AI in Proton Therapy Treatment Planning: A Narrative Review.
8 affiliations
- 1Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA.
- 2School of Computing, The University of Georgia, Athens, GA 30602, USA.
- 3Department of Computer & Cyber Sciences, Augusta University, Augusta, GA 30912, USA.
- 4Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
- 5New York Proton Center, New York City, NY 10035, USA.
- 6Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York City, NY 10065, USA.
- 7Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, USA.
- 8Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77054, USA.
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 narrative review evaluates the role of artificial intelligence (AI) in improving proton therapy treatment planning, with emphasis on proton-specific requirements for dose-calculation accuracy, robustness, and adaptive decision-making.Materials and methods
Recent studies on AI applications across the proton therapy planning workflow, including imaging, registration, segmentation, contour assessment, dose prediction/denoising, optimization, adaptive therapy, and large language model (LLM) and multimodal AI were reviewed and summarized by application domain and validation strategy. Both proton-therapy-specific studies and selected general radiotherapy AI studies were included when they introduced methods relevant to proton treatment planning, with attention to the additional validation needed for proton therapy.Results
AI has shown promise in enhancing imaging for dose calculation, automating contouring and contour assessment, accelerating dose calculation, predicting and denoising dose distributions, supporting robust optimization, and improving adaptive planning workflows. Compared with photon therapy, AI tools in proton therapy require more stringent evaluation because errors in AI-generated images, deformation fields, contours, dose estimates, or optimization outputs may propagate into water-equivalent path length, range uncertainty, scenario-specific dose distributions, and adaptive replanning decisions. Emerging applications, including LLMs and multimodal foundation models, may further support clinical-context integration, treatment-planning documentation, plan review, and workflow coordination. Key limitations include data scarcity, limited generalizability, physical interpretability, uncertainty management, lack of standardized proton-specific benchmarks, and limited prospective validation.Conclusion
AI is emerging as a key enabler of efficient, consistent, and patient-specific proton therapy treatment planning. Clinical translation will require interpretable and physics-aware models, multi-institutional validation, workflow-level testing, and evaluation using proton-specific endpoints such as HU/RSP accuracy, range error, dose-calculation accuracy, robustness, deliverability, and adaptive decision consistency.
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BibTeX
@article{ding2026proton,
title = {{AI in Proton Therapy Treatment Planning: A Narrative Review.}},
author = {Yuzhen Ding and Hongying Feng and Martin Bues and Mirek Fatyga and Tianming Liu and Wei Zhang and Thomas J Whitaker and Haibo Lin and Nancy Y Lee and Charles B Simone and Samir H Patel and Daniel J Ma and Steven J Frank and Sujay A Vora and Jonathan B Ashman and Wei Liu},
journal = {International journal of particle therapy},
year = {2026},
month = sep,
doi = {10.1016/j.ijpt.2026.101965},
url = {https://europepmc.org/article/MED/42890210},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI in Proton Therapy Treatment Planning: A Narrative Review.
AU - Yuzhen Ding
AU - Hongying Feng
AU - Martin Bues
AU - Mirek Fatyga
AU - Tianming Liu
AU - Wei Zhang
AU - Thomas J Whitaker
AU - Haibo Lin
AU - Nancy Y Lee
AU - Charles B Simone
AU - Samir H Patel
AU - Daniel J Ma
AU - Steven J Frank
AU - Sujay A Vora
AU - Jonathan B Ashman
AU - Wei Liu
PY - 2026
DA - 2026/09/25
JO - International journal of particle therapy
DO - 10.1016/j.ijpt.2026.101965
UR - https://europepmc.org/article/MED/42890210
AB - 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 narrative review evaluates the role of artificial intelligence (AI) in improving proton therapy treatment planning, with emphasis on proton-specific requirements for dose-calculation accuracy, robustness, and adaptive decision-making.Materials and methods Recent studies on AI applications across the proton therapy planning workflow, including imaging, registration, segmentation, contour assessment, dose prediction/denoising, optimization, adaptive therapy, and large language model (LLM) and multimodal AI were reviewed and summarized by application domain and validation strategy. Both proton-therapy-specific studies and selected general radiotherapy AI studies were included when they introduced methods relevant to proton treatment planning, with attention to the additional validation needed for proton therapy.Results AI has shown promise in enhancing imaging for dose calculation, automating contouring and contour assessment, accelerating dose calculation, predicting and denoising dose distributions, supporting robust optimization, and improving adaptive planning workflows. Compared with photon therapy, AI tools in proton therapy require more stringent evaluation because errors in AI-generated images, deformation fields, contours, dose estimates, or optimization outputs may propagate into water-equivalent path length, range uncertainty, scenario-specific dose distributions, and adaptive replanning decisions. Emerging applications, including LLMs and multimodal foundation models, may further support clinical-context integration, treatment-planning documentation, plan review, and workflow coordination. Key limitations include data scarcity, limited generalizability, physical interpretability, uncertainty management, lack of standardized proton-specific benchmarks, and limited prospective validation.Conclusion AI is emerging as a key enabler of efficient, consistent, and patient-specific proton therapy treatment planning. Clinical translation will require interpretable and physics-aware models, multi-institutional validation, workflow-level testing, and evaluation using proton-specific endpoints such as HU/RSP accuracy, range error, dose-calculation accuracy, robustness, deliverability, and adaptive decision consistency.
SN - 2331-5180
ER -