Near-infrared II hyperspectral imaging improves the accuracy of pathological sampling of multiple cancer specimens

Pathological histology is the clinical gold standard for cancer diagnosis. Incomplete or excessive sampling of the formalin-fixed excised cancer specimen will result in inaccurate histology assessment or excessive workload. Conventionally, pathologists perform specimen sampling relying on naked-eye observation which is subjective and limited by human perception. Precise identification of tumor beds, size, and margin is challenging, especially for lesions with inconspicuous tumor beds. To break the limits of human eye perception (visible: 400-700 nm) and improve the sampling efficiency, in this study, we propose using a second near-infrared window (NIR-II: 900-1700 nm) hyperspectral imaging (HSI) system to assist specimen sampling on the strength of the verified deep anatomical penetration and low scattering characteristics of the NIR-II optical window. We use selected NIR-II HSI narrow bands to synthesize color images for human eye observation and also apply artificial intelligence (AI)-based algorithm on the complete NIR-II HSI data for automatic tissue classification to assist doctors in specimen sampling. Our study employing 5 pathologists, 92 samples and 7 cancer types shows that NIR-II HSI-assisted methods have significant improvements in determining tumor beds compared with conventional methods (Conventional color image with or without X-ray). The proposed system can be easily integrated into the current workflow, and has high imaging efficiency and no ionizing radiation. It may also find applications in intraoperative detection of residual lesions and identification of different tissues.