ArticleFrontiers in oncology2021
Virtual Patient-Specific Quality Assurance of IMRT Using UNet++: Classification, Gamma Passing Rates Prediction, and Dose Difference Prediction.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- Prediction of patient-specific quality assurance for volumetric modulated arc therapy using machine log files.Quantitative imaging in medicine and surgery · 2026Article
- Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures.Journal of applied clinical medical physics · 2026Article
- A multimodal multi-scale transformer for virtual pretreatment patient-specific QA of SBRT using portal-dosimetry fluence maps.Scientific reports · 2026Article
- Evaluating the trade-off between plan complexity, dosimetric accuracy, and treatment efficiency: Role of aperture shape controller settings in HyperArc for intracranial oligometastases.Precision radiation oncology · 2025Article
- Prediction of three-dimensional dose distribution for patient-specific quality assurance based on log files using WingsNet.Radiation oncology (London, England) · 2025Article
- A Balanced Multimodal Multi-Task Deep Learning Framework for Robust Patient-Specific Quality Assurance.Diagnostics (Basel, Switzerland) · 2025Article
- Research on the error pattern recognition of dosimetric quality assurance by Bayesian optimization.Translational cancer research · 2025Article
- A feasibility study of deep learning prediction model for VMAT patient-specific QA.Frontiers in oncology · 2025Article
- AI-enhanced cancer radiotherapy quality assessment: utilizing daily linac performance, radiomics, dosimetrics, and planning complexity.Frontiers in oncology · 2025Article
- Assessment of the deep learning-based gamma passing rate prediction system for 1.5 T magnetic resonance-guided linear accelerator.Radiological physics and technology · 2024Article
- Multi-institutional generalizability of a plan complexity machine learning model for predicting pre-treatment quality assurance results in radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Therapeutic dose prediction using score-based diffusion model for pretreatment patient-specific quality assurance.Frontiers in oncology · 2024Article
- Pretreatment patient-specific quality assurance prediction based on 1D complexity metrics and 3D planning dose: classification, gamma passing rates, and DVH metrics.Radiation oncology (London, England) · 2023Article
- Improvement of deep learning prediction model in patient-specific QA for VMAT with MLC leaf position map and patient's dose distribution.Journal of applied clinical medical physics · 2023Article
- Image-based features in machine learning to identify delivery errors and predict error magnitude for patient-specific IMRT quality assurance.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2023Article
- Article
- A recurrent neural network for rapid detection of delivery errors during real-time portal dosimetry.Physics and imaging in radiation oncology · 2022Article
- Deep Learning for Patient-Specific Quality Assurance: Predicting Gamma Passing Rates for IMRT Based on Delivery Fluence Informed by log Files.Technology in cancer research & treatmentArticle
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15 authors.
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Abstract
The dose verification in radiotherapy quality assurance (QA) is time-consuming and places a heavy workload on medical physicists. To provide a clinical tool to perform patient specific QA accurately, the UNet++ is investigated to classify failed or pass fields (the GPR lower than 85% is considered "failed" while the GPR higher than 85% is considered "pass"), predict gamma passing rates (GPR) for different gamma criteria, and predict dose difference from virtual patient-specific quality assurance in radiotherapy. UNet++ was trained and validated with 473 fields and tested with 95 fields. All plans used Portal Dosimetry for dose verification pre-treatment. Planar dose distribution of each field was used as the input for UNet++, with QA classification results, gamma passing rates of different gamma criteria, and dose difference were used as the output. In the test set, the accuracy of the classification model was 95.79%. The mean absolute error (MAE) were 0.82, 0.88, 2.11, 2.52, and the root mean squared error (RMSE) were 1.38, 1.57, 3.33, 3.72 for 3%/3mm, 3%/2 mm, 2%/3 mm, 2%/2 mm, respectively. The trend and position of the predicted dose difference were consistent with the measured dose difference. In conclusion, the Virtual QA based on UNet++ can be used to classify the field passed or not, predict gamma pass rate for different gamma criteria, and predict dose difference. The results show that UNet++ based Virtual QA is promising in quality assurance for radiotherapy.
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