ArticleJournal of applied clinical medical physics2024
Deep-neural network approaches for predicting 3D dose distribution in intensity-modulated radiotherapy of the brain tumors.
Article in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Radiotherapy for High-Grade Gliomas in Adults and Children: A Systematic Review of Advances Published in the Second Half of 2023.International journal of molecular sciences · 2026Pooled it
- Assessment of rotational dose perturbations in SBRT: A radiomic-dosiomic predictive model based on the structural similarity index measure.Journal of applied clinical medical physics · 2026Article
- 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
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Nested CNN architecture for three-dimensional dose distribution prediction in tomotherapy for prostate cancer.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- RadDeploy: A framework for integrating in-house developed software and artificial intelligence models seamlessly into radiotherapy workflows.Physics and imaging in radiation oncology · 2024Article
- Deep-neural network approaches for predicting 3D dose distribution in intensity-modulated radiotherapy of the brain tumors.Journal of applied clinical medical physics · 2024Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of this study is to reduce treatment planning time by predicting the intensity-modulated radiotherapy 3D dose distribution using deep learning for brain cancer patients. "For this purpose, two different approaches in dose prediction, i.e., first only planning target volume (PTV) and second PTV with organs at risk (OARs) as input of the U-net model, are employed and their results are compared." METHODS AND MATERIALS: The data of 99 patients with glioma tumors referred for IMRT treatment were used so that the images of 90 patients were regarded as training datasets and the others were for the test. All patients were manually planned and treated with sixth-field IMRT; the photon energy was 6MV. The treatment plans were done with the Collapsed Cone Convolution algorithm to deliver 60 Gy in 30 fractions.
resultsThe obtained accuracy and similarity for the proposed methods in dose prediction when compared to the clinical dose distributions on test patients according to MSE, dice metric and SSIM for the Only-PTV and PTV-OARs methods are on average (0.05, 0.851, 0.83) and (0.056, 0.842, 0.82) respectively. Also, dose prediction is done in an extremely short time.
conclusionThe same results of the two proposed methods prove that the presence of OARs in addition to PTV does not provide new knowledge to the network and only by defining the PTV and its location in the imaging slices, does the dose distribution become predictable. Therefore, the Only-PTV method by eliminating the process of introducing OARs can reduce the overall designing time of treatment by IMRT in patients with glioma tumors.
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