TY - GEN
T1 - Tumor margin classification 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 - Chen, Amy Y.
AU - Fei, Baowei
N1 - 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. The authors would like to thank the surgical pathology team at Emory University Hospital Midtown including Andrew Balicki, Jacqueline Ernst, Tara Meade, Dana Uesry, and Mark Mainiero, for their help in collecting fresh tissue specimens.
Publisher Copyright:
© 2018 SPIE.
PY - 2018
Y1 - 2018
N2 - One of the largest factors affecting disease recurrence after surgical cancer resection is negative surgical margins. Hyperspectral imaging (HSI) is an optical imaging technique with potential to serve as a computer aided diagnostic tool for identifying cancer in gross ex-vivo specimens. We developed a tissue classifier using three distinct convolutional neural network (CNN) architectures on HSI data to investigate the ability to classify the cancer margins from ex-vivo human surgical specimens, collected from 20 patients undergoing surgical cancer resection as a preliminary validation group. A new approach for generating the HSI ground truth using a registered histological cancer margin is applied in order to create a validation dataset. The CNN-based method classifies the tumor-normal margin of squamous cell carcinoma (SCCa) versus normal oral tissue with an area under the curve (AUC) of 0.86 for inter-patient validation, performing with 81% accuracy, 84% sensitivity, and 77% specificity. Thyroid carcinoma cancer-normal margins are classified with an AUC of 0.94 for inter-patient validation, performing with 90% accuracy, 91% sensitivity, and 88% specificity. Our preliminary results on a limited patient dataset demonstrate the predictive ability of HSI-based cancer margin detection, which warrants further investigation with more patient data and additional processing techniques to optimize the proposed deep learning method.
AB - One of the largest factors affecting disease recurrence after surgical cancer resection is negative surgical margins. Hyperspectral imaging (HSI) is an optical imaging technique with potential to serve as a computer aided diagnostic tool for identifying cancer in gross ex-vivo specimens. We developed a tissue classifier using three distinct convolutional neural network (CNN) architectures on HSI data to investigate the ability to classify the cancer margins from ex-vivo human surgical specimens, collected from 20 patients undergoing surgical cancer resection as a preliminary validation group. A new approach for generating the HSI ground truth using a registered histological cancer margin is applied in order to create a validation dataset. The CNN-based method classifies the tumor-normal margin of squamous cell carcinoma (SCCa) versus normal oral tissue with an area under the curve (AUC) of 0.86 for inter-patient validation, performing with 81% accuracy, 84% sensitivity, and 77% specificity. Thyroid carcinoma cancer-normal margins are classified with an AUC of 0.94 for inter-patient validation, performing with 90% accuracy, 91% sensitivity, and 88% specificity. Our preliminary results on a limited patient dataset demonstrate the predictive ability of HSI-based cancer margin detection, which warrants further investigation with more patient data and additional processing techniques to optimize the proposed deep learning method.
KW - Cancer margin detection
KW - Convolutional neural network
KW - Deep learning
KW - Head and neck cancer
KW - Head and neck surgery
KW - Hyperspectral imaging
KW - In-traoperative imaging
UR - http://www.scopus.com/inward/record.url?scp=85050665323&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050665323&partnerID=8YFLogxK
U2 - 10.1117/12.2293167
DO - 10.1117/12.2293167
M3 - Conference contribution
C2 - 30245540
AN - SCOPUS:85050665323
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2018
A2 - Fei, Baowei
A2 - Webster, Robert J.
PB - SPIE
T2 - Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
Y2 - 12 February 2018 through 15 February 2018
ER -