Computer-assisted Treatment Planning and Mathematical Modeling for Percutaneous Liver Tumor Ablation: An Updated...

Area of Science:

• Medical Imaging and Intervention
• Computational Medicine
• Minimally Invasive Surgery

Background:

• Percutaneous liver tumor ablation is a key minimally invasive treatment.
• Current planning methods involve significant manual workload and can impact efficiency.
• Enhancing precision and safety in ablation procedures is a critical clinical need.

Purpose of the Study:

• To review and classify computer-assisted automatic planning (CAAP) technologies for percutaneous liver tumor ablation.
• To outline the theoretical foundations and key technologies of CAAP.
• To evaluate existing CAAP methods and propose future developments.

Main Methods:

• Review of basic principles: thermal ablation, path planning, treatment parameter optimization, temperature field modeling.
• Classification of CAAP methods: traditional optimization, intelligent optimization, deep reinforcement learning.
• Evaluation of methods based on automation, real-time processing, and multi-physics modeling.

Main Results:

• CAAP technology significantly reduces manual workload and improves planning efficiency and reliability.
• Various CAAP methods, including single-needle and multi-needle path planning, are discussed.
• Performance evaluation highlights strengths and weaknesses of different algorithmic approaches.

Conclusions:

• CAAP is vital for advancing precision and safety in percutaneous liver tumor ablation.
• Deep reinforcement learning shows promise for dynamic environmental adaptability in ablation planning.
• Further research is needed to optimize CAAP for complex clinical scenarios.