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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2601.05348 |
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| _version_ | 1866911362437873664 |
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| author | Zhao, Yao Tam, Ka Ho Douglas, Raphael Oh, Kyuhak Wang, Xin Subashi, Ergys Yang, Jinzhong Court, Laurence Rhee, Dong Joo |
| author_facet | Zhao, Yao Tam, Ka Ho Douglas, Raphael Oh, Kyuhak Wang, Xin Subashi, Ergys Yang, Jinzhong Court, Laurence Rhee, Dong Joo |
| contents | Conventional radiotherapy dose calculation algorithms are often computationally slow and non-differentiable, creating bottlenecks for online adaptive radiotherapy (ART) and limiting end-to-end automatic planning. Deep learning provides consistent inference performance and a differentiable framework essential for rapid optimization. In this study, we developed a generalized, site-independent deep learning dose engine using a beamlet-based input strategy. This establishes a computationally consistent and differentiable module that enables end-to-end training for autoplanning while maintaining accuracy across diverse geometries. A dataset of 3,600 plans from 120 patients across six anatomical sites was used to train two 3D convolutional neural networks, a standard U-Net and a Cascade U-Net, to predict 3D dose distributions from CT images and divergent MLC/jaw projections. Performance was validated via 3D gamma analysis on an independent cohort of 60 VMAT plans. The optimal model (U-Net with MAE loss) achieved a mean gamma passing rate of $98.9 \pm 1.6\%$ (3%/2mm, 10% threshold). Performance remained robust across all sites (passing rates $>98\%$), demonstrating that the beamlet-based strategy generalizes effectively to complex geometries without site-specific training. These results indicate that a single, site-independent model can calculate radiotherapy dose distributions with clinical accuracy. This differentiable engine is highly suitable for integration into end-to-end automatic planning, online ART, and secondary dose verification workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05348 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Feasibility of a General-Purpose Deep Learning Dose Engine: A Multi-Site Validation Study Zhao, Yao Tam, Ka Ho Douglas, Raphael Oh, Kyuhak Wang, Xin Subashi, Ergys Yang, Jinzhong Court, Laurence Rhee, Dong Joo Medical Physics Conventional radiotherapy dose calculation algorithms are often computationally slow and non-differentiable, creating bottlenecks for online adaptive radiotherapy (ART) and limiting end-to-end automatic planning. Deep learning provides consistent inference performance and a differentiable framework essential for rapid optimization. In this study, we developed a generalized, site-independent deep learning dose engine using a beamlet-based input strategy. This establishes a computationally consistent and differentiable module that enables end-to-end training for autoplanning while maintaining accuracy across diverse geometries. A dataset of 3,600 plans from 120 patients across six anatomical sites was used to train two 3D convolutional neural networks, a standard U-Net and a Cascade U-Net, to predict 3D dose distributions from CT images and divergent MLC/jaw projections. Performance was validated via 3D gamma analysis on an independent cohort of 60 VMAT plans. The optimal model (U-Net with MAE loss) achieved a mean gamma passing rate of $98.9 \pm 1.6\%$ (3%/2mm, 10% threshold). Performance remained robust across all sites (passing rates $>98\%$), demonstrating that the beamlet-based strategy generalizes effectively to complex geometries without site-specific training. These results indicate that a single, site-independent model can calculate radiotherapy dose distributions with clinical accuracy. This differentiable engine is highly suitable for integration into end-to-end automatic planning, online ART, and secondary dose verification workflows. |
| title | Feasibility of a General-Purpose Deep Learning Dose Engine: A Multi-Site Validation Study |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2601.05348 |