ArticlePhysics and imaging in radiation oncology2021
Artificial intelligence based treatment planning of radiotherapy for locally advanced breast cancer.
Article in Physics and imaging in radiation oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Objective-function-guided automated VMAT planning reduces OAR dose, low-dose exposure, and inter-planner variability in breast radiotherapy.Scientific reports · 2026Article
- AI and Big Data in Oncology: A Physician-Centered Perspective on Emerging Clinical and Research Applications.Cancer innovation · 2026Review
- Evaluation and tuning of a commercial automated planning system for prostate radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Application of artificial intelligence in medical imaging for tumor diagnosis and treatment: a comprehensive approach.Discover oncology · 2025Review
- BreastWatch: A Varian Eclipse script tool for Community-Based automatic evaluation of breast treatment plans.Journal of applied clinical medical physics · 2025Article
- A comparative analysis of deep learning architectures with data augmentation and multichannel input for locoregional breast cancer radiotherapy.Journal of applied clinical medical physics · 2025Article
- Automated plan generation for prostate radiotherapy patients using deep learning and scripted optimization.Physics and imaging in radiation oncology · 2024Article
- Improving 3D dose prediction for breast radiotherapy using novel glowing masks and gradient-weighted loss functions.Medical physics · 2024Article
- A deep learning based dynamic arc radiotherapy photon dose engine trained on Monte Carlo dose distributions.Physics and imaging in radiation oncology · 2024Article
- How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications.Bioengineering (Basel, Switzerland) · 2023Review
- Artificial intelligence in breast cancer: application and future perspectives.Journal of cancer research and clinical oncology · 2023Review
- Fully automated volumetric modulated arc therapy technique for radiation therapy of locally advanced breast cancer.Radiation oncology (London, England) · 2023Article
- Clinical implementation of deep learning-based automated left breast simultaneous integrated boost radiotherapy treatment planning.Physics and imaging in radiation oncology · 2023Article
- Clinical evaluation of a deep learning segmentation model including manual adjustments afterwards for locally advanced breast cancer.Technical innovations & patient support in radiation oncology · 2023Article
- Prospective deployment of an automated implementation solution for artificial intelligence translation to clinical radiation oncology.Frontiers in oncology · 2023Article
- Deep Learning-Based Artificial Intelligence to Investigate Targeted Nanoparticles' Uptake in TNBC Cells.International journal of molecular sciences · 2022Article
- Towards real-time radiotherapy planning: The role of autonomous treatment strategies.Physics and imaging in radiation oncology · 2022Article
- Five years, 20 volumes and 300 publications ofPhysics and imaging in radiation oncology · 2022Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeTreatment planning of radiotherapy for locally advanced breast cancer patients can be a time consuming process. Artificial intelligence based treatment planning could be used as a tool to speed up this process and maintain plan quality consistency. The purpose of this study was to create treatment plans for locally advanced breast cancer patients using a Convolutional Neural Network (CNN). MATERIALS AND
methodsData of 60 patients treated for left-sided breast cancer was used with a training, validation and test split of 36/12/12, respectively. The in-house built CNN model was a hierarchically densely connected U-net (HD U-net). The inputs for the HD U-net were 2D distance maps of the relevant regions of interest. Dose predictions, generated by the HD U-net, were used for a mimicking algorithm in order to create clinically deliverable plans.
resultsDose predictions were generated by the HD U-net and mimicked using a commercial treatment planning system. The predicted plans fulfilling all clinical goals while showing small (≤0.5 Gy) statistically significant differences (p < 0.05) in the doses compared to the manual plans. The mimicked plans show statistically significant differences in the average doses for the heart and lung of ≤0.5 Gy and a reduced D2% of all PTVs. In total, ten of the twelve mimicked plans were clinically acceptable.
conclusionsWe created a CNN model which can generate clinically acceptable plans for left-sided locally advanced breast cancer patients. This model shows great potential to speed up the treatment planning process while maintaining consistent plan quality.
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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 Socratic graph.