Computational Modeling of Thermal Ablation Zones in the Liver: A Systematic Review

Abstract

Purpose: This systematic review aims to identify, evaluate, and summarize the findings of the literature on existing computational models for radiofrequency and microwave thermal liver ablation planning and compare their accuracy.

Methods: A systematic literature search was performed in the MEDLINE and Web of Science databases. Characteristics of the computational model and validation method of the included articles were retrieved.

Results: The literature search identified 780 articles, of which 35 were included. A total of 19 articles focused on simulating radiofrequency ablation (RFA) zones, and 16 focused on microwave ablation (MWA) zones. Out of the 16 articles simulating MWA, only 2 used in vivo experiments to validate their simulations. Out of the 19 articles simulating RFA, 10 articles used in vivo validation. Dice similarity coefficients describing the overlap between in vivo experiments and simulated RFA zones varied between 0.418 and 0.728, with mean surface deviations varying between 1.1 mm and 8.67 mm.

Conclusion: Computational models to simulate ablation zones of MWA and RFA show considerable heterogeneity in model type and validation methods. It is currently unknown which model is most accurate and best suitable for use in clinical practice.

Keywords: ablation zone simulation; computational modeling; liver neoplasm; therapy planning; thermal ablation.

Figure A1

Schematic illustration of the size-based outcome metrics; (a) Outline of the experimentally created ablation zone. The transverse and longitudinal diameter are represented with the dotted black lines; (b) Outline of the simulated ablation zone. The transverse and longitudinal diameter are represented with the dotted black lines; (c); Illustration of the advancement. The yellow line represents the ablation probe. In this example, the experimentally obtained advancement (blue line) is smaller compared to the simulated advancement (dotted black line). Note: Illustration is in 2D, while outcome metrics might be based on 3D measurements.

Figure A2

Schematic illustration of the shape-based outcome metrics; (a–c) Representation of surface-based outcome metrics; (a) average surface deviation, (b) maximum surface deviation, (c) maximum negative surface deviation. (d–g) Representation of overlap-based outcome metrics; (d) dice similarity coefficient (DSC), (e) sensitivity, (f) positive predictive value (PPV), and (g) negative predictive value. Note: visualization is in 2D, while all outcome metrics were based on 3D measurements.

Figure 1

Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram describing the study-selection process.

Figure 2

Schematic overview of the basic structure of a computational model for simulating thermal liver ablation. The optional dependencies are shown by the dotted lines.

Figure 3

Scatterplot of the relative error in longitudinal and transverse diameters of the modeled MWA zones compared to ex vivo validation [26,30,35,37,38,44,46,52,53,55]. In case of an experimental diameter of 30 mm, a relative error of 0.1 means the simulated diameter was 33 mm.

Figure 4

Scatterplot of the relative error in longitudinal and transverse diameters of the modeled RFA zones compared to ex vivo validation [27,28,32,51]. In case of an experimental diameter of 30 mm, a relative error of 0.1 means the simulated diameter was 33 mm.

Figure 5

Scatterplot of the relative error in longitudinal and transverse diameters of the modeled MWA and RFA zones compared to ex vivo validation. In case of an experimental diameter of 30 mm, a relative error of 0.1 means the simulated diameter was 33 mm.