ArticleMedical physics2022
Feasibility study of fast intensity-modulated proton therapy dose prediction method using deep neural networks for prostate cancer.
Article in Medical physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 20 citations in OpenAlex.
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- Quality assurance for deep learning-based proton therapy planning in routine clinical use for oropharyngeal cancer.Physics and imaging in radiation oncology · 2026Article
- Deep learning-based dose prediction in proton beam therapy for hepatocellular carcinoma: comparison of network architectures and loss functions.Journal of radiation research · 2026Article
- Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: Physics enhances generalizability.Medical physics · 2026Article
- Generalizability of deep learning-based dose conversion model in proton beam therapy.Journal of applied clinical medical physics · 2026Article
- Developing an automatic decision-assistance tool to choose proton/photon radiotherapy for patients with prostate cancer.Journal of applied clinical medical physics · 2026Article
- 3D Swin Transformer for patient-specific proton dose prediction of brain cancer patients.Acta oncologica (Stockholm, Sweden) · 2025Article
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- 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
- Improvement of deep learning-based dose conversion accuracy to a Monte Carlo algorithm in proton beam therapy for head and neck cancers.Journal of radiation research · 2025Article
- Deep learning-based prediction of Monte Carlo dose distribution for heavy ion therapy.Physics and imaging in radiation oncology · 2025Article
- Current status and upcoming developments for online adaptive proton therapy enabling a closed feedback loop for near real-time adaptation.Frontiers in oncology · 2025Review
- Quality assurance for online adaptive radiotherapy: a secondary dose verification model with geometry-encoded U-Net.Machine learning: science and technology · 2024Article
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Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeCompared to the pencil-beam algorithm, the Monte-Carlo (MC) algorithm is more accurate for dose calculation but time-consuming in proton therapy. To solve this problem, this study uses deep learning to provide fast 3D dose prediction for prostate cancer patients treated with intensity-modulated proton therapy (IMPT).
methodsA novel recurrent U-net (RU-net) architecture was trained to predict the 3D dose distribution. Doses, CT images, and beam spot information from IMPT plans were used to train the RU-net with a five-fold cross-validation. However, predicting the complicated dose properties of the IMPT plan is difficult for neural networks. Instead of the peak-monitor unit (MU) model, this work develops the multi-MU model that adopted more comprehensive inputs and was trained with a combinational loss function. The dose difference between the prediction dose and Monte Carlo (MC) dose was evaluated with gamma analysis, dice similarity coefficient (DSC), and dose-volume histogram (DVH) metrics. The MC dropout was also added to the network to quantify the uncertainty of the model.
resultsCompared to the peak-MU model, the multi-MU model led to smaller mean absolute errors (3.03% vs. 2.05%, p = 0.005), higher gamma-passing rate (2 mm, 3%: 97.42% vs. 93.69%, p = 0.005), higher dice similarity coefficient, and smaller relative DVH metrics error (clinical target volume (CTV) D
conclusionThis method was a nearly real-time IMPT dose prediction algorithm with accuracy comparable to the pencil beam (PB) analytical algorithms used in prostate cancer. This RU-net might be used in plan robustness optimization and robustness evaluation in the future.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.