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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Computational modeldigital twinfatty liver diseasefinite elementliver cancermicrowave ablation

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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.