Large-Language-Model Empowered Dose Volume Histogram Prediction for Intensity Modulated Radiotherapy
CoRR(2024)
摘要
Treatment planning is currently a patient specific, time-consuming, and
resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction
plays a critical role in automating this process. The geometric relationship
between DVHs in radiotherapy plans and organs-at-risk (OAR) and planning target
volume (PTV) has been well established. This study explores the potential of
deep learning models for predicting DVHs using images and subsequent human
intervention facilitated by a large-language model (LLM) to enhance the
planning quality. We propose a pipeline to convert unstructured images to a
structured graph consisting of image-patch nodes and dose nodes. A novel Dose
Graph Neural Network (DoseGNN) model is developed for predicting DVHs from the
structured graph. The proposed DoseGNN is enhanced with the LLM to encode
massive knowledge from prescriptions and interactive instructions from
clinicians. In this study, we introduced an online human-AI collaboration
(OHAC) system as a practical implementation of the concept proposed for the
automation of intensity-modulated radiotherapy (IMRT) planning. In comparison
to the widely-employed DL models used in radiotherapy, DoseGNN achieved mean
square errors that were 80%, 76% and 41.0% of those predicted by Swin
U-Net Transformer, 3D U-Net CNN and vanilla MLP, respectively. Moreover, the
LLM-empowered DoseGNN model facilitates seamless adjustment to treatment plans
through interaction with clinicians using natural language.
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