and thromboinflammation are vital in tumour advancement, metastatic spread, evasion of immunity, and resistance to treatment. D-dimer has become a notable biomarker for coagulation, signalling tumour aggressiveness, disease extent, and unfavorable clinical results. Nonetheless, traditional D-dimer evaluation depends on singular assessments that do not reflect the intricate biological interactions influencing breast cancer advancement. To overcome this limitation, the idea of D-dimeromics has arisen as a comprehensive framework that combines D-dimer with additional haemostatic, inflammatory, molecular, radiological, and clinical data. Simultaneously, progress in artificial intelligence (AI), particularly in machine learning and deep learning, has facilitated the examination of high-dimensional biomedical data and the identification of predictive patterns that exceed the capabilities of conventional statistical methods. This narrative review examines the biological justifications, technological bases, and clinical uses of AI-driven D-dimeromics as a groundbreaking precision oncology approach for detecting ultra-high-risk breast cancer phenotypes. The article examined the connections between coagulation activation and tumour development, the prognostic value of D-dimer in various breast cancer subtypes, and the function of AI in combining multidimensional data for dynamic risk assessment, metastatic prediction, treatment response evaluation, and individualized treatment strategies.