ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2020
Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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53 citing papers in PubMed, 94 citations in OpenAlex.
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- Artificial intelligence in radiation treatment planning: a narrative review from automation to clinical decision support.Translational cancer research · 2026Review
- 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-Learning-Based 3D Dose Distribution Prediction for VMAT Lung Cancer Treatment Using an Enhanced UNet3D Architecture with Composite Loss Functions.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence applications in adaptive radiotherapy-a narrative review.Translational cancer research · 2026Review
- Evaluation of deliverable dose-mimicking automated volumetric arc radiation therapy planning for stage III non-small cell lung cancer patients: comparison with a commercial DVH-predicted automated planning system.Journal of radiation research · 2026Article
- Developing an automatic decision-assistance tool to choose proton/photon radiotherapy for patients with prostate cancer.Journal of applied clinical medical physics · 2026Article
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- 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
- The dose-volume histogram-based evaluation of predictors for gross hematuria after postprostatectomy radiotherapy.Prostate international · 2025Article
- Next-Generation Advances in Prostate Cancer Imaging and Artificial Intelligence Applications.Journal of imaging · 2025Review
- 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
- A Preliminary Study on Deep Learning-Based Plan Quality Prediction in Gamma Knife Radiosurgery for Brain Metastases.Cancers · 2025Article
- Identifying drug targets and evaluating KLK3-targeted inhibitors for prostate cancer using in-silico and in-vitro approaches.Medical oncology (Northwood, London, England) · 2025Article
- Actor critic with experience replay-based automatic treatment planning for prostate cancer intensity modulated radiotherapy.Medical physics · 2025Article
- Transformer-Integrated Hybrid Convolutional Neural Network for Dose Prediction in Nasopharyngeal Carcinoma Radiotherapy.Journal of imaging informatics in medicine · 2025Article
- An isodose-constrained automatic treatment planning strategy using a multicriteria predicted dose rating.Medical physics · 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
- Risk Estimation of Late Rectal Toxicity Using a Convolutional Neural Network-based Dose Prediction in Prostate Cancer Radiation Therapy.Advances in radiation oncology · 2025Article
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Authors and funding
8 authors at 6 institutions in 2 countries.
Funding
Abstract
purposeThis work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adapt the model, via transfer learning with minimal input data, to three different internal treatment planning styles and one external institution planning style.
methodsWe built the source model with planning data from 108 patients previously treated with VMAT for prostate cancer. For the transfer learning, we selected patient cases planned with three different styles, 14-29 cases per style, in the same institution and 20 cases treated in a different institution to adapt the source model to four target models in total. We compared the dose distributions predicted by the source model and the target models with the corresponding clinical plan dose used for patient treatments and quantified the improvement in the prediction quality for the target models over the source model using the Dice similarity coefficients (DSC) of 0% to 100% isodose volumes and the dose-volume-histogram (DVH) parameters of the planning target volume and the organs-at-risk.
resultsThe source model accurately predicts dose distributions for plans generated in the same source style, but performs sub-optimally for the three different internal and one external target styles, with the mean DSC ranging between 0.81-0.94 and 0.82-0.91 for the internal and the external styles, respectively. With transfer learning, the target model predictions improved the mean DSC to 0.88-0.95 and 0.92-0.96 for the internal and the external styles, respectively. Target model predictions significantly improved the accuracy of the DVH parameter predictions to within 1.6%.
conclusionWe demonstrated the problem of model generalizability for DL-based dose prediction and the feasibility of using transfer learning to solve this problem. With 14-29 cases per style, we successfully adapted the source model into several different practice styles. This indicates a realistic way forward to widespread clinical implementation of DL-based dose prediction.
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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.