  Target volume contouring for radiation therapy is considered significantly
more challenging than the normal organ segmentation tasks as it necessitates
the utilization of both image and text-based clinical information. Inspired by
the recent advancement of large language models (LLMs) that can facilitate the
integration of the textural information and images, here we present a novel
LLM-driven multimodal AI, namely LLMSeg, that utilizes the clinical text
information and is applicable to the challenging task of target volume
contouring for radiation therapy, and validate it within the context of breast
cancer radiation therapy target volume contouring. Using external validation
and data-insufficient environments, which attributes highly conducive to
real-world applications, we demonstrate that the proposed model exhibits
markedly improved performance compared to conventional unimodal AI models,
particularly exhibiting robust generalization performance and data efficiency.
To our best knowledge, this is the first LLM-driven multimodal AI model that
integrates the clinical text information into target volume delineation for
radiation oncology.
