Challenges to validate multi-physics model of liver tumor radiofrequency ablation from pre-clinical data

Abstract

The planning and interventional guidance of liver tumor radiofrequency ablation (RFA) is difficult due to the cooling effect of large vessels and the large variability of tissue parameters. Subject-specific modeling of RFA is challenging as it requires the knowledge of model geometry and hemodynamics as well as the simulation of heat transfer and cell death mechanisms.In this paper, we propose to validate such a model from pre-operative multi-modal images and intra-operative signals (temperature and power) measured by the ablation device itself. In particular, the RFA computation becomes subject-specific after three levels of personalization: anatomical, heat transfer, and a novel cellular necrosis model. We propose an end-to-end pre-clinical validation framework that considers the most comprehensive dataset for model validation. This framework can also be used for parameter estimation and we evaluate its predictive power in order to fully assess the possibility to personalize our model in the future. Such a framework would therefore not require any necrosis information, thus better suited for clinical applications. We evaluated our approach on seven ablations from three healthy pigs.The predictive power of the model was tested: a mean point-to-mesh error between predicted and actual ablation extent of 3.5mm was achieved.