ArticleJournal of applied clinical medical physics2023
TrDosePred: A deep learning dose prediction algorithm based on transformers for head and neck cancer radiotherapy.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 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
- 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
- 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
- A dual-layer quality assurance approach leveraging dose prediction for efficient review of automated contours of organs at risk in the brain in radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Transformer-Integrated Hybrid Convolutional Neural Network for Dose Prediction in Nasopharyngeal Carcinoma Radiotherapy.Journal of imaging informatics in medicine · 2025Article
- High-dose-rate Brachytherapy Planning with Dendrite Cross-Attention UNet.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- An Efficient 3D Convolutional Neural Network for Dose Prediction in Cancer Radiotherapy from CT Images.Diagnostics (Basel, Switzerland) · 2025Article
- A new deep learning model for predicting IMRT dose distributions for lung cancer with dose masks.Frontiers in oncology · 2025Article
- Artificial intelligence in radiation therapy: from imaging to delivery-a comprehensive review.BJR artificial intelligence · 2025Review
- Enhancing stereotactic ablative boost radiotherapy dose prediction for bulky lung cancer: A multi-scale dilated network approach with scale-balanced structure loss.Journal of applied clinical medical physics · 2025Article
- Innovative patient-specific delivered-dose prediction for volumetric modulated arc therapy using lightweight Swin-Transformer.Frontiers in oncology · 2025Article
- Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.Medical physics · 2024Article
- Application and progress of artificial intelligence in radiation therapy dose prediction.Clinical and translational radiation oncology · 2024Review
- TrDosePred: A deep learning dose prediction algorithm based on transformers for head and neck cancer radiotherapy.Journal of applied clinical medical physics · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundIntensity-Modulated Radiation Therapy (IMRT) has been the standard of care for many types of tumors. However, treatment planning for IMRT is a time-consuming and labor-intensive process. PURPOSE: To alleviate this tedious planning process, a novel deep learning based dose prediction algorithm (TrDosePred) was developed for head and neck cancers.
methodsThe proposed TrDosePred, which generated the dose distribution from a contoured CT image, was a U-shape network constructed with a convolutional patch embedding and several local self-attention based transformers. Data augmentation and ensemble approach were used for further improvement. It was trained based on the dataset from Open Knowledge-Based Planning Challenge (OpenKBP). The performance of TrDosePred was evaluated with two mean absolute error (MAE) based scores utilized by OpenKBP challenge (i.e., Dose score and DVH score) and compared to the top three approaches of the challenge. In addition, several state-of-the-art methods were implemented and compared to TrDosePred.
resultsThe TrDosePred ensemble achieved the dose score of 2.426 Gy and the DVH score of 1.592 Gy on the test dataset, ranking at 3rd and 9th respectively in the leaderboard on CodaLab as of writing. In terms of DVH metrics, on average, the relative MAE against the clinical plans was 2.25% for targets and 2.17% for organs at risk.
conclusionsA transformer-based framework TrDosePred was developed for dose prediction. The results showed a comparable or superior performance as compared to the previous state-of-the-art approaches, demonstrating the potential of transformer to boost the treatment planning procedures.
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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.