Toward Digital Twins for Optimal Radioembolization

Radioembolization is a liver cancer treatment delivering radioactive microspheres (20-60 μm) to tumors via a catheter in the hepatic arterial tree. Treatment response depends on multiple factors including the complex hepatic artery anatomy, variable blood flow, and microsphere transport. Patient-specific digital twins powered by computational fluid dynamics (CFD) and physics-informed artificial intelligence (AI) methods offer a promising solution to optimize planning. This review discusses core principles of CFD and generative AI applied to radioembolization, emphasizing physics-informed networks and their role in translating digital twins into clinical practice for enhanced personalization and precision in treatment delivery.