Haisheng He, Wenwei Zhu, H. Miao, Shangfeng Wang, Zunguo Du, Hongxin Zhang, Jiang Ming, Ben Shi, Hao Wang, Jianping Qi, Yong Fan, Wei Wu, Dongyuan Zhao, Lun-Xiu Qin, Fan Zhang
2026.1.20Nature Biomedical Engineering
tlooto Summary
This work identifies endogenous substances with intense autofluorescence in the second near-infrared window (NIR-II, 1,000-1,700 nm) that are abundant in human liver tissues but negligible in cancerous tissues and develops a label-free and wide-field imaging approach, named tissue autofluorescence NIR-II imaging (TANI) for visualizing human liver malignancies.
Abstract
Successful surgical resection of solid tumours requires highly reliable real-time intraoperative tools to accurately delineate tumour boundaries, which remains challenging in routine clinical standards. Here, we identify endogenous substances with intense autofluorescence in the second near-infrared window (NIR-II, 1,000–1,700 nm) that are abundant in human liver tissues but negligible in cancerous tissues. Inspired by this discovery, we develop a label-free and wide-field imaging approach, named tissue autofluorescence NIR-II imaging (TANI) for visualizing human liver malignancies. TANI demonstrates exceptional contrast (7.69 ± 0.52), sensitivity (97.8%) and specificity (98.4%) in delineating various liver malignancies, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma and liver metastasis from cirrhotic or non-cirrhotic livers, outperforming routine fluorescence-guided surgery and conventional autofluorescence imaging in the visible (400–650 nm) or first near-infrared (700–900 nm) window. The excellent performance of TANI remains unaffected by cancer grade/stage, benign lesions or blood/bile contamination. These findings represent a promising advance in intraoperative decision-making and suggest a strong correlation between near-infrared autofluorescence and diseases. We believe that clarifying the molecular insights underlying these autofluorescent substances may provide new diagnostic directions. A label-free imaging approach detects differences in NIR-II autofluorescence intensity between human liver malignancy and the adjacent tissue.
Citation format
HE, Haisheng, et al. Label-free tissue NIR-II autofluorescence imaging for visualization of human liver malignancy. Nature Biomedical Engineering, 2026.