Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

Martin Halicek, James V. Little, Xu Wang, Mihir Patel, Christopher C. Griffith, Mark W. El-Deiry, Amy Y. Chen, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationOptical Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2018
EditorsMax J. Witjes, Brian J. F. Wong, Justus F. Ilgner
PublisherSPIE
ISBN (Electronic)9781510614239
DOIs
StatePublished - 2018
Externally publishedYes
EventOptical Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2018 - San Francisco, United States
Duration: Jan 27 2018Jan 28 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10469
ISSN (Print)1605-7422

Conference

ConferenceOptical Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2018
CountryUnited States
CitySan Francisco
Period1/27/181/28/18

Keywords

  • Hyperspectral imaging
  • convolutional neural network
  • deep learning
  • head and neck cancer
  • head and neck surgery
  • intraoperative imaging
  • optical biopsy

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Halicek, M., Little, J. V., Wang, X., Patel, M., Griffith, C. C., El-Deiry, M. W., Chen, A. Y., & Fei, B. (2018). Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks. In M. J. Witjes, B. J. F. Wong, & J. F. Ilgner (Eds.), Optical Imaging, Therapeutics, and Advanced Technology in Head and Neck Surgery and Otolaryngology 2018 [104690X] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10469). SPIE. https://doi.org/10.1117/12.2289023