Feasibility study of automatic radiotherapy treatment planning for cervical cancer using a large language model

作者：Shuoyang Wei, Ankang Hu, Yongguang Liang, Jingru Yang, Lang Yu, Wenbo Li, Bo Yang, Jie Qiu · 发表于：Radiation Oncology · 年份：2025 · DOI：10.1186/s13014-025-02660-5 · 被引用次数：10 · 研究领域：Advanced Radiotherapy Techniques、Endometrial and Cervical Cancer Treatments、Advances in Oncology and Radiotherapy

BACKGROUND: Radiotherapy treatment planning traditionally involves complex and time-consuming processes, often relying on trial-and-error methods. The emergence of artificial intelligence, particularly Large Language Models (LLMs), surpassing human capabilities and existing algorithms in various domains, presents an opportunity to automate and enhance this optimization process. PURPOSE: This study seeks to evaluate the capacity of LLMs to generate radiotherapy treatment plans comparable to those crafted by human medical physicists, focusing on target volume conformity and organs-at-risk (OARs) dose sparing. The goal is to automate the optimization process of radiotherapy treatment plans through the utilization of LLMs. METHODS: Multiple LLMs were employed to adjust optimization parameters for radiotherapy treatment plans, using a dataset comprising 35 cervical cancer patients treated with volumetric modulated arc therapy (VMAT). Customized prompts were applied to 5 patients to tailor the LLMs, which were subsequently tested on 30 patients. Evaluation metrics included target volume conformity, dose homogeneity, monitor units (MU) value, and OARs dose sparing, comparing plans generated by various LLMs to manual plans. RESULTS: With the exception of Gemini-1.5-flash, which faced challenges due to hallucinations, Qwen-2.5-max and Llama-3.2 produced acceptable VMAT plans in 16.3 ± 5.0 and 9.8 ± 2.1 min, respectively, outperforming an experienced human physicist's time cost of abou...