Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning

• URL: http://arxiv.org/abs/2608.26901v1
• Date: Thu, 27 Aug 2026 10:05:01 GMT
• Title: Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning
• Abstract summary: Dose-PlanNet is a physics-guided 3D deep learning architecture designed to predict dose distributions.<n>This pipeline demonstrates that physics-informed deep learning can accelerate radiotherapy while safely maintaining the stringent dosimetric quality required for high-precision clinical deployment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 ($D_{95}$), 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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