ArticleRadiation oncology (London, England)2021
Deep learning method for prediction of patient-specific dose distribution in breast cancer.
Article in Radiation oncology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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52 citing papers in PubMed, 1 synthesis or guideline pooled it, 91 citations in OpenAlex.
- Deep learning for head and neck radiation dose prediction: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Trial
- Volume ratio factor: a novel CT-derived biomarker for pre-planning ipsilateral lung dose risk stratification in breast cancer radiotherapy.Quantitative imaging in medicine and surgery · 2026Article
- AI-assisted VMAT planning incorporating deep learning-based dose prediction for head and neck cancer: feasibility of quality standardization and human intervention for irregular cases.Journal of applied clinical 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
- 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
- Contralateral breast cancer after radiotherapy and hormone therapy in two cohorts of US breast cancer survivors.British journal of cancer · 2026Article
- The evolution of surrogate microspheres in transarterial radioembolization: from SPECT prediction to multimodal AI-guided precision dosimetry.Theranostics · 2026Review
- Signal-aware deep learning-based respiratory motion prediction for lung tumor management.Frontiers in oncology · 2026Article
- Deep learning-based 3D dose reconstruction for intensity modulated radiation therapy using electronic portal imaging devices.Journal of applied clinical medical physics · 2025Article
- A multi-centre evaluation of deep learning based radiotherapy planning for left-sided node-negative breast cancer.Physics and imaging in radiation oncology · 2025Article
- Real-time volumetric CBCT reconstruction using surface and X-ray imaging for image-guided radiotherapy.Medical image analysis · 2025Article
- Decision support using machine learning for predicting adequate bladder filling in prostate radiotherapy: a feasibility study.Radiological physics and technology · 2025Article
- Early prediction of proton therapy dose distributions and DVHs for hepatocellular carcinoma using contour-based CNN models from diagnostic CT and MRI.Radiation oncology (London, England) · 2025Article
- Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy.ArXiv · 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
- A deep learning model to predict dose distributions for breast cancer radiotherapy.Discover oncology · 2025Article
- Estimation of heart dose in left breast cancer radiotherapy: Assessment of vDIBH feasibility using the supervised machine learning algorithm.Journal of applied clinical medical physics · 2025Article
- Improving Radiotherapy Plan Quality for Nasopharyngeal Carcinoma With Enhanced UNet Dose Prediction.Cancer medicine · 2025Article
- Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators.Journal of applied clinical medical physics · 2025Article
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11 authors at 1 institution in 1 country.
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Abstract
backgroundPatient-specific dose prediction improves the efficiency and quality of radiation treatment planning and reduces the time required to find the optimal plan. In this study, a patient-specific dose prediction model was developed for a left-sided breast clinical case using deep learning, and its performance was compared with that of conventional knowledge-based planning using RapidPlan™.
methodsPatient-specific dose prediction was performed using a contour image of the planning target volume (PTV) and organs at risk (OARs) with a U-net-based modified dose prediction neural network. A database of 50 volumetric modulated arc therapy (VMAT) plans for left-sided breast cancer patients was utilized to produce training and validation datasets. The dose prediction deep neural network (DpNet) feature weights of the previously learned convolution layers were applied to the test on a cohort of 10 test sets. With the same patient data set, dose prediction was performed for the 10 test sets after training in RapidPlan. The 3D dose distribution, absolute dose difference error, dose-volume histogram, 2D gamma index, and iso-dose dice similarity coefficient were used for quantitative evaluation of the dose prediction.
resultsThe mean absolute error (MAE) and one standard deviation (SD) between the clinical and deep learning dose prediction models were 0.02 ± 0.04%, 0.01 ± 0.83%, 0.16 ± 0.82%, 0.52 ± 0.97, - 0.88 ± 1.83%, - 1.16 ± 2.58%, and - 0.97 ± 1.73% for D
conclusionsIn this study, a deep learning method for dose prediction was developed and was demonstrated to accurately predict patient-specific doses for left-sided breast cancer. Using the deep learning framework, the efficiency and accuracy of the dose prediction were compared to those of RapidPlan. The doses predicted by deep learning were superior to the results of the RapidPlan-generated VMAT plan.
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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.