Thermal ablation of biological tissues in disease treatment: A review of computational models and future directions

ABSTRACT

Percutaneous thermal ablation has proven to be an effective modality for treating both benign and malignant tumours in various tissues. Among these modalities, radiofrequency ablation (RFA) is the most promising and widely adopted approach that has been extensively studied in the past decades. Microwave ablation (MWA) is a newly emerging modality that is gaining rapid momentum due to its capability of inducing rapid heating and attaining larger ablation volumes, and its lesser susceptibility to the heat sink effects as compared to RFA. Although the goal of both these therapies is to attain cell death in the target tissue by virtue of heating above 50°C, their underlying mechanism of action and principles greatly differs. Computational modelling is a powerful tool for studying the effect of electromagnetic interactions within the biological tissues and predicting the treatment outcomes during thermal ablative therapies. Such a priori estimation can assist the clinical practitioners during treatment planning with the goal of attaining successful tumour destruction and preservation of the surrounding healthy tissue and critical structures. This review provides current state-of-the-art developments and associated challenges in the computational modelling of thermal ablative techniques, viz., RFA and MWA, as well as touch upon several promising avenues in the modelling of laser ablation, nanoparticles assisted magnetic hyperthermia and non-invasive RFA. The application of RFA in pain relief has been extensively reviewed from modelling point of view. Additionally, future directions have also been provided to improve these models for their successful translation and integration into the hospital work flow.

KEYWORDS:

• Thermal ablation
• minimally invasive treatment
• microwave ablation
• radiofrequency ablation
• laser ablation
• nanoparticles-assisted ablation
• nerve ablation
• bioheat transfer
• tissue deformation
• blood vessels
• AI and machine-learning algorithms
• multiscale modelling

Acknowledgments

Authors are grateful to the NSERC and the CRC Program for their support. RM is also acknowledging support of the BERC 2018-2021 program and Spanish Ministry of Science, Innovation and Universities through the Agencia Estatal de Investigacion (AEI) BCAM Severo Ochoa excellence accreditation SEV-2017-0718, and the Basque Government fund Artificial Intelligence in BCAM EXP. 2019/00432.

Declaration of interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this article.