ArticleMedical physics2021
A feasibility study on deep learning-based individualized 3D dose distribution prediction.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Fast 3D whole-body occupational dose estimation in interventional radiology using physics-informed deep learning.Radiological physics and technology · 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
- Deep learning-based 3D dose reconstruction for intensity modulated radiation therapy using electronic portal imaging devices.Journal of applied clinical medical physics · 2025Article
- Towards quantitative ionizing radiation acoustic imaging (iRAI) for radiation dose measurement: Validation from simulations to experiments.Medical physics · 2025Article
- Cone-Beam CT to CT Image Translation Using a Transformer-Based Deep Learning Model for Prostate Cancer Adaptive Radiotherapy.Journal of imaging informatics in medicine · 2025Article
- Risk Estimation of Late Rectal Toxicity Using a Convolutional Neural Network-based Dose Prediction in Prostate Cancer Radiation Therapy.Advances in radiation oncology · 2025Article
- A deep learning model to predict dose distributions for breast cancer radiotherapy.Discover oncology · 2025Article
- A new deep learning model for predicting IMRT dose distributions for lung cancer with dose masks.Frontiers in oncology · 2025Article
- Novel in-house knowledge-based automated planning system for lung cancer treated with intensity-modulated radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2024Article
- Application and progress of artificial intelligence in radiation therapy dose prediction.Clinical and translational radiation oncology · 2024Review
- Exploring the impact of network depth on 3D U-Net-based dose prediction for cervical cancer radiotherapy.Frontiers in oncology · 2024Article
- PRT-Net: a progressive refinement transformer for dose prediction to guide ovarian transposition.Frontiers in oncology · 2024Article
- Advances in Automated Treatment Planning.Seminars in radiation oncology · 2022Review
- A Survey on Deep Learning for Precision Oncology.Diagnostics (Basel, Switzerland) · 2022Review
- Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy.Frontiers in oncology · 2021Article
- Three-dimensional Hierarchically Dense U-net Architecture for Knowledge-based Dose Prediction in Head and Neck Radiotherapy.Journal of medical physicsArticle
- Deep Learning-based Volumetric Modulated Arc Therapy Dose Prediction with Uncertainty Quantification for Left- and Right-sided Breast Cancer: A Single-institution Feasibility Study.Journal of medical physicsArticle
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8 authors.
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Abstract
purposeRadiation therapy treatment planning is a trial-and-error, often time-consuming process. An approximately optimal dose distribution corresponding to a specific patient's anatomy can be predicted by using pre-trained deep learning (DL) models. However, dose distributions are often optimized based not only on patient-specific anatomy but also on physicians' preferred trade-offs between planning target volume (PTV) coverage and organ at risk (OAR) sparing or among different OARs. Therefore, it is desirable to allow physicians to fine-tune the dose distribution predicted based on patient anatomy. In this work, we developed a DL model to predict the individualized 3D dose distributions by using not only the patient's anatomy but also the desired PTV/OAR trade-offs, as represented by a dose volume histogram (DVH), as inputs.
methodsIn this work, we developed a modified U-Net network to predict the 3D dose distribution by using patient PTV/OAR masks and the desired DVH as inputs. The desired DVH, fine-tuned by physicians from the initially predicted DVH, is first projected onto the Pareto surface, then converted into a vector, and then concatenated with feature maps encoded from the PTV/OAR masks. The network output for training is the dose distribution corresponding to the Pareto optimal DVH. The training/validation datasets contain 77 prostate cancer patients, and the testing dataset has 20 patients.
resultsThe trained model can predict a 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the input desired DVH. We calculated the difference between the predicted dose distribution and the optimized dose distribution that has a DVH closest to the desired one for the PTV and for all OARs as a quantitative evaluation. The largest absolute error in mean dose was about 3.6% of the prescription dose, and the largest absolute error in the maximum dose was about 2.0% of the prescription dose.
conclusionsIn this feasibility study, we have developed a 3D U-Net model with the patient's anatomy and the desired DVH curves as inputs to predict an individualized 3D dose distribution that is approximately Pareto optimal while having the DVH closest to the desired one. The predicted dose distributions can be used as references for dosimetrists and physicians to rapidly develop a clinically acceptable treatment plan.
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