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Updated: Jan 15, 2026

Dual-phase Cone-beam Computed Tomography to See, Reach, and Treat Hepatocellular Carcinoma during Drug-eluting Beads Transarterial Chemo-embolization

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Toward Digital Twins for Optimal Radioembolization.

Nisanth Kumar Panneerselvam¹, Guneet Mummaneni¹, Emilie Roncali²

¹Department of Biomedical Engineering, University of California Davis, Davis, CA, USA.

|October 15, 2025

Summary

Digital twins using computational fluid dynamics and AI can personalize liver radioembolization. This approach enhances treatment precision by modeling complex blood flow and microsphere delivery for better patient outcomes.

Keywords:

CFD Computational fluid dynamics Digital twins GANs Generative AI Interventional radiology PINNs Radioembolization

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Area of Science:

Hepatobiliary Medicine
Medical Imaging & Simulation
Interventional Oncology

Background:

Radioembolization is a liver cancer treatment using radioactive microspheres delivered via the hepatic artery.
Treatment effectiveness is influenced by complex hepatic artery anatomy, blood flow dynamics, and microsphere behavior.
Optimizing treatment delivery requires advanced methods to account for patient-specific variations.

Purpose of the Study:

To review the application of computational fluid dynamics (CFD) and generative artificial intelligence (AI) in radioembolization.
To highlight the role of physics-informed neural networks in creating patient-specific digital twins.
To discuss the potential of digital twins for enhancing personalized and precise radioembolization treatment planning.

Main Methods:

Review of core principles in computational fluid dynamics (CFD) for simulating blood flow.
Discussion of generative artificial intelligence (AI) techniques, particularly physics-informed neural networks.
Exploration of digital twin technology for patient-specific modeling in medical applications.

Main Results:

CFD and AI-powered digital twins offer a promising approach to model complex hepatic arterial anatomy and blood flow.
Physics-informed AI can integrate physiological data to create accurate patient-specific simulations.
These digital twins can predict microsphere transport and deposition, aiding treatment planning.

Conclusions:

Patient-specific digital twins integrating CFD and AI represent a significant advancement in radioembolization planning.
This technology facilitates enhanced personalization and precision in microsphere delivery.
The translation of digital twins into clinical practice holds potential for improved liver cancer treatment outcomes.