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Simulation of Image-Guided Microwave Ablation Therapy Using a Digital Twin Computational Model.
Frankangel Servin1,2, Jarrod A Collins1, Jon S Heiselman1,2,3
1Department of Biomedical EngineeringVanderbilt University Nashville TN 37235 USA.
IEEE Open Journal of Engineering in Medicine and Biology
|March 6, 2024
Summary
Healthcare digital twins (DTs) enhance liver cancer microwave ablation (MWA) planning. Increased liver fat significantly boosts MWA volumes, with tumor presence impacting results in specific simulations.
Area of Science:
- Computational modeling in medicine
- Biophysical simulation for surgical planning
- Medical imaging and image analysis
Background:
- Patient-specific surgical planning for liver cancer is advancing with computational tools like healthcare digital twins (DTs).
- Microwave ablation (MWA) is used for liver cancer treatment, but current guidelines lack tissue-specific parameters like perfusion, steatosis, and fibrosis.
- Understanding these factors is crucial for optimizing MWA therapeutic extent.
Purpose of the Study:
- To establish an MR imaging framework for creating 3D biophysical DTs of the liver.
- To predict MWA delivery in livers with varying fat content (steatosis) and in the presence of a tumor.
- To evaluate the impact of tissue-specific factors on MWA outcomes.
Main Methods:
- Constructed patient-specific liver DTs using structural, functional, and biomarker imaging data.
- Simulated MWA using 915 MHz and 2450 MHz antennae in DTs with five grades of steatosis, both with and without a tumor.
- Analyzed four microwave antenna placement strategies and their impact on ablation zone coverage.
Main Results:
- Liver fat content significantly increased ablation volumes: 29-42% in Tumor Naïve DTs and 55-60% in Tumor Informed DTs.
- Tumor presence did not significantly affect ablation volumes in 915 MHz simulations but increased them in mild, moderate, and high-fat steatosis grades for 2450 MHz simulations.
- Accounting for patient-specific tumor properties significantly impacts ablation forecasting for preoperative evaluation.
Conclusions:
- Liver fat content is a critical factor influencing MWA efficacy, necessitating its inclusion in treatment planning.
- The developed MR imaging framework and DTs enable accurate prediction of MWA delivery, improving surgical planning.
- DTs offer a powerful tool for personalized medicine, optimizing MWA for liver cancer treatment by considering patient-specific factors.
