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Background <p>Before surgical resection of lung tumor, intraoperative biopsy is needed for cancer diagnosis, while current techniques that guide biopsy have limited performance in tumor identification and boundary determination. Remodeling of extracellular matrix (ECM), mainly collagen and elastin fibers, is an emerging hallmark of tumorigenesis.</p> Methods <p>Herein, we establish a quantitative multiphoton microscopy (MPM) imaging method for time-efficient, highly-sensitive lung cancer detection via characterization of ECM remodeling. From label-free images of collagen and elastin fibers acquired simultaneously, we construct a similarity coefficient (SC) metric to describe their interaction, and further develop an artificial intelligence (AI)-ECM framework by producing a fiber voxel dictionary via unsupervised learning of morpho-structural features for explainable and visible assessments of cancer risk.</p> Results <p>The application is demonstrated by ex vivo human lung cancer diagnosis with a sensitivity of 99.37%, and recognizing the tumor boundary. The translational potential is further revealed via in vivo imaging of a murine model harboring human lung cancer.</p> Conclusions <p>This technology can help surgeons perform more precise biopsies and surgeries by providing explainable visual cues, thus leading to better outcomes for lung cancer patients.</p>

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Intraoperative biopsy imaging of lung cancer risk

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月6日

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  • Shuhao Qian,
  • Lu Yang,
  • Jia Meng,
  • Lingxi Zhou,
  • Tao Han,
  • Lingmei Chen,
  • Gangqin Xi,
  • Rushan Jiang,
  • Chuncheng Wang,
  • Bo Niu,
  • Zhihua Ding,
  • Ke Sun,
  • Jianping Lu,
  • Shuangmu Zhuo,
  • Zhiyi Liu

摘要

Background

Before surgical resection of lung tumor, intraoperative biopsy is needed for cancer diagnosis, while current techniques that guide biopsy have limited performance in tumor identification and boundary determination. Remodeling of extracellular matrix (ECM), mainly collagen and elastin fibers, is an emerging hallmark of tumorigenesis.

Methods

Herein, we establish a quantitative multiphoton microscopy (MPM) imaging method for time-efficient, highly-sensitive lung cancer detection via characterization of ECM remodeling. From label-free images of collagen and elastin fibers acquired simultaneously, we construct a similarity coefficient (SC) metric to describe their interaction, and further develop an artificial intelligence (AI)-ECM framework by producing a fiber voxel dictionary via unsupervised learning of morpho-structural features for explainable and visible assessments of cancer risk.

Results

The application is demonstrated by ex vivo human lung cancer diagnosis with a sensitivity of 99.37%, and recognizing the tumor boundary. The translational potential is further revealed via in vivo imaging of a murine model harboring human lung cancer.

Conclusions

This technology can help surgeons perform more precise biopsies and surgeries by providing explainable visual cues, thus leading to better outcomes for lung cancer patients.