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Open AccessArticle

Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning

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Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan
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Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan
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Center for Big Data Analytics and Statistics, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan
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aetherAI, Co., Ltd., No. 3-2, Yuan-Qu Street, Nangang District, Taipei City 115, Taiwan
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Chang Gung Molecular Medicine Research Center, Chang Gung University, No. 259, Wenhua First Road, Guishan District, Taoyuan City 333, Taiwan
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Department of Pathology, MacKay Memorial Hospital, No. 92, Section 2, Zhongshan North Road, Zhongshan District, Taipei City 104, Taiwan
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Tissue Bank, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan
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Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan
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Author to whom correspondence should be addressed.
Cancers 2020, 12(2), 507; https://doi.org/10.3390/cancers12020507
Received: 22 January 2020 / Revised: 16 February 2020 / Accepted: 19 February 2020 / Published: 22 February 2020
Pathologic diagnosis of nasopharyngeal carcinoma (NPC) can be challenging since most cases are nonkeratinizing carcinoma with little differentiation and many admixed lymphocytes. Our aim was to evaluate the possibility to identify NPC in nasopharyngeal biopsies using deep learning. A total of 726 nasopharyngeal biopsies were included. Among them, 100 cases were randomly selected as the testing set, 20 cases as the validation set, and all other 606 cases as the training set. All three datasets had equal numbers of NPC cases and benign cases. Manual annotation was performed. Cropped square image patches of 256 × 256 pixels were used for patch-level training, validation, and testing. The final patch-level algorithm effectively identified NPC patches, with an area under the receiver operator characteristic curve (AUC) of 0.9900. Using gradient-weighted class activation mapping, we demonstrated that the identification of NPC patches was based on morphologic features of tumor cells. At the second stage, whole-slide images were sequentially cropped into patches, inferred with the patch-level algorithm, and reconstructed into images with a smaller size for training, validation, and testing. Finally, the AUC was 0.9848 for slide-level identification of NPC. Our result shows for the first time that deep learning algorithms can identify NPC. View Full-Text
Keywords: nasopharyngeal carcinoma; deep learning; artificial intelligence; convolutional neural network; gradient-weighted class activation mapping; digital pathology; cancer identification nasopharyngeal carcinoma; deep learning; artificial intelligence; convolutional neural network; gradient-weighted class activation mapping; digital pathology; cancer identification
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MDPI and ACS Style

Chuang, W.-Y.; Chang, S.-H.; Yu, W.-H.; Yang, C.-K.; Yeh, C.-J.; Ueng, S.-H.; Liu, Y.-J.; Chen, T.-D.; Chen, K.-H.; Hsieh, Y.-Y.; Hsia, Y.; Wang, T.-H.; Hsueh, C.; Kuo, C.-F.; Yeh, C.-Y. Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning. Cancers 2020, 12, 507.

AMA Style

Chuang W-Y, Chang S-H, Yu W-H, Yang C-K, Yeh C-J, Ueng S-H, Liu Y-J, Chen T-D, Chen K-H, Hsieh Y-Y, Hsia Y, Wang T-H, Hsueh C, Kuo C-F, Yeh C-Y. Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning. Cancers. 2020; 12(2):507.

Chicago/Turabian Style

Chuang, Wen-Yu; Chang, Shang-Hung; Yu, Wei-Hsiang; Yang, Cheng-Kun; Yeh, Chi-Ju; Ueng, Shir-Hwa; Liu, Yu-Jen; Chen, Tai-Di; Chen, Kuang-Hua; Hsieh, Yi-Yin; Hsia, Yi; Wang, Tong-Hong; Hsueh, Chuen; Kuo, Chang-Fu; Yeh, Chao-Yuan. 2020. "Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning" Cancers 12, no. 2: 507.

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