AI in Proton Therapy Treatment Planning: A Review
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866911226303348736 |
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| author | Ding, Yuzhen Feng, Hongying Bues, Martin Fatyga, Mirek Liu, Tianming Whitaker, Thomas J. Lin, Haibo Lee, Nancy Y. Simone II, Charles B. Patel, Samir H. Ma, Daniel J. Frank, Steven J. Vora, Sujay A. Ashman, Jonathan A. Liu, Wei |
| author_facet | Ding, Yuzhen Feng, Hongying Bues, Martin Fatyga, Mirek Liu, Tianming Whitaker, Thomas J. Lin, Haibo Lee, Nancy Y. Simone II, Charles B. Patel, Samir H. Ma, Daniel J. Frank, Steven J. Vora, Sujay A. Ashman, Jonathan A. Liu, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_19213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI in Proton Therapy Treatment Planning: A Review Ding, Yuzhen Feng, Hongying Bues, Martin Fatyga, Mirek Liu, Tianming Whitaker, Thomas J. Lin, Haibo Lee, Nancy Y. Simone II, Charles B. Patel, Samir H. Ma, Daniel J. Frank, Steven J. Vora, Sujay A. Ashman, Jonathan A. Liu, Wei Medical Physics 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. |
| title | AI in Proton Therapy Treatment Planning: A Review |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2510.19213 |