Integration of imaging with liquid biopsy using artificial intelligence for ultra-early detection of breast cancer

Breast cancer early detection using liquid biopsy, circulating tumor DNA (ctDNA), artificial intelligence (AI), and multimodal fusion offers a promising but still emerging research strategy to overcome the limitations of conventional imaging. Breast cancer screening and molecular diagnostics remain constrained by the inability of mammography and magnetic resonance imaging to detect pre-invasive disease, alongside the low sensitivity and spatial ambiguity of ctDNA in early-stage settings. This mini-review summarizes the rationale and recent advances in AI-driven multimodal frameworks that integrate imaging phenotypes with blood-derived genotypic signals through feature-, decision-, and intermediate-level fusion strategies. Such approaches improve diagnostic sensitivity and specificity by capturing complementary biological and structural information, enabling earlier detection and longitudinal risk assessment. Despite this progress, clinical translation is hindered by data heterogeneity, the lack of standardized multimodal datasets, and limited prospective validation. This study highlights the emerging biology-first, imaging-informed framework. Despite recent progress, current multimodal approaches remain largely investigational and require robust prospective evidence before clinical deployment. It outlines key future directions, including federated learning, longitudinal modeling, and large-scale validation, to support the future evolution of scalable and equitable early-detection strategies.