ArticlePhysics and imaging in radiation oncology2023
Clinical implementation of deep learning-based automated left breast simultaneous integrated boost radiotherapy treatment planning.
Article in Physics and imaging in radiation oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Interpretable graph-conditioned CNNs for dose-volume histogram prediction in radiotherapy.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
- 3D Swin Transformer for patient-specific proton dose prediction of brain cancer patients.Acta oncologica (Stockholm, Sweden) · 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
- Evaluation and tuning of a commercial automated planning system for prostate radiotherapy.Physics and imaging in radiation oncology · 2025Article
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation 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 deep learning based dynamic arc radiotherapy photon dose engine trained on Monte Carlo dose distributions.Physics and imaging in radiation oncology · 2024Article
- Knowledge-based versus deep learning based treatment planning for breast radiotherapy.Physics and imaging in radiation oncology · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: Automation in radiotherapy treatment planning aims to improve both the quality and the efficiency of the process. The aim of this study was to report on a clinical implementation of a Deep Learning (DL) auto-planning model for left-sided breast cancer. Materials and methods: The DL model was developed for left-sided breast simultaneous integrated boost treatments under deep-inspiration breath-hold. Eighty manual dose distributions were revised and used for training. Ten patients were used for model validation. The model was then used to design 17 clinical auto-plans. Manual and auto-plans were scored on a list of clinical goals for both targets and organs-at-risk (OARs). For validation, predicted and mimicked dose (PD and MD, respectively) percent error (PE) was calculated with respect to manual dose. Clinical and validation cohorts were compared in terms of MD only. Results: Median values of both PD and MD validation plans fulfilled the evaluation criteria. PE was < 1% for targets for both PD and MD. PD was well aligned to manual dose while MD left lung mean dose was significantly less (median:5.1 Gy vs 6.1 Gy). The left-anterior-descending artery maximum dose was found out of requirements (median values:+5.9 Gy and + 2.9 Gy, for PD and MD respectively) in three validation cases, while it was reduced for clinical cases (median:-1.9 Gy). No other clinically significant differences were observed between clinical and validation cohorts. Conclusion: Small OAR differences observed during the model validation were not found clinically relevant. The clinical implementation outcomes confirmed the robustness of the model.
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