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LLM-driven multimodal target volume contouring in radiation oncology

Author

Listed:
  • Yujin Oh

    (Massachusetts General Hospital (MGH) and Harvard Medical School)

  • Sangjoon Park

    (Yonsei University College of Medicine
    Yonsei University)

  • Hwa Kyung Byun

    (Yongin Severance Hospital)

  • Yeona Cho

    (Gangnam Severance Hospital)

  • Ik Jae Lee

    (Yonsei University College of Medicine)

  • Jin Sung Kim

    (Yonsei University College of Medicine
    Oncosoft Inc.)

  • Jong Chul Ye

    (Korea Advanced Institute of Science and Technology (KAIST))

Abstract

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 an LLM-driven multimodal artificial intelligence (AI), namely LLMSeg, that utilizes the clinical information and is applicable to the challenging task of 3-dimensional context-aware target volume delineation for radiation oncology. We validate our proposed LLMSeg within the context of breast cancer radiotherapy using external validation and data-insufficient environments, which attributes highly conducive to real-world applications. We demonstrate that the proposed multimodal LLMSeg exhibits markedly improved performance compared to conventional unimodal AI models, particularly exhibiting robust generalization performance and data-efficiency.

Suggested Citation

  • Yujin Oh & Sangjoon Park & Hwa Kyung Byun & Yeona Cho & Ik Jae Lee & Jin Sung Kim & Jong Chul Ye, 2024. "LLM-driven multimodal target volume contouring in radiation oncology," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-53387-y
    DOI: 10.1038/s41467-024-53387-y
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    References listed on IDEAS

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    1. Michael Moor & Oishi Banerjee & Zahra Shakeri Hossein Abad & Harlan M. Krumholz & Jure Leskovec & Eric J. Topol & Pranav Rajpurkar, 2023. "Foundation models for generalist medical artificial intelligence," Nature, Nature, vol. 616(7956), pages 259-265, April.
    2. Karan Singhal & Shekoofeh Azizi & Tao Tu & S. Sara Mahdavi & Jason Wei & Hyung Won Chung & Nathan Scales & Ajay Tanwani & Heather Cole-Lewis & Stephen Pfohl & Perry Payne & Martin Seneviratne & Paul G, 2023. "Publisher Correction: Large language models encode clinical knowledge," Nature, Nature, vol. 620(7973), pages 19-19, August.
    3. Feng Shi & Weigang Hu & Jiaojiao Wu & Miaofei Han & Jiazhou Wang & Wei Zhang & Qing Zhou & Jingjie Zhou & Ying Wei & Ying Shao & Yanbo Chen & Yue Yu & Xiaohuan Cao & Yiqiang Zhan & Xiang Sean Zhou & Y, 2022. "Deep learning empowered volume delineation of whole-body organs-at-risk for accelerated radiotherapy," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    4. Karan Singhal & Shekoofeh Azizi & Tao Tu & S. Sara Mahdavi & Jason Wei & Hyung Won Chung & Nathan Scales & Ajay Tanwani & Heather Cole-Lewis & Stephen Pfohl & Perry Payne & Martin Seneviratne & Paul G, 2023. "Large language models encode clinical knowledge," Nature, Nature, vol. 620(7972), pages 172-180, August.
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