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

arXiv: https://arxiv.org/abs/2608.26901 · HTML: https://mlsanity.com/paper/2608.26901/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Automated Treatment Planning in Radiation Therapy using Generative Adversarial Networks](https://mlsanity.com/paper/1807.06489/) (2018)
- [Physics-Guided Radiotherapy Treatment Planning with Deep Learning](https://mlsanity.com/paper/2506.19880/) (2025)

## 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.