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<p>The concomitant development and evolution of lung computed tomography (CT) and artificial intelligence (AI) have made non-invasive lung imaging a key component of clinical care of patients. However, the scarcity of labeled CT data and the limited generative capacity of existing models have constrained their clinical utility. Here, we present LCTfound, a large-scale vision foundation model designed to overcome these limitations. Trained on a multi-center dataset comprising 105,184 CT scans, LCTfound leverages diffusion-based pretraining and joint encoding of imaging and clinical information to support 8 tasks, including CT enhancement, virtual computed&#xa0;tomography angiography&#xa0;(CTA), sparse-view reconstruction, lesion segmentation, diagnosis, prognosis, cancer pathological response prediction, and three-dimensional surgical navigation. In comprehensive multicenter evaluations, LCTfound consistently outperforms leading baseline models, delivering a unified, broadly deployable solution that both augments clinical decision-making and elevates CT image quality across diverse practice settings. LCTfound establishes a scalable foundation for next-generation clinical imaging intelligence, uniting large AI model with precision healthcare.</p>

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A lung CT vision foundation model facilitating disease diagnosis and medical imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月3日

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  • Zebin Gao,
  • Guoxun Zhang,
  • Hengrui Liang,
  • Jiaxin Liu,
  • Liangdi Ma,
  • Tianyun Wang,
  • Yanchen Guo,
  • YuJia Chen,
  • Zeping Yan,
  • Xiangru Chen,
  • Jianxing He,
  • Feng Xu,
  • Tien Yin Wong,
  • Yuchen Guo,
  • Qionghai Dai

摘要

The concomitant development and evolution of lung computed tomography (CT) and artificial intelligence (AI) have made non-invasive lung imaging a key component of clinical care of patients. However, the scarcity of labeled CT data and the limited generative capacity of existing models have constrained their clinical utility. Here, we present LCTfound, a large-scale vision foundation model designed to overcome these limitations. Trained on a multi-center dataset comprising 105,184 CT scans, LCTfound leverages diffusion-based pretraining and joint encoding of imaging and clinical information to support 8 tasks, including CT enhancement, virtual computed tomography angiography (CTA), sparse-view reconstruction, lesion segmentation, diagnosis, prognosis, cancer pathological response prediction, and three-dimensional surgical navigation. In comprehensive multicenter evaluations, LCTfound consistently outperforms leading baseline models, delivering a unified, broadly deployable solution that both augments clinical decision-making and elevates CT image quality across diverse practice settings. LCTfound establishes a scalable foundation for next-generation clinical imaging intelligence, uniting large AI model with precision healthcare.