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<p>Early detection of lung cancer is crucial for improving patient outcomes. However, accurately diagnosing invasive pulmonary nodules and predicting tumor invasiveness remain major clinical challenges. Given the established role of immune dysfunction in cancer development, we hypothesize that peripheral immune profiling could provide a strategy for managing pulmonary nodules. In this multi-center, prospective study, we combine peripheral immune profiling via mass cytometry with machine learning algorithms to develop an integrated pulmonary nodule management platform. This platform accurately distinguishes invasive from non-invasive pulmonary nodules (AUC = 0.952), outperforming established clinical and radiomics-based models. Furthermore, it effectively predicts tumor invasiveness, differentiating minimally invasive from invasive adenocarcinoma (AUC = 0.949), thereby offering valuable guidance for surgical decision-making. In conclusion, the platform demonstrates substantial clinical utility and holds significant promise as a precision tool for future management of pulmonary nodules.</p>

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Precise diagnosis of small invasive pulmonary nodules driven by single-cell immune signatures in peripheral blood

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月8日

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  • Yang Xia,
  • Yin Zhu,
  • Sai Zhang,
  • Mo Zhou,
  • Jiaqi Zhou,
  • Guanxin Xu,
  • Baiqin Zhao,
  • Guofei Zhang,
  • Qiyuan Wang,
  • Bin Wang,
  • Zexin Chen,
  • Hongyu Shi,
  • Xiuning Le,
  • Tony Mok,
  • Wei Chen,
  • Junqiang Fan,
  • Weiwei Yin,
  • Wen Li

摘要

Early detection of lung cancer is crucial for improving patient outcomes. However, accurately diagnosing invasive pulmonary nodules and predicting tumor invasiveness remain major clinical challenges. Given the established role of immune dysfunction in cancer development, we hypothesize that peripheral immune profiling could provide a strategy for managing pulmonary nodules. In this multi-center, prospective study, we combine peripheral immune profiling via mass cytometry with machine learning algorithms to develop an integrated pulmonary nodule management platform. This platform accurately distinguishes invasive from non-invasive pulmonary nodules (AUC = 0.952), outperforming established clinical and radiomics-based models. Furthermore, it effectively predicts tumor invasiveness, differentiating minimally invasive from invasive adenocarcinoma (AUC = 0.949), thereby offering valuable guidance for surgical decision-making. In conclusion, the platform demonstrates substantial clinical utility and holds significant promise as a precision tool for future management of pulmonary nodules.