Digital twin forecasting of microwave ablation via fat quantification image-to-grid computational methods

Abstract

Computational tools, such as "digital twin" modeling, are beginning to enable patient-specific surgical planning of ablative therapies to treat hepatocellular carcinoma. Digital twins models use patient functional data and biomarker imaging to build anatomically accurate models to forecast therapeutic outcomes through simulation, i.e., providing accurate information for guiding clinical decision-making. In microwave ablation (MWA), tissue-specific factors (e.g., tissue perfusion, material properties, disease state, etc.) can affect ablative therapies, but current thermal dosing guidelines do not account for these differences. This study establishes an imaging-data-driven framework to construct digital twin biophysical models to predict ablation extents in livers with varying fat content in MWA. Patient anatomic scans were segmented to develop customized three-dimensional computational biophysical models, and fat-quantification images were acquired to reconstruct spatially accurate biophysical material properties. Simulated patient-specific microwave ablations of homogenous digital-twin models (control) and enhanced digital twin models were performed at four levels of fatty liver disease. When looking at the short diameter (SD), long diameter (LD), ablation volume, and spherical index of the ablation margins - the heterogenous digital-twin models did not produce significantly different ablation margins compared to the control models. Both models produced results that report ablation margins for patients with high-fat livers are larger than low-fat livers (LD of 6.17cm vs. 6.30cm and SD of 2.10 vs. 1.99, respectively). Overall, the results suggest that modeling heterogeneous clinical fatty liver disease using fat-quantitative imaging data has the potential to improve patient specificity for this treatment modality.