Transforming Multimodal Models into Action Models for Radiotherapy
This work addresses the need for faster, more standardized, and higher-quality radiotherapy treatment planning for cancer patients, though it is a proof-of-concept with incremental improvements over existing RL methods.
The paper tackled the problem of automating radiotherapy treatment planning, which is traditionally iterative and reliant on human expertise, by proposing a framework that transforms a multimodal foundation model into an action model using few-shot reinforcement learning, resulting in higher reward scores and more optimal dose distributions in simulations on prostate cancer data compared to conventional RL-based approaches.
Radiotherapy is a crucial cancer treatment that demands precise planning to balance tumor eradication and preservation of healthy tissue. Traditional treatment planning (TP) is iterative, time-consuming, and reliant on human expertise, which can potentially introduce variability and inefficiency. We propose a novel framework to transform a large multimodal foundation model (MLM) into an action model for TP using a few-shot reinforcement learning (RL) approach. Our method leverages the MLM's extensive pre-existing knowledge of physics, radiation, and anatomy, enhancing it through a few-shot learning process. This allows the model to iteratively improve treatment plans using a Monte Carlo simulator. Our results demonstrate that this method outperforms conventional RL-based approaches in both quality and efficiency, achieving higher reward scores and more optimal dose distributions in simulations on prostate cancer data. This proof-of-concept suggests a promising direction for integrating advanced AI models into clinical workflows, potentially enhancing the speed, quality, and standardization of radiotherapy treatment planning.