Purpose: Thermal ablation of liver tumors has emerged as a first-line curative treatment for single small tumors (diameter < 2.5 cm) due to similar overall survival rates as surgical resection. Moreover, it is far less invasive, has lower complication rates, a superior cost-effectiveness, and an extremely low treatment-associated mortality. However, in many cases, complete tumor coverage cannot be achieved only with a single electrode and several electrodes are used to create overlapping ablations. Multi-electrode planning is a challenging 3D task with many contradictive constraints to consider, a dimensionality difficult to assess even for experts. It requires extremely long planning time since it is mostly performed mentally by clinicians looking at 2D CT views. An accurate and reliable prediction of the ablation zone would help to turn thermal ablation into a first-line curative treatment also for large liver tumors treated with multiple electrodes. In order to determine the level of model simplification that can be acceptable, we compared three computational models, a simple spherical model, a biophysics-based model and an Eikonal model.
Methods: RF ablation electrodes were virtually placed at a desired position in the patient pre-operative CT image and the models predicted the ablation zone generated by multiple electrodes. The last two models are patient-specific. In these cases, hepatic structures were automatically segmented from the pre-operative CT images to predict a patient-specific ablation zone.
Results: The three models were used to simulate multiple electrode ablations on 12 large tumors from 11 patients for which the procedure information was available. Biophysics-based simulations approximate better the post-operative ablation zone in term of Hausdorff distance, Dice Similarity Coefficient, radius, and volume compared to two other methods. It also predicts better the coverage percentage and thus the tumor ablation margin.
Conclusion: The results obtained with the biophysics-based model indicate that it could improve ablation planning by accurately predicting the ablation zone, avoiding over or under-treatment. This is particularly beneficial for multi-electrode radiofrequency ablation of larger liver tumors where the planning phase is particularly challenging.
Keywords: Computational model; Computer-aided intervention; Liver tumor; Radiofrequency ablation; Simulation.
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