ArticleJournal of applied clinical medical physics2020
A method of using deep learning to predict three-dimensional dose distributions for intensity-modulated radiotherapy of rectal cancer.
Article in Journal of applied clinical medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed, 59 citations in OpenAlex.
- Gamma Knife treatment planning using knowledge-based reinforcement learning.Medical physics · 2026Article
- Explicit modeling of beam geometry improves three-dimensional dose prediction for esophageal cancer radiotherapy under heterogeneous beam configurations.Radiation oncology (London, England) · 2026Article
- Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: Physics enhances generalizability.Medical physics · 2026Article
- A universal framework for IMRT dose prediction.Medical physics · 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
- Fully automated intensity-modulated radiotherapy plans for rectal cancer based on deep learning predictions of three-dimensional dose distributions.Quantitative imaging in medicine and surgery · 2025Article
- Total brain dose estimation in single-isocenter-multiple-targets (SIMT) radiosurgery via a novel deep neural network with spherical convolutions.Medical physics · 2025Article
- Transformer-Integrated Hybrid Convolutional Neural Network for Dose Prediction in Nasopharyngeal Carcinoma Radiotherapy.Journal of imaging informatics in medicine · 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 deep learning model to predict dose distributions for breast cancer radiotherapy.Discover oncology · 2025Article
- Dose prediction for cervical cancer in radiotherapy based on the beam channel generative adversarial network.Heliyon · 2024Article
- Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction.Journal of imaging informatics in medicine · 2024Article
- Article
- Deep-learning-driven dose prediction and verification for stereotactic radiosurgical treatment of isolated brain metastases.Frontiers in oncology · 2023Article
- Dose prediction for cervical cancer VMAT patients with a full-scale 3D-cGAN-based model and the comparison of different input data on the prediction results.Radiation oncology (London, England) · 2022Article
- Combining dense elements with attention mechanisms for 3D radiotherapy dose prediction on head and neck cancers.Journal of applied clinical medical physics · 2022Article
- A Survey on Deep Learning for Precision Oncology.Diagnostics (Basel, Switzerland) · 2022Review
- Robust automated radiation therapy treatment planning using scenario-specific dose prediction and robust dose mimicking.Medical physics · 2022Article
- The feasibility study on the generalization of deep learning dose prediction model for volumetric modulated arc therapy of cervical cancer.Journal of applied clinical medical physics · 2022Article
- A feasibility study for in vivo treatment verification of IMRT using Monte Carlo dose calculation and deep learning-based modelling of EPID detector response.Radiation oncology (London, England) · 2022Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeTo develop and test a three-dimensional (3D) deep learning model for predicting 3D voxel-wise dose distributions for intensity-modulated radiotherapy (IMRT).
methodsA total of 122 postoperative rectal cancer cases treated by IMRT were considered in the study, of which 100 cases were randomly selected as the training-validating set and the remaining as the testing set. A 3D deep learning model named 3D U-Res-Net_B was constructed to predict 3D dose distributions. Eight types of 3D matrices from CT images, contoured structures, and beam configurations were fed into the independent input channel, respectively, and the 3D matrix of dose distributions was taken as the output to train the 3D model. The obtained 3D model was used to predict new 3D dose distributions. The predicted accuracy was evaluated in two aspects: (a) The dice similarity coefficients (DSCs) of different isodose volumes, the average dose difference of all voxels within the body, and 3%/5 mm global gamma passing rates of organs at risks (OARs) and planned target volume (PTV) were used to address the spatial correspondence between predicted and clinical delivered 3D dose distributions; (b) The dosimetric index (DI) including homogeneity index, conformity index, V
resultsThe 3D U-Res-Net_B model predicted 3D dose distributions accurately. For the 22 testing cases, the average prediction bias ranged from -1.94% to 1.58%, and the overall mean absolute errors (MAEs) was 3.92 ± 4.16%; there was no statistically significant difference for nearly all DIs. The model had a DSCs value above 0.9 for most isodose volumes, and global 3D gamma passing rates varying from 0.81 to 0.90 for PTV and OARs, clearly outperforming 3D U-Res-Net_O and being slightly superior to 3D U-Net.
conclusionsThis study developed a more general deep learning model by considering beam configurations input and achieved an accurate 3D voxel-wise dose prediction for rectal cancer treated by IMRT, a potentially easier clinical implementation for more comprehensive automatic planning.
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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.