Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images
Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images
复制标题
基于喉镜图像的深度学习计算机辅助喉癌诊断
DOI：
10.1016/j.ebiom.2019.08.075
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发表时间：
2019-10-01
期刊：
影响因子：
11.1
通讯作者：
Yang, Haidi
中科院分区：
文献类型：
--
作者：
Xiong, Hao;Lin, Peiliang;Yang, Haidi
目的：目的：开发一种深度卷积神经网络（DCNN），用于自动检测喉镜图像中的喉癌（LCA）。方法：使用来自中国2家三级医院的13，721张LCA、喉癌前病变（PRELCA）、良性喉肿瘤（BLT）和正常组织（NORM）的喉镜图像构建并训练基于DCNN的诊断系统，其中2293张来自206名LCA受试者，1807例来自203例PRELCA受试者，6448例来自774例BLT受试者，3191例来自633例NORM受试者。来自中国其他3家三级医院的1176张喉镜图像的独立测试集，包括来自44名LCA受试者的132张，43名PRELCA受试者的129张，168名BLT受试者的504张和137名NORM受试者的411张，应用于构建的DCNN，以评估其与经验丰富的内窥镜医生的性能。DCCN检测所有病变和正常组织中LCA和PRELCA的灵敏度为0.731，特异性为0.922，AUC为0.922，总准确度为0.867。当与独立测试集中的人类专家相比时，DCCN检测LCA和PRELCA的性能达到了0.720的灵敏度，0.948的特异性，0.953的AUC和0.897的总体准确度，这与具有10-20年工作经验的经验丰富的人类专家相当。此外，DCNN检测LCA的总体准确率为0.773，这也是一个有经验的人类专家与10-20年的工作经验，并超过了不到10年的工作experiences.Conclusions的专家：DCNN具有较高的灵敏度和特异性自动检测的LCA和PRELCA从BLT和NORM在喉镜图像。这种新颖有效的方法有助于早期LCA的早期诊断，从而改善临床结局并减轻内镜医生的负担。(C)2019年，任作家。由爱思唯尔公司出版
Objective: To develop a deep convolutional neural network (DCNN) that can automatically detect laryngeal cancer (LCA) in laryngoscopic images.Methods: A DCNN-based diagnostic system was constructed and trained using 13,721 laryngoscopic images of LCA, precancerous laryngeal lesions (PRELCA), benign laryngeal tumors (BLT) and normal tissues (NORM) from 2 tertiary hospitals in China, including 2293 from 206 LCA subjects, 1807 from 203 PRELCA subjects, 6448 from 774 BLT subjects and 3191 from 633 NORM subjects. An independent test set of 1176 laryngoscopic images from other 3 tertiary hospitals in China, including 132 from 44 LCA subjects, 129 from 43 PRELCA subjects, 504 from 168 BLT subjects and 411 from 137 NORM subjects, was applied to the constructed DCNN to evaluate its performance against experienced endoscopists.Results: The DCCN achieved a sensitivity of 0.731, a specificity of 0.922, an AUC of 0.922, and the overall accuracy of 0.867 for detecting LCA and PRELCA among all lesions and normal tissues. When compared to human experts in an independent test set, the DCCN's performance on detection of LCA and PRELCA achieved a sensitivity of 0.720, a specificity of 0.948, an AUC of 0.953, and the overall accuracy of 0.897, which was comparable to that of an experienced human expert with 10-20 years of work experience. Moreover, the overall accuracy of DCNN for detection of LCA was 0.773, which was also comparable to that of an experienced human expert with 10-20 years of work experience and exceeded the experts with less than 10 years of work experience.Conclusions: The DCNN has high sensitivity and specificity for automated detection of LCA and PRELCA from BLT and NORM in laryngoscopic images. This novel and effective approach facilitates earlier diagnosis of early LCA, resulting in improved clinical outcomes and reducing the burden of endoscopists. (C) 2019 The Authors. Published by Elsevier B.V.