ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2023
Implementation and evaluation of an intelligent automatic treatment planning robot for prostate cancer stereotactic body radiation therapy.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- An interactive virtual dosimetrist for adjusting radiotherapy dose quality.Physics and imaging in radiation oncology · 2026Article
- A rule-based, feedback-driven framework for fully automated prostate VMAT planning.Journal of applied clinical medical physics · 2026Article
- Reference-Free large language model agents for physician-guided radiotherapy treatment planning.Medical physics · 2026Article
- Gamma Knife treatment planning using knowledge-based reinforcement learning.Medical physics · 2026Article
- Objective-function-guided automated VMAT planning reduces OAR dose, low-dose exposure, and inter-planner variability in breast radiotherapy.Scientific reports · 2026Article
- Towards human-centric intelligent treatment planning for radiation therapy.NPJ digital medicine · 2026Review
- AI-driven precision diagnosis and treatment of prostate cancer: a narrative review.Frontiers in oncology · 2026Review
- Article
- NRG Oncology Assessment of Artificial Intelligence for Automatic Treatment Planning in Radiation Therapy Clinical Trials: Present and Future.International journal of radiation oncology, biology, physics · 2025Article
- Artificial intelligence in prostate cancer.Chinese medical journal · 2025Review
- Actor critic with experience replay-based automatic treatment planning for prostate cancer intensity modulated radiotherapy.Medical physics · 2025Article
- An isodose-constrained automatic treatment planning strategy using a multicriteria predicted dose rating.Medical physics · 2025Article
- Feasibility study of automatic radiotherapy treatment planning for cervical cancer using a large language model.Radiation oncology (London, England) · 2025Article
- A physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans.Physics and imaging in radiation oncology · 2025Article
- Exploration of an adaptive proton therapy strategy using CBCT with the concept of digital twins.Physics in medicine and biology · 2025Article
- The promise of artificial intelligence-assisted radiotherapy for prostate cancer in Morocco: a transformational opportunity.Frontiers in medicine · 2025Article
- Human-like intelligent automatic treatment planning of head and neck cancer radiation therapy.Physics in medicine and biology · 2024Article
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4 authors.
Funding
Abstract
purposeWe previously developed a virtual treatment planner (VTP), an artificial intelligence robot, operating a treatment planning system (TPS). Using deep reinforcement learning guided by human knowledge, we trained the VTP to autonomously adjust relevant parameters in treatment plan optimization, similar to a human planner, to generate high-quality plans for prostate cancer stereotactic body radiation therapy (SBRT). This study describes the clinical implementation and evaluation of VTP. MATERIALS AND
methodsWe integrate VTP with Eclipse TPS using scripting Application Programming Interface. VTP observes dose-volume histograms of relevant structures, decides how to adjust dosimetric constraints, including doses, volumes, and weighting factors, and applies the adjustments to the TPS interface to launch the optimization engine. This process continues until a high-quality plan is achieved. We evaluated VTP's performance using the prostate SBRT case from the 2016 American Association of Medical Dosimetrist/Radiosurgery Society plan study with its plan scoring system, and compared to human-generated plans submitted to the challenge. Using the same scoring system, we also compared the plan quality of 36 prostate SBRT cases (20 planned with IMRT and 16 planned with VMAT) treated at our institution for both VTP and human-generated plans.
resultsIn the plan study case, VTP achieved a score of 142.1/150.0, ranking the third in the competition (median 134.6). For the clinical cases, VTP achieved 110.6 ± 6.5 for 20 IMRT plans and 126.2 ± 4.7 for 16 VMAT plans, similar to scores of human-generated plans with 110.4 ± 7.0 for IMRT plans and 125.4 ± 4.4 for VMAT plans. The workflow, plan quality and planning time of VTP were reviewed to be satisfactory by experienced physicists.
conclusionWe successfully implemented VTP to operate a TPS for autonomous human-like treatment planning for prostate SBRT.
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