AI in Proton Therapy Treatment Planning: A Review

TL;DR

This review highlights how artificial intelligence enhances proton therapy treatment planning by automating tasks, improving accuracy, and enabling personalized adaptive strategies, despite current challenges in validation and clinical integration.

Contribution

The paper systematically reviews recent AI applications in proton therapy planning, emphasizing their potential to improve efficiency, accuracy, and personalization in clinical workflows.

Findings

AI automates contouring and image enhancement.

AI predicts dose distributions and accelerates optimization.

Challenges include data scarcity and clinical validation.

Abstract

Purpose: Proton therapy provides superior dose conformity compared to photon therapy, but its treatment planning is challenged by sensitivity to anatomical changes, setup/range uncertainties, and computational complexity. This review evaluates the role of artificial intelligence (AI) in improving proton therapy treatment planning. Materials and methods: Recent studies on AI applications in image reconstruction, image registration, dose calculation, plan optimization, and quality assessment were reviewed and summarized by application domain and validation strategy. Results: AI has shown promise in automating contouring, enhancing imaging for dose calculation, predicting dose distributions, and accelerating robust optimization. These methods reduce manual workload, improve efficiency, and support more personalized planning and adaptive planning. Limitations include data scarcity, model generalizability, and clinical integration. Conclusion: AI is emerging as a key enabler of efficient, consistent, and patient-specific proton therapy treatment planning. Addressing challenges in validation and implementation will be essential for its translation into routine clinical practice.