Digital Twin Framework Accelerates Adaptive Proton Therapy Planning for Prostate Cancer
Researchers developed a digital twin framework using deep learning and daily imaging to speed up treatment planning for prostate cancer radiotherapy, reducing reoptimization time from ~20 minutes to ~5.5 minutes. The system uses historical patient data and cone-beam CT scans to predict anatomical changes and pre-generate treatment plans in real time. This advancement could improve treatment precision while reducing radiation exposure to healthy tissues in adaptive radiotherapy.
A new digital twin framework integrates deep learning-based image registration, daily patient imaging updates, and automated plan quality evaluation to enable faster adaptive proton therapy planning for prostate stereotactic body radiation therapy (SBRT) with intraprostatic lesion boosts. Tested on 43 prior prostate SBRT cases, the system predicts how patient anatomy changes between treatment sessions and pre-generates multiple treatment plans. When daily cone-beam CT scans are acquired, the framework rapidly reoptimizes plans in approximately 5.5 minutes compared to 19.8 minutes for traditional clinical workflows. The digital twin plans achieved superior or equivalent plan quality scores and dose coverage metrics while minimizing radiation to organs at risk, including bladder, rectum, and urethra doses within clinical standards. By addressing anatomical variations efficiently, the framework enhances treatment precision and supports real-time adaptive radiotherapy, offering a scalable solution for prostate cancer treatment.
What's missing
The study's limitations include its retrospective design using only 43 prior cases, which may limit generalizability to diverse patient populations. The framework's performance on patients with significantly different anatomies or treatment complexities beyond the training dataset is unclear. Clinical validation through prospective trials and comparison with other adaptive planning methods would strengthen evidence for clinical adoption.
What different sources said
- arXiv physicsCenter
A Digital Twin Framework for Adaptive Treatment Planning in Radiotherapy
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