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This study introduces Dose-PlanNet, a physics-guided 3D deep learning architecture that automates the prediction of dose distributions in prostate radiotherapy, particularly for complex hypofractionated regimens. The model was validated against patient data from a prospective trial, achieving comparable target coverage while significantly improving high-dose organ-at-risk sparing. Despite a slight reduction in target homogeneity, the automated plans met clinical acceptance criteria in the majority of cases, demonstrating the potential of physics-informed deep learning to enhance radiotherapy workflows without compromising dosimetric quality.
Physics-informed deep learning can automate complex radiotherapy planning while achieving superior organ-at-risk sparing.
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.
