Demo: Generative AI helps Radiotherapy Planning with User Preference

Riqiang Gao, Simon Arberet, Martin Kraus, Han Liu, Wilko F.A.R. Verbakel, Dorin Comaniciu, Florin-Cristian Ghesu, Ali Kamen

Keywords: Radiotherapy Planning, Dose Prediction, User Preference, Generative AI

TL;DR: We are the first to demostrate flexible user-preferences in a dose prediction model and its value in practical radiotherapy planning pipeline.

Abstract: Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing deep learning approaches for 3D dose prediction rely on reference plans as ground truth during training, which can inadvertently bias models toward specific planning styles or institutional preferences. In this study, we introduce a novel generative model that predicts 3D dose distributions based solely on user-defined preference “flavors”. These customizable preferences enable planners to prioritize specific trade-offs between organs-at-risk (OARs) and planning target volumes (PTVs), offering greater flexibility and personalization. Designed for seamless integration with clinical treatment planning systems, our approach assists users in generating high-quality plans efficiently. Comparative evaluations demonstrate that our method surpasses the Varian RapidPlan$^\text{TM}$ model in both adaptability and plan quality. Demo video: https://huggingface.co/HappySubmit/DoseProposerDemo.

Submission Number: 19