Automated radiotherapy treatment planning guided by GPT-4Vision
<|reference_start|>Automated radiotherapy treatment planning guided by GPT-4Vision: Radiotherapy treatment planning is a time-consuming and potentially subjective process that requires the iterative adjustment of model parameters to balance multiple conflicting objectives. Recent advancements in large foundation models offer promising avenues for addressing the challenges in planning and clinical decision-making. This study introduces GPT-RadPlan, a fully automated treatment planning framework that harnesses prior radiation oncology knowledge encoded in multi-modal large language models, such as GPT-4Vision (GPT-4V) from OpenAI. GPT-RadPlan is made aware of planning protocols as context and acts as an expert human planner, capable of guiding a treatment planning process. Via in-context learning, we incorporate clinical protocols for various disease sites as prompts to enable GPT-4V to acquire treatment planning domain knowledge. The resulting GPT-RadPlan agent is integrated into our in-house inverse treatment planning system through an API. The efficacy of the automated planning system is showcased using multiple prostate and head & neck cancer cases, where we compared GPT-RadPlan results to clinical plans. In all cases, GPT-RadPlan either outperformed or matched the clinical plans, demonstrating superior target coverage and organ-at-risk sparing. Consistently satisfying the dosimetric objectives in the clinical protocol, GPT-RadPlan represents the first multimodal large language model agent that mimics the behaviors of human planners in radiation oncology clinics, achieving remarkable results in automating the treatment planning process without the need for additional training.<|reference_end|>
@article{liu2024automated,
    title={Automated radiotherapy treatment planning guided by GPT-4Vision},
    author={Sheng Liu, Oscar Pastor-Serrano, Yizheng Chen, Matthew Gopaulchan,
  Weixing Liang, Mark Buyyounouski, Erqi Pollom, Quynh-Thu Le, Michael
  Gensheimer, Peng Dong, Yong Yang, James Zou, Lei Xing},
    journal={arXiv preprint arXiv:2406.15609},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15609},
    primaryClass={physics.med-ph cs.AI}
}