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Optical Biopsy of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks.
Proc SPIE Int Soc Opt Eng 2018 Jan-Feb; 10469PS

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.

Authors+Show Affiliations

Georgia Institute of Technology & Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, GA, USA. Medical College of Georgia, Augusta University, Augusta, GA, USA.Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, GA, USA.Emory University School of Medicine, Department of Hematology and Medical Oncology, Atlanta, GA, USA.Emory University School of Medicine, Department of Otolaryngology, Atlanta, GA, USA. Winship Cancer Institute of Emory University, Atlanta, GA, USA.Emory University School of Medicine, Department of Pathology and Laboratory Medicine, Atlanta, GA, USA.Emory University School of Medicine, Department of Otolaryngology, Atlanta, GA, USA. Winship Cancer Institute of Emory University, Atlanta, GA, USA.Emory University School of Medicine, Department of Otolaryngology, Atlanta, GA, USA. Winship Cancer Institute of Emory University, Atlanta, GA, USA.Georgia Institute of Technology & Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, GA, USA. Winship Cancer Institute of Emory University, Atlanta, GA, USA. Emory University, Department of Mathematics and Computer Science, Atlanta, GA, USA. Emory University, Department of Radiology and Imaging Sciences, Atlanta, GA, USA.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30197462

Citation

Halicek, Martin, et al. "Optical Biopsy of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks." Proceedings of SPIE--the International Society for Optical Engineering, vol. 10469, 2018.
Halicek M, Little JV, Wang X, et al. Optical Biopsy of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks. Proc SPIE Int Soc Opt Eng. 2018;10469.
Halicek, M., Little, J. V., Wang, X., Patel, M., Griffith, C. C., El-Deiry, M. W., ... Fei, B. (2018). Optical Biopsy of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks. Proceedings of SPIE--the International Society for Optical Engineering, 10469, doi:10.1117/12.2289023.
Halicek M, et al. Optical Biopsy of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks. Proc SPIE Int Soc Opt Eng. 2018;10469 PubMed PMID: 30197462.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Optical Biopsy of Head and Neck Cancer Using Hyperspectral Imaging and Convolutional Neural Networks. AU - Halicek,Martin, AU - Little,James V, AU - Wang,Xu, AU - Patel,Mihir, AU - Griffith,Christopher C, AU - El-Deiry,Mark W, AU - Chen,Amy Y, AU - Fei,Baowei, Y1 - 2018/02/12/ PY - 2018/9/11/entrez PY - 2018/9/11/pubmed PY - 2018/9/11/medline KW - Hyperspectral imaging KW - convolutional neural network KW - deep learning KW - head and neck cancer KW - head and neck surgery KW - intraoperative imaging KW - optical biopsy JF - Proceedings of SPIE--the International Society for Optical Engineering JO - Proc SPIE Int Soc Opt Eng VL - 10469 N2 - 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. SN - 0277-786X UR - https://www.unboundmedicine.com/medline/citation/30197462/Optical_Biopsy_of_Head_and_Neck_Cancer_Using_Hyperspectral_Imaging_and_Convolutional_Neural_Networks_ L2 - https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/30197462/ DB - PRIME DP - Unbound Medicine ER -