Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning
Ankit Bhattacharjee, Sougata Maity, Santam Chakraborty, Indranil Mallick
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.