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Purpose: To introduce the concept of using large language models (LLMs) tore-label structure names in accordance with the American Association ofPhysicists in Medicine (AAPM) Task Group (TG)-263 standard, and to establish abenchmark for future studies to reference.  Methods and Materials: The Generative Pre-trained Transformer (GPT)-4application programming interface (API) was implemented as a Digital Imagingand Communications in Medicine (DICOM) storage server, which upon receiving astructure set DICOM file, prompts GPT-4 to re-label the structure names of bothtarget volumes and normal tissues according to the AAPM TG-263. Three diseasesites, prostate, head and neck, and thorax were selected for evaluation. Foreach disease site category, 150 patients were randomly selected for manuallytuning the instructions prompt (in batches of 50) and 50 patients were randomlyselected for evaluation. Structure names that were considered were those thatwere most likely to be relevant for studies utilizing structure contours formany patients.  Results: The overall re-labeling accuracy of both target volumes and normaltissues for prostate, head and neck, and thorax cases was 96.0%, 98.5%, and96.9% respectively. Re-labeling of target volumes was less accurate on averageexcept for prostate - 100%, 93.1%, and 91.1% respectively.  Conclusions: Given the accuracy of GPT-4 in re-labeling structure names ofboth target volumes and normal tissues as presented in this work, LLMs arepoised to be the preferred method for standardizing structure names inradiation oncology, especially considering the rapid advancements in LLMcapabilities that are likely to continue.

Benchmarking a foundation LLM on its ability to re-label structure names in accordance with the AAPM TG-263 report