Intelligent ensemble learning-enhanced finite element modeling for precision thermal ablation in cancer therapy

Microwave ablation (MWA) is a minimally invasive treatment for liver tumors, yet accurate prediction of ablation zones remains challenging due to tissue heterogeneity, uncertain antenna placement, and complex thermal dynamics.

This study develops a hybrid computational framework that integrates finite element modeling (FEM) with supervised machine learning to improve the prediction and optimization of MWA-induced tissue ablation.

Ex vivo porcine liver experiments were conducted at varying power (40, 60, 80 W) and duration (3, 5, 10 min) settings, measuring long-axis, short-axis, and volumetric ablation outcomes. FEM simulations captured the coupled electromagnetic and thermal processes, while a Random Forest regression model was trained on FEM-generated data to optimize antenna insertion depth and predict ablation geometry with high fidelity. The framework enabled systematic exploration of the parameter space and reduced reliance on iterative laboratory experiments.

The integrated FEM-ML approach accurately predicted ablation dimensions, showing strong agreement with experimental measurements. Optimized antenna placement enhanced spatial and temporal temperature predictions, allowing precise estimation of lesion size, shape, and volume. The method supports rapid, patient-specific treatment planning and minimizes collateral tissue damage.

Combining FEM simulations with supervised learning provides a scalable, data-driven framework for precision MWA. This approach improves predictive reliability, accelerates treatment planning, and enables adaptive, optimized thermal therapies, offering potential for enhanced clinical outcomes in minimally invasive oncological interventions.