Minimally invasive cancer treatments (MICTs) such as radiofrequency ablation, microwave ablation, magnetic nanoparticle induced thermal ablation, cryoablation, laser-induced interstitial thermotherapy, and irreversible electroporation rely fundamentally on controlled heat transfer mechanisms to destroy tumours while preserving surrounding healthy tissue. Accurate modeling and real-time simulation of bioheat transfer are critical for predicting temperature distribution and thermal damage within heterogeneous biological tissues.

Traditional computational methods for simulating heat transfer phenomena in biological tissue, for example, the finite element method, finite volume method, and finite difference method, as well as particle-based methods like the lattice Boltzmann method, smoothed particle hydrodynamics, and moving particle semi-implicit method, can accurately represent the complexities of bioheat transfer. However, these approaches often require significant computational resources, limiting their application in real-time, patient-specific scenarios.

Advanced approaches, such as physics-informed neural networks, offer new opportunities by explicitly incorporating the fundamental physics of bioheat transfer into machine-learning models. These mesh-free methods can leverage sparse patient data and provide real-time simulations with personalized treatment planning. Additionally, developments in quantum computing could accelerate large-scale bioheat transfer simulations and optimize treatment parameters for thermal therapies in real-time clinical environments.

This Research Topic explores advances in computational approaches for bioheat transfer in minimally invasive cancer thermal therapies. Articles should address the computational challenges associated with simulating heat transfer mechanisms within biological tissues for improved prediction of therapeutic outcomes and personalized treatment planning.

Research themes within the scope of this collection include:
• Physics-informed neural networks (PINNs) for solving the bioheat equation and modeling multi-modal heat transfer in MICTs
• Mesh-free and particle-based methods for modeling heat transfer and simulating thermal damage in biological tissues
• Real-time, patient-specific simulation frameworks for adaptive, image-guided cancer thermal therapy
• Inverse problem formulations and parameter estimation for bioheat transfer models in MICTs
• Quantum computing and quantum-inspired optimization for accelerating bioheat transfer simulations and treatment parameter estimation
• Integration of bioheat transfer modeling with medical imaging for image-guided MICTs
• Fluid-structure interaction modelling within the context of the bioheat equation for MICTs, including blood flow and tissue deformation
• Data-driven and hybrid approaches for validating bioheat transfer simulations using experimental data.

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Advanced Computational Approaches for Bioheat Transfer in Minimally Invasive Cancer Thermal Therapies