Abstract
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 about 20 min. The average conformity index (CI) for Qwen-2.5-max plans, Llama-3.2 plans, and manual plans on the test set were 0.929 ± 0.007, 0.928 ± 0.007, and 0.926 ± 0.007, respectively. The average homogeneity index (HI) was 0.058 ± 0.006, 0.059 ± 0.005, and 0.065 ± 0.006, respectively. While there was a significant difference in target volume conformity between LLM plans and manual plans, OARs dose sparing showed no significant variations. In lateral comparisons among different LLMs, no statistically significant differences were observed in the PTV dose, OARs dose sparing, and target volume conformity between Qwen-2.5-max and Llama-3.2 plans. CONCLUSIONS: Through an assessment of LLM-generated plans and clinical plans in terms of target volume conformity and OARs dose sparing, this study provides preliminary evidence supporting the viability of LLMs for optimizing radiotherapy treatment plans. The implementation of LLMs demonstrates the potential for enhancing clinical workflows and reducing the workload associated with treatment planning.