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Mathematical modeling of microwave liver ablation with a variable-porosity medium approach
Claudio Tucci1, Macarena Trujillo2, Enrique Berjano3
1Dipartimento di Medicina e Scienze della Salute "Vincenzo Tiberio", Università del Molise, Via Francesco De Sanctis 1, 86100, Campobasso, Italy.
Computer Methods and Programs in Biomedicine
|December 15, 2021
Summary
This study developed a realistic mathematical model for microwave thermal ablation (MTA) of liver tumors. The model accurately predicts ablation zones, improving treatment efficacy and minimizing damage to healthy tissue by considering variable tissue properties and vascularization.
Area of Science:
- Biomedical Engineering
- Computational Medicine
- Oncology
Background:
- Thermal ablation is a minimally invasive cancer treatment with advantages over surgery and chemotherapy.
- Accurate computational modeling of heat transfer is crucial for predicting ablation zones, preventing tumor recurrence, and preserving healthy tissue.
Purpose of the Study:
- To develop a realistic porous media-based mathematical model for simulating microwave thermal ablation (MTA) of in vivo liver tumors.
- To enhance the prediction of ablation zone accuracy and treatment outcomes.
Main Methods:
- Coupling modified Local Thermal Non Equilibrium (LTNE) equations with electromagnetic equations.
- Incorporating a variable porosity function based on experimental in vivo data.
- Investigating the impact of four different blood vessel distributions on ablation outcomes.
Main Results:
- Temperature fields, ablation diameters, and volumes were calculated using the Arrhenius thermal damage model.
- Results demonstrated strong agreement with a clinical study on hepatocellular carcinoma when considering terminal artery distribution.
- The model's accuracy was validated against clinical data for MTA of liver tumors.
Conclusions:
- A variable-porosity medium model was successfully developed for simulating in vivo liver tumor MTA.
- Accurate modeling, incorporating realistic features like vascularization, is essential for improving MTA protocols and devices.
- This approach helps avoid tumor recurrence and optimize treatment for liver cancer.
