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TRUVACE RECORD VERSION
record: TRV-2026-1790
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-18T06:54:24.948447Z
status: published
lens: trace
sector: health
headline: AI in Proton Therapy Treatment Planning: A Narrative Review
dek: 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 application…
gain_title: AI tools are showing promise for automating contouring, accelerating dose calculation, predicting dose distributions and improving adaptive replanning in proton therapy planning.
problem_title: Errors in AI-generated images, contours, dose estimates or optimization outputs can propagate into range uncertainty and adaptive replanning decisions, so proton therapy requires more stringent evaluation than photon therapy.
trace_subject: AI in proton therapy treatment planning
gain_reading: AI tools are showing promise for automating contouring, accelerating dose calculation, predicting dose distributions and improving adaptive replanning in proton therapy planning.
gain_evidence: AI has shown promise in enhancing imaging for dose calculation, automating contouring and contour assessment, accelerating dose calculation
problem_reading: Errors in AI-generated images, contours, dose estimates or optimization outputs can propagate into range uncertainty and adaptive replanning decisions, so proton therapy requires more stringent evaluation than photon therapy.
problem_evidence: errors in AI-generated images, deformation fields, contours, dose estimates, or optimization outputs may propagate into water-equivalent path length, range uncertainty
quick_read: This narrative review surveyed recent studies on AI across the proton therapy planning workflow, including imaging, registration, segmentation, contour assessment, dose prediction and denoising, optimization, adaptive therapy, and large language and multimodal models. Studies were grouped by application domain and validation strategy.

The review matters because proton therapy offers tight dose conformity but is sensitive to anatomical change and range uncertainty, so planning errors carry particular weight. The authors say AI is emerging as an enabler of efficient and consistent planning, but clinical translation depends on validation that has not yet been widely done.
limitation: The authors identify unresolved barriers: data scarcity, limited generalizability, limited physical interpretability, uncertainty management, no standardized proton-specific benchmarks, and limited prospective validation.
tag: Dual reading
key_points: Proton therapy offers superior dose conformity versus photon therapy but is challenged by anatomical changes, setup and range uncertainties, and computational complexity. | Reviewed AI uses include imaging for dose calculation, automated contouring, faster dose calculation, dose prediction and denoising, robust optimization and adaptive planning. | Emerging LLM and multimodal foundation models may support documentation, plan review, clinical-context integration and workflow coordination. | Proposed proton-specific endpoints include HU/RSP accuracy, range error, dose-calculation accuracy, robustness, deliverability and adaptive decision consistency.
rundown: The review included proton-therapy-specific studies and selected general radiotherapy AI studies when their methods were relevant to proton planning, paying attention to the extra validation proton therapy needs.

Because AI errors in images, deformation fields, contours, dose estimates or optimization outputs may propagate into water-equivalent path length and scenario-specific dose distributions, the authors argue proton tools need more stringent evaluation than photon tools.

The authors call for interpretable, physics-aware models, multi-institutional validation and workflow-level testing before clinical translation.
sources:
- peer_reviewed | International Journal of Particle Therapy | https://doi.org/10.1016/j.ijpt.2026.101965 | 2026-09-25
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