Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging

• Type of Material: Article
• Authors: Guolan Lu, Georgia Institute of Technology; James Little III, Emory University; Xu Wang, Emory University; Hongzheng Zhang, Emory University; Mihir Patel, Emory University; Christopher Griffith, Emory University; Mark El-Deiry, Emory University; Amy Chen, Emory University; Baowei Fei, Emory University
• Language: English
• Date: 2017-09-15
• Publisher: American Association for Cancer Research
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: ©2017 AACR.
• Final Published Version (URL): http://dx.doi.org/10.1158/1078-0432.CCR-17-0906
• Title of Journal or Parent Work: Clinical Cancer Research
• ISSN: 1078-0432
• Volume: 23
• Issue: 18
• Start Page: 5426
• End Page: 5436
• Grant/Funding Information: This research is supported in part by the NIH grants (CA176684, R01CA156775, and CA204254) and by Developmental Funds from the Win-ship Cancer Institute of Emory University under award number P30CA138292.
• Keywords: GLUCOSE; SURGERY; SQUAMOUS-CELL CARCINOMA; TISSUE; FROZEN-SECTION; UTILITY; MARGINS; Oncology; Science & Technology; Life Sciences & Biomedicine
• Research Categories: Health Sciences, Oncology; Health Sciences, Medicine and Surgery; Health Sciences, Radiology

Abstract

Purpose: This study intends to investigate the feasibility of using hyperspectral imaging (HSI) to detect and delineate cancers in fresh, surgical specimens of patients with head and neck cancers. Experimental Design: A clinical study was conducted in order to collect and image fresh, surgical specimens from patients (N = 36) with head and neck cancers undergoing surgical resection. A set of machine-learning tools were developed to quantify hyperspectral images of the resected tissue in order to detect and delineate cancerous regions which were validated by histopathologic diagnosis. More than two million reflectance spectral signatures were obtained by HSI and analyzed using machine-learning methods. The detection results of HSI were compared with autofluorescence imaging and fluorescence imaging of two vital-dyes of the same specimens. Results: Quantitative HSI differentiated cancerous tissue from normal tissue in ex vivo surgical specimens with a sensitivity and specificity of 91% and 91%, respectively, and which was more accurate than autofluorescence imaging (P < 0.05) or fluorescence imaging of 2-NBDG (P < 0.05) and proflavine (P < 0.05). The proposed quantification tools also generated cancer probability maps with the tumor border demarcated and which could provide real-time guidance for surgeons regarding optimal tumor resection. Conclusions: This study highlights the feasibility of using quantitative HSI as a diagnostic tool to delineate the cancer boundaries in surgical specimens, and which could be translated into the clinic application with the hope of improving clinical outcomes in the future.