ArticleJournal of applied clinical medical physics2023
A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Physics-informed neural network with adaptive loss balancing for real-time radiotherapy dose prediction and verification.Scientific reports · 2026Article
- Federated learning-driven intelligent framework for multi-center radiotherapy dose distribution prediction oriented toward linear accelerators.Scientific reports · 2026Article
- A deep learning-driven automated treatment planning framework for cervical cancer patients treated with volumetric modulated arc therapy.Radiation oncology (London, England) · 2026Article
- Ovarian dose prediction after ovarian transposition in cervical cancer: comparing three models and the impact of geometric distance on predictive performance.Frontiers in oncology · 2026Article
- A comparative analysis of deep learning architectures with data augmentation and multichannel input for locoregional breast cancer radiotherapy.Journal of applied clinical medical physics · 2025Article
- Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.Journal of applied clinical medical physics · 2025Article
- Enhancing stereotactic ablative boost radiotherapy dose prediction for bulky lung cancer: A multi-scale dilated network approach with scale-balanced structure loss.Journal of applied clinical medical physics · 2025Article
- A deep learning model for predicting the modified micro-dosimetric kinetic model-based dose and the dose-averaged linear energy transfer for prostate cancer in carbon ion therapy.Physics and imaging in radiation oncology · 2024Article
- Predicting voxel-level dose distributions of single-isocenter volumetric modulated arc therapy treatment plan for multiple brain metastases.Frontiers in oncology · 2024Article
- A comparative study of deep learning-based knowledge-based planning methods for 3D dose distribution prediction of head and neck.Journal of applied clinical medical physics · 2023Article
- 'Line' Constraints Optimization for Improved Dose Distribution in Locally Recurrent Nasopharyngeal Carcinoma Using Knowledge-Based Planning.Technology in cancer research & treatmentArticle
- Three-dimensional Hierarchically Dense U-net Architecture for Knowledge-based Dose Prediction in Head and Neck Radiotherapy.Journal of medical physicsArticle
- A Comparative Study of Deep Learning Dose Prediction Models for Cervical Cancer Volumetric Modulated Arc Therapy.Technology in cancer research & treatmentArticle
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
Abstract
purposeIn this paper, we compare four novel knowledge-based planning (KBP) algorithms using deep learning to predict three-dimensional (3D) dose distributions of head and neck plans using the same patients' dataset and quantitative assessment metrics.
methodsA dataset of 340 oropharyngeal cancer patients treated with intensity-modulated radiation therapy was used in this study, which represents the AAPM OpenKBP - 2020 Grand Challenge dataset. Four 3D convolutional neural network architectures were built. The models were trained on 64% of the data set and validated on 16% for voxel-wise dose predictions: U-Net, attention U-Net, residual U-Net (Res U-Net), and attention Res U-Net. The trained models were then evaluated for their performance on a test data set (20% of the data) by comparing the predicted dose distributions against the ground-truth using dose statistics and dose-volume indices.
resultsThe four KBP dose prediction models exhibited promising performance with an averaged mean absolute dose error within the body contour <3 Gy on 68 plans in the test set. The average difference in predicting the D
conclusionAll models demonstrated almost comparable performance for voxel-wise dose prediction. KBP models that employ 3D U-Net architecture as a base could be deployed for clinical use to improve cancer patient treatment by creating plans with consistent quality and making the radiotherapy workflow more efficient.
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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.