ArticleMedical physics2019
A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning.
Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 101 papers, 1 of them a synthesis that pooled it.
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Who cites it
101 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
- Interpretable graph-conditioned CNNs for dose-volume histogram prediction in radiotherapy.Medical physics · 2026Article
- Local large language model-guided optimization for prostate radiotherapy planning.Medical physics · 2026Article
- Gamma Knife treatment planning using knowledge-based reinforcement learning.Medical physics · 2026Article
- Optimizing Cardiac Dose Prediction in Left-Sided Breast Cancer Radiation Therapy: Clinical Strategies for Identifying Beneficiaries of the Deep Inspiration Breath-Hold Technique.Advances in radiation oncology · 2026Article
- Feasibility study of three-dimensional dose prediction of esophageal cancer radiotherapy based on CGAN.Journal of applied clinical medical physics · 2026Article
- Global spotlight editorial: Advancing treatment planning to promote equity through AI and automation in clinical medical physics.Journal of applied clinical medical physics · 2026Article
- Deep Reinforcement Learning-Based Automated Treatment Planning for a Rotating Gamma System.Cureus · 2026Article
- Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: Physics enhances generalizability.Medical physics · 2026Article
- Federated learning-driven intelligent framework for multi-center radiotherapy dose distribution prediction oriented toward linear accelerators.Scientific reports · 2026Article
- From data to treatment plan: An AI-driven path for automated breast radiotherapy planning.Journal of applied clinical medical physics · 2026Article
- Explainable deep learning and radiomics integration for differentiating insulinomas from NF-PNETs in EUS imaging.BMC medical informatics and decision making · 2026Article
- A tandem reinforcement learning framework for localized prostate cancer treatment planning and machine parameter optimization.Medical physics · 2026Article
- Towards human-centric intelligent treatment planning for radiation therapy.NPJ digital medicine · 2026Review
- 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
- Automatic radiotherapy planning for deliverable plans using deep learning dose prediction and dose rings optimization in cervical cancer.Journal of applied clinical medical physics · 2025Article
- Article
- The Role of Artificial Intelligence in Advancing Theranostics Dosimetry for Cancer Therapy: a Review.Nuclear medicine and molecular imaging · 2025Review
- Criteria-calibration approaches to deep learning-based cervical cancer radiation treatment auto-planning.Radiation oncology (London, England) · 2025Article
- A Decision-Making Method for Photon/Proton Selection for Nasopharyngeal Cancer Based on Dose Prediction and NTCP.Cancers · 2025Article
41 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
purposeTo develop a method for predicting optimal dose distributions, given the planning image and segmented anatomy, by applying deep learning techniques to a database of previously optimized and approved Intensity-modulated radiation therapy treatment plans.
methodsEighty cases of early-stage nasopharyngeal cancer (NPC) were included in the study. Seventy cases were chosen randomly as the training set and the remaining as the test set. The inputs were the images with structures, with each target and organs at risk (OARs) assigned a unique label. The outputs were dose maps, including coarse dose maps and converted fine dose maps (FDM) from convolution. Two types of input images with structures were used in the model building. One type of input included the images (with associated structures) without manipulation. The second type of input involved modifying the image gray label with information from radiation beam geometry. ResNet101 was chosen as the deep learning network for both. The accuracy of predicted dose distributions was evaluated against the corresponding dose as used in the clinic. A global three-dimensional gamma analysis was calculated for the evaluation.
resultsThe proposed model trained with the two different sets of input images and structures could both predict patient-specific dose distributions accurately. For the out-of-field dose distributions, the model obtained from the input with radiation geometry performed better (dose difference in %, 4.7 ± 6.1% vs 5.5 ± 7.9%, P < 0.05). The mean Gamma pass rates of dose distributions predicted with both types of input were comparable for most OARs (P > 0.05), except for the bilateral optic nerves and the optic chiasm.
conclusionsThe proposed system with radiation geometry added to the input is a promising method to generate patient-specific dose distributions for radiotherapy. It can be applied to obtain the dose distributions slice-by-slice for planning quality assurance and for guiding automated planning.
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