Large Language Model-Augmented Auto-Delineation of Treatment Target Volume in Radiation Therapy
<|reference_start|>Large Language Model-Augmented Auto-Delineation of Treatment Target Volume in Radiation Therapy: Radiation therapy (RT) is one of the most effective treatments for cancer, and its success relies on the accurate delineation of targets. However, target delineation is a comprehensive medical decision that currently relies purely on manual processes by human experts. Manual delineation is time-consuming, laborious, and subject to interobserver variations. Although the advancements in artificial intelligence (AI) techniques have significantly enhanced the auto-contouring of normal tissues, accurate delineation of RT target volumes remains a challenge. In this study, we propose a visual language model-based RT target volume auto-delineation network termed Radformer. The Radformer utilizes a hierarichal vision transformer as the backbone and incorporates large language models to extract text-rich features from clinical data. We introduce a visual language attention module (VLAM) for integrating visual and linguistic features for language-aware visual encoding (LAVE). The Radformer has been evaluated on a dataset comprising 2985 patients with head-and-neck cancer who underwent RT. Metrics, including the Dice similarity coefficient (DSC), intersection over union (IOU), and 95th percentile Hausdorff distance (HD95), were used to evaluate the performance of the model quantitatively. Our results demonstrate that the Radformer has superior segmentation performance compared to other state-of-the-art models, validating its potential for adoption in RT practice.<|reference_end|>
@article{rajendran2024large,
    title={Large Language Model-Augmented Auto-Delineation of Treatment Target
  Volume in Radiation Therapy},
    author={Praveenbalaji Rajendran, Yong Yang, Thomas R. Niedermayr, Michael
  Gensheimer, Beth Beadle, Quynh-Thu Le, Lei Xing, and Xianjin Dai},
    journal={arXiv preprint arXiv:2407.07296},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.07296},
    primaryClass={physics.med-ph cs.AI cs.CV}
}