Optical Biopsy of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks

• Type of Material: Article
• Authors: Martin Halicek, Georgia Institute of Technology; James Little III, Emory University; Xu Wang, 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: 2018-01-01
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © COPYRIGHT SPIE.
• Final Published Version (URL): http://dx.doi.org/10.1117/12.2289023
• Title of Journal or Parent Work: Proceedings of SPIE
• ISSN: 0277-786X
• Volume: 10469
• Grant/Funding Information: This research is supported in part by NIH grants CA176684, CA156775 and CA204254, Georgia Cancer Coalition Distinguished Clinicians and Scientists Award, and a pilot project fund from the Winship Cancer Institute of Emory University under the award number P30CA138292.
• Keywords: Physical Sciences; convolutional neural network; Surgery; Science & Technology; Radiology, Nuclear Medicine & Medical Imaging; head and neck cancer; deep learning; Otorhinolaryngology; intraoperative imaging; Optics; Hyperspectral imaging; head and neck surgery; optical biopsy; Life Sciences & Biomedicine
• Research Categories: Health Sciences, Radiology; Health Sciences, Oncology

Abstract

Successful outcomes of surgical cancer resection necessitate negative, cancer-free surgical margins. Currently, tissue samples are sent to pathology for diagnostic confirmation. Hyperspectral imaging (HSI) is an emerging, non-contact optical imaging technique. A reliable optical method could serve to diagnose and biopsy specimens in real-time. Using convolutional neural networks (CNNs) as a tissue classifier, we developed a method to use HSI to perform an optical biopsy of ex-vivo surgical specimens, collected from 21 patients undergoing surgical cancer resection. Training and testing on samples from different patients, the CNN can distinguish squamous cell carcinoma (SCCa) from normal aerodigestive tract tissues with an area under the curve (AUC) of 0.82, 81% accuracy, 81% sensitivity, and 80% specificity. Additionally, normal oral tissues can be sub-classified into epithelium, muscle, and glandular mucosa using a decision tree method, with an average AUC of 0.94, 90% accuracy, 93% sensitivity, and 89% specificity. After separately training on thyroid tissue, the CNN differentiates between thyroid carcinoma and normal thyroid with an AUC of 0.95, 92% accuracy, 92% sensitivity, and 92% specificity. Moreover, the CNN can discriminate medullary thyroid carcinoma from benign multi-nodular goiter (MNG) with an AUC of 0.93, 87% accuracy, 88% sensitivity, and 85% specificity. Classical-type papillary thyroid carcinoma is differentiated from benign MNG with an AUC of 0.91, 86% accuracy, 86% sensitivity, and 86% specificity. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multi-category diagnostic information for normal head-and-neck tissue, SCCa, and thyroid carcinomas. More patient data are needed in order to fully investigate the proposed technique to establish reliability and generalizability of the work.