ArticleMachine learning: science and technology2021
Improving Proton Dose Calculation Accuracy by Using Deep Learning.
Article in Machine learning: science and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent advances in applying machine learning to proton radiotherapy.Biomedical physics & engineering express · 2025Pooled it
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- Transformer-based rapid dose calculation for carbon ion therapy.Medical physics · 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
- Article
- An Artificial Intelligence Dose Engine for Fast Carbon Ion Treatment Planning.International journal of particle therapy · 2026Article
- Generalizability of deep learning-based dose conversion model in proton beam therapy.Journal of applied clinical medical physics · 2026Article
- Geometry-encoded deep learning (GeoDL) framework for real-time 3D dose verification for online adaptive radiotherapy.Machine learning. Health · 2025Article
- The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review.Nuclear medicine and molecular imaging · 2025Review
- A proof-of-concept study of direct magnetic resonance imaging-based proton dose calculation for brain tumors via neural networks with Monte Carlo-comparable accuracy.Physics and imaging in radiation oncology · 2025Article
- A Python package for fast GPU-based proton pencil beam dose calculation.Journal of applied clinical medical 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
- Automated Euler number of the alveolar capillary network based on deep learning segmentation with verification by stereological methods.Journal of microscopy · 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
- A deep learning-based dose calculation method for volumetric modulated arc therapy.Radiation oncology (London, England) · 2024Article
- Advancing equitable and personalized cancer care: Novel applications and priorities of artificial intelligence for fairness and inclusivity in the patient care workflow.European journal of cancer (Oxford, England : 1990) · 2024Review
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Introduction: Pencil beam (PB) dose calculation is fast but inaccurate due to the approximations when dealing with inhomogeneities. Monte Carlo (MC) dose calculation is the most accurate method but it is time consuming. The aim of this study was to develop a deep learning model that can boost the accuracy of PB dose calculation to the level of MC dose by converting PB dose to MC dose for different tumor sites. Methods: The proposed model uses the PB dose and CT image as inputs to generate the MC dose. We used 290 patients (90 head and neck, 93 liver, 75 prostate and 32 lung) to train, validate, and test the model. For each tumor site, we performed four numerical experiments to explore various combinations of training datasets. Results: Training the model on data from all tumor sites together and using the dose distribution of each individual beam as input yielded the best performance for all four tumor sites. The average gamma passing rate (1mm/1%) between the converted and the MC dose was 92.8%, 92.7%, 89.7% and 99.6% for head and neck, liver, lung, and prostate test patients, respectively. The average dose conversion time for a single field was less than 4 seconds. The trained model can be adapted to new datasets through transfer learning. Conclusions: Our deep learning-based approach can quickly boost the accuracy of PB dose to that of MC dose. The developed model can be added to the clinical workflow of proton treatment planning to improve dose calculation accuracy.
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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.