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Hyperspectral Imaging of Head and Neck Squamous Cell Carcinoma for Cancer Margin Detection in Surgical Specimens from 102 Patients Using Deep Learning

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Department of Bioengineering, The University of Texas at Dallas, Richardson, TX 75080, USA
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Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30329, USA
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Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
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Department of Otolaryngology, Emory University School of Medicine, Atlanta, GA 30322, USA
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Department of Otolaryngology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Advanced Imaging Research Center, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
*
Author to whom correspondence should be addressed.
Cancers 2019, 11(9), 1367; https://doi.org/10.3390/cancers11091367
Received: 29 July 2019 / Revised: 4 September 2019 / Accepted: 12 September 2019 / Published: 14 September 2019
Surgical resection of head and neck (H and N) squamous cell carcinoma (SCC) may yield inadequate surgical cancer margins in 10 to 20% of cases. This study investigates the performance of label-free, reflectance-based hyperspectral imaging (HSI) and autofluorescence imaging for SCC detection at the cancer margin in excised tissue specimens from 102 patients and uses fluorescent dyes for comparison. Fresh surgical specimens (n = 293) were collected during H and N SCC resections (n = 102). The tissue specimens were imaged with reflectance-based HSI and autofluorescence imaging and afterwards with two fluorescent dyes for comparison. A histopathological ground truth was made. Deep learning tools were developed to detect SCC with new patient samples (inter-patient) and machine learning for intra-patient tissue samples. Area under the curve (AUC) of the receiver-operator characteristic was used as the main evaluation metric. Additionally, the performance was estimated in mm increments circumferentially from the tumor-normal margin. In intra-patient experiments, HSI classified conventional SCC with an AUC of 0.82 up to 3 mm from the cancer margin, which was more accurate than proflavin dye and autofluorescence (both p < 0.05). Intra-patient autofluorescence imaging detected human papilloma virus positive (HPV+) SCC with an AUC of 0.99 at 3 mm and greater accuracy than proflavin dye (p < 0.05). The inter-patient results showed that reflectance-based HSI and autofluorescence imaging outperformed proflavin dye and standard red, green, and blue (RGB) images (p < 0.05). In new patients, HSI detected conventional SCC in the larynx, oropharynx, and nasal cavity with 0.85–0.95 AUC score, and autofluorescence imaging detected HPV+ SCC in tonsillar tissue with 0.91 AUC score. This study demonstrates that label-free, reflectance-based HSI and autofluorescence imaging methods can accurately detect the cancer margin in ex-vivo specimens within minutes. This non-ionizing optical imaging modality could aid surgeons and reduce inadequate surgical margins during SCC resections. View Full-Text
Keywords: hyperspectral imaging; head and neck cancer; squamous cell carcinoma; deep learning; convolutional neural network hyperspectral imaging; head and neck cancer; squamous cell carcinoma; deep learning; convolutional neural network
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Halicek, M.; Dormer, J.D.; Little, J.V.; Chen, A.Y.; Myers, L.; Sumer, B.D.; Fei, B. Hyperspectral Imaging of Head and Neck Squamous Cell Carcinoma for Cancer Margin Detection in Surgical Specimens from 102 Patients Using Deep Learning. Cancers 2019, 11, 1367.

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