ArticlePhysics and imaging in radiation oncology2025
A multi-centre evaluation of deep learning based radiotherapy planning for left-sided node-negative breast cancer.
Article in Physics and imaging in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Risk stratification and cross-center prediction of thoracoabdominal 6D setup errors.BMC cancer · 2026Article
- Automated deep learning segmentation and planning for left-sided breast radiotherapy with minimised adaptations based on dose, TCP and NTCP criteria.Physics and imaging in radiation oncology · 2026Article
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13 authors.
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Abstract
Background and Purpose: Deep learning based planning (DLP) has the potential to improve consistency and efficiency in radiotherapy treatment planning. However, its clinical applicability remains limited, partly due to the need to translate a predicted dose into a deliverable dose. This study evaluated the generalisability of an institution specific DLP solution across multiple institutions by assessing its performance and developing a standardised translation parameter set. Materials and Methods: Four institutions provided clinical treatment plans of 15 patients with left-sided node-negative breast cancer. Treatment plans delivering 40.05 Gy were generated using a deep learning prediction model trained on data from one institution. External validation was performed using national consensus criteria, by applying the initial parameter settings (InitialMimick) to datasets (n = 45) from three other institutions. A standardised parameter set (GenericMimick) was then developed based on data (n = 12) from all four institutions, whereafter it was evaluated on the remaining 48 patients of the dataset. Results: InitialMimick plans showed higher average dose values in the planning target volume for the D Conclusion: DLP with a standardised translation parameter set demonstrated general applicability across multiple institutions.
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