Demo: Generative AI helps Radiotherapy Planning with User Preference
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911310753562624 |
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| author | Gao, Riqiang Arberet, Simon Kraus, Martin Liu, Han Verbakel, Wilko FAR Comaniciu, Dorin Ghesu, Florin-Cristian Kamen, Ali |
| author_facet | Gao, Riqiang Arberet, Simon Kraus, Martin Liu, Han Verbakel, Wilko FAR Comaniciu, Dorin Ghesu, Florin-Cristian Kamen, Ali |
| contents | Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing deep learning approaches for 3D dose prediction rely on reference plans as ground truth during training, which can inadvertently bias models toward specific planning styles or institutional preferences. In this study, we introduce a novel generative model that predicts 3D dose distributions based solely on user-defined preference flavors. These customizable preferences enable planners to prioritize specific trade-offs between organs-at-risk (OARs) and planning target volumes (PTVs), offering greater flexibility and personalization. Designed for seamless integration with clinical treatment planning systems, our approach assists users in generating high-quality plans efficiently. Comparative evaluations demonstrate that our method can surpasses the Varian RapidPlan model in both adaptability and plan quality in some scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08996 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Demo: Generative AI helps Radiotherapy Planning with User Preference Gao, Riqiang Arberet, Simon Kraus, Martin Liu, Han Verbakel, Wilko FAR Comaniciu, Dorin Ghesu, Florin-Cristian Kamen, Ali Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing deep learning approaches for 3D dose prediction rely on reference plans as ground truth during training, which can inadvertently bias models toward specific planning styles or institutional preferences. In this study, we introduce a novel generative model that predicts 3D dose distributions based solely on user-defined preference flavors. These customizable preferences enable planners to prioritize specific trade-offs between organs-at-risk (OARs) and planning target volumes (PTVs), offering greater flexibility and personalization. Designed for seamless integration with clinical treatment planning systems, our approach assists users in generating high-quality plans efficiently. Comparative evaluations demonstrate that our method can surpasses the Varian RapidPlan model in both adaptability and plan quality in some scenarios. |
| title | Demo: Generative AI helps Radiotherapy Planning with User Preference |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.08996 |