ArticlePractical radiation oncology
Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans.
Article in Practical radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Autonomous radiotherapy planning via agentic orchestration using a multimodal TPS-integrated compound AI platform.Machine learning. Health · 2026Article
- An interactive virtual dosimetrist for adjusting radiotherapy dose quality.Physics and imaging in radiation oncology · 2026Article
- Comparison of DVH-based machine learning and 3D convolutional neural network approaches for automated VMAT planning in head and neck cancer.Radiological physics and technology · 2026Article
- End-to-end deep learning pipeline for automated IMRT planning in lymphoma: Feasibility and limitations.Journal of applied clinical medical physics · 2026Article
- Nanomaterials reshaping cancer radiotherapy: Radiosensitization mechanisms, delivery and theranostic platforms, multimodal synergy, and clinical translation strategies.Materials today. Bio · 2026Review
- Artificial Intelligence for Prognostic Modelling and Adaptive Treatment Monitoring in Radiation Oncology.Cureus · 2026Review
- Toward universal dose prediction: A multi-scale, multi-objective framework for sequential boost radiotherapy.Medical physics · 2026Article
- Physics-informed neural network with adaptive loss balancing for real-time radiotherapy dose prediction and verification.Scientific reports · 2026Article
- Review
- Parallel/Opposed: Automated treatment planning systems should be designed to minimize human intervention.Journal of applied clinical medical physics · 2026Article
- Federated learning-driven intelligent framework for multi-center radiotherapy dose distribution prediction oriented toward linear accelerators.Scientific reports · 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
- 3D Swin Transformer for patient-specific proton dose prediction of brain cancer patients.Acta oncologica (Stockholm, Sweden) · 2025Article
- Intentional creation of suboptimal, realistic dose distributions.Journal of applied clinical medical physics · 2025Article
- Article
- A comprehensive multifaceted technical evaluation framework for implementation of auto-segmentation models in radiotherapy.Communications medicine · 2025Article
- Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy.ArXiv · 2025Article
- An isodose-constrained automatic treatment planning strategy using a multicriteria predicted dose rating.Medical physics · 2025Article
- A physics-informed deep learning model for predicting beam dose distribution of intensity-modulated radiation therapy treatment plans.Physics and imaging in radiation oncology · 2025Article
Corrections and comments
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Authors and funding
23 authors at 4 institutions in 1 country.
Funding
Abstract
purposeThis study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. METHODS AND MATERIALS: A total of 245 volumetric modulated arc therapy HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included computed tomography images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared with manual flags by 3 HN radiation oncologists.
resultsThe predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D
conclusionsDeep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality.
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Registered trials
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.