ArticleRadiation oncology (London, England)2023
Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT.
Article in Radiation oncology (London, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparing the effectiveness of various cumulative doses of cisplatin in treating locally advanced nasopharyngeal cancer during the period of intensity-modulated radiotherapy: a meta-analysis.Frontiers in medicine · 2026Pooled it
- Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Improving deep-learning IMRT plan quality for lymphoma using deliverability-aware training.Journal of applied clinical medical physics · 2026Article
- A hybrid deep learning framework for automated treatment planning in cervical cancer radiotherapy.Journal of applied clinical medical physics · 2026Article
- Prediction of three-dimensional dose distribution for patient-specific quality assurance based on log files using WingsNet.Radiation oncology (London, England) · 2025Article
- Deep learning-powered radiotherapy dose prediction: clinical insights from 622 patients across multiple sites tumor at a single institution.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
- Deformable dose prediction network based on hybrid 2D and 3D convolution for nasopharyngeal carcinoma radiotherapy.Medical & biological engineering & computing · 2025Article
- Automatic IMRT treatment planning through fluence prediction and plan fine-tuning for nasopharyngeal carcinoma.Radiation oncology (London, England) · 2024Article
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8 authors.
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Abstract
backgroundCurrent intensity-modulated radiation therapy (IMRT) treatment planning is still a manual and time/resource consuming task, knowledge-based planning methods with appropriate predictions have been shown to enhance the plan quality consistency and improve planning efficiency. This study aims to develop a novel prediction framework to simultaneously predict dose distribution and fluence for nasopharyngeal carcinoma treated with IMRT, the predicted dose information and fluence can be used as the dose objectives and initial solution for an automatic IMRT plan optimization scheme, respectively.
methodsWe proposed a shared encoder network to simultaneously generate dose distribution and fluence maps. The same inputs (three-dimensional contours and CT images) were used for both dose distribution and fluence prediction. The model was trained with datasets of 340 nasopharyngeal carcinoma patients (260 cases for training, 40 cases for validation, 40 cases for testing) treated with nine-beam IMRT. The predicted fluence was then imported back to treatment planning system to generate the final deliverable plan. Predicted fluence accuracy was quantitatively evaluated within projected planning target volumes in beams-eye-view with 5 mm margin. The comparison between predicted doses, predicted fluence generated doses and ground truth doses were also conducted inside patient body.
resultsThe proposed network successfully predicted similar dose distribution and fluence maps compared with ground truth. The quantitative evaluation showed that the pixel-based mean absolute error between predicted fluence and ground truth fluence was 0.53% ± 0.13%. The structural similarity index also showed high fluence similarity with values of 0.96 ± 0.02. Meanwhile, the difference in the clinical dose indices for most structures between predicted dose, predicted fluence generated dose and ground truth dose were less than 1 Gy. As a comparison, the predicted dose achieved better target dose coverage and dose hot spot than predicted fluence generated dose compared with ground truth dose.
conclusionWe proposed an approach to predict 3D dose distribution and fluence maps simultaneously for nasopharyngeal carcinoma patients. Hence, the proposed method can be potentially integrated in a fast automatic plan generation scheme by using predicted dose as dose objectives and predicted fluence as a warm start.
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