This study presents a statistical approach for prediction and uncertainty quantification of a hepatic tumor Microwave Ablation treatment simulation with a spatially varying response. The study examines the variation in the volume of necrotic tissue in a human liver tissue model during Microwave Ablation/Coagulation therapy due to tissue property variation as a function of wattage and treatment time. The analysis also assesses the effects of tissue variation on the size, shape, and percentage of necrotic tissue as a spatially distributed field rather than as a single-value parameter like cross-sectional area or volume. Data with spatial and functional correlations can often be modeled with fewer training data points using machine learning techniques. This approach also has the advantage of representing the entire spatial-transient field, which allows analysis on a greater range of results such as the percentage of necrotic tissue around an ablation region or areas in which tissue was unaffected. The surrogate models developed in this study are used to perform sensitivity analysis and uncertainty quantification studies to assess the variation in volume and shape of necrotic tissues due to variations in human tissue properties and treatment procedures.The microwave heating of a cancer tumor was used to simulate a region of hepatic cancer tissue.&nbsp; A commonly used coaxial microwave ablation antenna, with the corresponding slotted catheter, is positioned in the center of the cylindrical tissue section. The antenna operates at 2.45 GHz, a frequency widely used in microwave ablation therapy. A Design of Experiments (DOE) containing both biological and treatment factors was used to generate proper training data for the surrogate model. The biological factors included temperature, conductivity, permittivity, and specific heat of cancerous hepatic tissue.The surrogate models developed in this study accurately mimic the behavior of the underlying simulation model. These surrogate models were then used to produce sensitivity results and uncertainty margins due to expected variation in human tissue properties corresponding to a mock set of liver tumor scenarios.This study presents a novel methodology for both transient and spatial-transient simulation of tumor ablation treatment using surrogate modeling. This surrogate model will help ensure a microwave ablation device is able to produce predictable ablation zones while keeping ablation time to a minimum.&nbsp; The techniques presented with this case study can be implemented to a wide variety of other medical devices to deliver similar results and improve treatment predictions on a case-by-case basis for patients. The presentation will provide attendees with an understanding of machine learning enhanced methods of modeling and uncertainty quantification in the context of ASME’s V&amp;V 40, by working through an end-to-end example.

Predictive Analytics and Uncertainty Quantification of a Microwave Ablation Simulation With a Spatially Varying Response

Paper Proceedings: https://asmedigitalcollection.asme.org/VVS/VVS2021/volume/84782

ASME 2021 Verification and Validation (VVS2021)

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