ArticleScientific reports2019
A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 147 papers.
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Who cites it
147 citing papers in PubMed.
- Neural network dose prediction for rectal spacer stratification in dose-escalated prostate radiotherapy.Medical physics · 2022Trial
- Autonomous radiotherapy planning via agentic orchestration using a multimodal TPS-integrated compound AI platform.Machine learning. Health · 2026Article
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- Deep learning-based dose prediction for stereotactic prostate cancer radiotherapy with CyberKnife.Journal of applied clinical medical physics · 2026Article
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Reference-Free large language model agents for physician-guided radiotherapy treatment planning.Medical physics · 2026Article
- Development and Clinical Validation of a Protocol-Agnostic Machine Learning Platform for Automated Treatment Planning in External Beam Radiation Therapy.Advances in radiation oncology · 2026Article
- Gamma Knife treatment planning using knowledge-based reinforcement learning.Medical physics · 2026Article
- Physics-informed neural network with adaptive loss balancing for real-time radiotherapy dose prediction and verification.Scientific reports · 2026Article
- Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: Physics enhances generalizability.Medical physics · 2026Article
- A comparative assessment of deep learning and knowledge-based dose prediction models for advanced radiotherapy planning of prostate cancer with focal boosting.Physics and imaging in radiation oncology · 2026Article
- Deep Reinforcement Learning-Based Automated Treatment Planning for a Rotating Gamma System.Cureus · 2026Article
- Prescription‑dose stratification improves deep learning‑based VMAT dose prediction in locally advanced NSCLC.Scientific reports · 2026Article
- From data to treatment plan: An AI-driven path for automated breast radiotherapy planning.Journal of applied clinical medical physics · 2026Article
- Towards human-centric intelligent treatment planning for radiation therapy.NPJ digital medicine · 2026Review
- Adaptive radiotherapy dose prediction on head and neck cancer patients with a 3D multi-headed U-Net deep learning architecture.Machine Learning. Health · 2025Article
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- 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
- A few-shot u-net learning framework for fast and accurate three-dimensional dose prediction in radiotherapy.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2025Article
87 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able to accurately predict the dose of intensity-modulated radiation therapy (IMRT) for prostate cancer patients, where the average Dice similarity coefficient is 0.91 when comparing the predicted vs. true isodose volumes between 0% and 100% of the prescription dose. The average value of the absolute differences in [max, mean] dose is found to be under 5% of the prescription dose, specifically for each structure is [1.80%, 1.03%](PTV), [1.94%, 4.22%](Bladder), [1.80%, 0.48%](Body), [3.87%, 1.79%](L Femoral Head), [5.07%, 2.55%](R Femoral Head), and [1.26%, 1.62%](Rectum) of the prescription dose. We thus managed to map a desired radiation dose distribution from a patient's PTV and OAR contours. As an additional advantage, relatively little data was used in the techniques and models described in this paper.
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