Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning
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Abstract (arXiv)
Automating prostate radiotherapy treatment planning is dosimetrically complex, particularly for extreme hypofractionated regimens. In this study, we introduce Dose-PlanNet, a physics-guided 3D deep learning architecture designed to predict dose distributions. This model's performance was evaluated on a cohort of patients treated in a prospective trial where two different dose fractionation regimens were employed. Dose-PlanNet achieved comparable target coverage (D95), though statistical analysis revealed a marginal reduction in target homogeneity (p<0.001) offset. However the model achieved statistically significant improvements in high-dose organ-at-risk sparing (p<0.001). When evaluated against strict Prospective Randomized protocol volumetric constraints, automated plans met prespecified clinical acceptance criteria in 11 out of 14 Moderate Hypofraction Arm plans and 9 out of 12 Stereotactic Body Radiation Therapy Arm plans. This pipeline demonstrates that physics-informed deep learning can accelerate radiotherapy workflows while safely maintaining the stringent dosimetric quality required for high-precision clinical deployment.
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References · 5
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,656
- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,475
- 2022MONAI: An open-source framework for deep learning in healthcarecited by 443
- 2018Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networkscited by 11
- 2025Physics-Guided Radiotherapy Treatment Planning with Deep Learningcited by 1
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