Automatic Radiotherapy Treatment Planning with Deep Functional Reinforcement Learning

摘要

Intensity-modulated radiation therapy (IMRT) is a crucial radiotherapy technique, which is often formulated as an optimization problem. However, when the constraints are too restrictive to provide a feasible solution, human planners resort to relaxing the optimization parameters and re-evaluating the problem until an acceptable solution is obtained. However, this process is laborious and time-consuming, which has prompted attempts to automate radiotherapy through inverse planning studies using reinforcement learning. Unfortunately, these studies face two major limitations. First, a separate sub-network must be designed for each organ, rendering it difficult to apply to patients with an inconsistent number of structures. Second, the low signal-to-noise input and discrete action space result in low training efficiency. To address these issues, we propose an organ-sharing network that contains a functional embedding layer to extract curve features of the dose-volume histogram. It outputs continuous actions that can adjust the optimization parameters, thereby automating the radiotherapy planning process. The results from a cervical cancer dataset demonstrate the feasibility and efficiency of the proposed model in real-world radiotherapy. The code is available on https://github.com/Cissise/FatPIN.