Digital Twin Framework Accelerates Adaptive Proton Therapy Planning for Prostate Cancer
Researchers developed a digital twin (DT) framework for online adaptive proton therapy planning in prostate cancer that reduces treatment reoptimization time from roughly 20 minutes to 5.5 minutes on average. The system combines deep learning-based image registration, daily cone-beam CT anatomy updates, and knowledge-based plan quality scoring, drawing on a database of 43 prior prostate cases. The approach could enable faster, more precise real-time adaptive radiotherapy while reducing radiation dose to surrounding healthy organs.
A research team has published a preprint on arXiv describing a digital twin framework designed to accelerate online adaptive proton therapy planning for prostate stereotactic body radiation therapy (SBRT) with a dominant intraprostatic lesion (DIL) boost. The framework uses deep learning-based multi-atlas deformable image registration and daily cone-beam CT imaging to track interfractional changes in patient anatomy, then rapidly reoptimizes treatment plans accordingly. Benchmarked against 43 prior clinical cases, the DT system achieved an average reoptimization time of 5.5 minutes compared to 19.8 minutes for conventional clinical workflows — a reduction of more than 70%. Plan quality scores for DT-generated plans (157.2) met or exceeded those of clinical plans, with dose coverage for the DIL reaching 99.5% and clinical target volume coverage at 99.8%. Doses to organs at risk, including the bladder, rectum, and urethra, remained within clinical standards. The authors argue the framework is scalable beyond prostate SBRT and could support broader real-time adaptive proton therapy applications.
What's missing
The study is a preprint and has not yet undergone formal peer review. The database of 43 prior cases is relatively small, and the framework has not been validated in a prospective clinical trial or across multiple institutions. Long-term patient outcome data (e.g., tumor control rates, late toxicity) are not reported.
What different sources said
- arXiv physicsCenter
A Digital Twin Framework for Adaptive Treatment Planning in Radiotherapy
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