Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow‐Band Imaging Endoscopy

作者：Jianwei Xu, Jun Wang, Xianzhang Bian, Ji‐Qing Zhu, Cheng‐Wei Tie, Xiaoqing Liu, Zhiyong Zhou, Xiao‐Guang Ni, Dahong Qian · 发表于：The Laryngoscope · 年份：2021 · DOI：10.1002/lary.29894 · 被引用次数：39 · 研究领域：Head and Neck Cancer Studies、Lung Cancer Diagnosis and Treatment、Photoacoustic and Ultrasonic Imaging

Objectives/Hypothesis To develop a deep‐learning‐based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow‐band imaging (NBI) nasopharyngoscopy images. Study Design Retrospective study. Methods A total of 4,783 nasopharyngoscopy images (2,898 WLI and 1,885 NBI) of 671 patients were collected and a novel deep convolutional neural network (DCNN) framework was developed named Siamese deep convolutional neural network (S‐DCNN), which can simultaneously utilize WLI and NBI images to improve the classification performance. To verify the effectiveness of combining the above‐mentioned two modal images for prediction, we compared the proposed S‐DCNN with two baseline models, namely DCNN‐1 (only considering WLI images) and DCNN‐2 (only considering NBI images). Results In the threefold cross‐validation, an overall accuracy and area under the curve of the three DCNNs achieved 94.9% (95% confidence interval [CI] 93.3%–96.5%) and 0.986 (95% CI 0.982–0.992), 87.0% (95% CI 84.2%–89.7%) and 0.930 (95% CI 0.906–0.961), and 92.8% (95% CI 90.4%–95.3%) and 0.971 (95% CI 0.953–0.992), respectively. The accuracy of S‐DCNN is significantly improved compared with DCNN‐1 ( P ‐value <.001) and DCNN‐2 ( P ‐value = .008). Conclusion Using the deep‐learning technology to automatically diagnose NPC under nasopharyngoscopy can provide valuable reference for NPC screening. Superior performa...