Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging

• Type of Material: Article
• Authors: Baowei Fei, Emory University; Guolan Lu, Georgia Institute of Technology; Xu Wang, Emory University; Hongzheng Zhang, Emory University; James Little III, Emory University; Kelly Magliocca, Emory University; Amy Chen, Emory University
• Language: English
• Date: 2017-01-01
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2017 SPIE.
• Final Published Version (URL): http://dx.doi.org/10.1117/12.2249803
• Title of Journal or Parent Work: Proceedings of SPIE
• ISSN: 0277-786X
• Volume: 10054
• Grant/Funding Information: This research is supported in part by NIH grants (CA176684, R01CA156775, and CA204254) and by Developmental Funds from the Winship Cancer Institute of Emory University under award number P30CA138292.
• Keywords: image-guided surgery; image classification; ACCURACY; Hyperspectral imaging; cancer detection; Engineering, Biomedical; SYSTEM; SURVIVAL; NAVIGATION; SURGERY; Technology; tumor margin assessment; SAFETY; head and neck cancer; Science & Technology; image quantification; Physical Sciences; RESECTION MARGINS; fluorescence imaging; HEAD; Optics; Engineering; label free
• Research Categories: Health Sciences, Radiology; Engineering, Biomedical

Abstract

We are developing label-free hyperspectral imaging (HSI) for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on the HSI image has an optical spectrum. We developed preprocessing and classification methods for HSI data. We used spectral features from HSI data for the classification of cancer and benign tissue. We collected surgical tissue specimens from 16 human patients 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. 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%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94±6%, sensitivity of 94±6%, and specificity of 95±6%. Hyperspectral imaging outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study suggests that label-free hyperspectral imaging has great potential for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the hyperspectral imaging technology is warranted for its application in image-guided surgery.