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- Title
Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning.
- Authors
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
- Abstract
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.
- Subjects
ALGORITHMS; BIOPSY; CELL differentiation; RESEARCH methodology; NASOPHARYNX; NASOPHARYNX cancer; ARTIFICIAL neural networks; RECEIVER operating characteristic curves; DEEP learning
- Publication
Cancers, 2020, Vol 12, Issue 2, p507
- ISSN
2072-6694
- Publication type
Article
- DOI
10.3390/cancers12020507