Volta

AI-driven multimodal molecular imaging for precision oncology in breast cancer: Advances, challenges, and future directions

Precision oncology for breast cancer is currently constrained by substantial biological heterogeneity, which renders traditional anatomical metrics—such as the Response Evaluation Criteria in Solid Tumors (RECIST)—inadequate for capturing distinct molecular subtypes and early therapeutic responses. Integrating artificial intelligence (AI) with multimodal molecular imaging establishes a transformative paradigm for non-invasive, quantitative assessment of tumor biology. This review synthesizes the pivotal role of AI in optimizing positron emission tomography (PET), functional and molecular magnetic resonance imaging (MRI), and optical/photoacoustic imaging, emphasizing technical milestones in reconstruction, automated segmentation, and deep feature fusion. We critically evaluate the clinical evidence for AI-enhanced molecular imaging across the continuum of care, focusing on diagnostic accuracy, non-invasive axillary staging, neoadjuvant therapy monitoring, and prognostic stratification. Despite this promise, …

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