Label-free hyperspectral imaging and quantification methods for surgical margin assessment of tissue specimens of cancer patients

Hyperspectral imaging (HSI) is a relatively new modality in medicine and can have many potential applications. In this study, we developed label-free hyperspectral imaging for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of images of the same field of view that are acquired at different wavelengths. Every pixel in the hypercube has an optical spectrum. We collected surgical tissue specimens from 16 human subjects who underwent head and neck (H&N) cancer surgery. We acquired both HSI, autofluorescence images, and fluorescence images with 2-NBDG and proflavine from the specimens. Digitized histologic slides were examined by an H&N pathologist. We developed image preprocessing and classification methods for HSI data and differentiate cancer from benign tissue. The hyperspectral imaging and classification method was able to distinguish between cancer and normal tissue from oral cavity with an average accuracy of 90±8%, sensitivity of 89±9%, and specificity of 91±6%. This study suggests that label-free hyperspectral imaging has great potential for surgical margin assessment in tissue specimens of H&N cancer patients. Further development of the imaging technology and quantification methods is warranted for its application in image-guided surgery.