Cancer Communications
indexed by SCI
BMC

Original article
Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies
Chaofeng Li, Bingzhong Jing, Liangru Ke, Bin Li, Weixiong Xia, Caisheng He, Chaonan Qian, Chong Zhao, Haiqiang Mai, Mingyuan Chen, Kajia Cao, Haoyuan Mo, Ling Guo, Qiuyan Chen, Linquan Tang, Wenze Qiu, Yahui Yu, Hu Liang, Xinjun Huang, Guoying Liu, Wangzhong Li, Lin Wang, Rui Sun, Xiong Zou, Shanshan Guo, Peiyu Huang, Donghua Luo, Fang Qiu, Yishan Wu, Yijun Hua, Kuiyuan Liu, Shuhui Lv, Jingjing Miao, Yanqun Xiang, Ying Sun, Xiang Guo and Xing Lv
Department of Nasopharyngeal Carcinoma, Sun Yat-Sen University Cancer Center, Guangzhou 510060, P. R. China
[Abstract]

Background
Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning.
Methods
An endoscopic images-based nasopharyngeal malignancy detection model (eNPM-DM) consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation. Briefly, a total of 28,966 qualified images were collected. Among these images, 27,536 biopsy-proven images from 7951 individuals obtained from January 1st, 2008, to December 31st, 2016, were split into the training, validation and test sets at a ratio of 7:1:2 using simple randomization. Additionally, 1430 images obtained from January 1st, 2017, to March 31st, 2017, were used as a prospective test set to compare the performance of the established model against oncologist evaluation. The dice similarity coefficient (DSC) was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images, by comparing automatic segmentation with manual segmentation performed by the experts.
Results
All images were histopathologically confirmed, and included 5713 (19.7%) normal control, 19,107 (66.0%) nasopharyngeal carcinoma (NPC), 335 (1.2%) NPC and 3811 (13.2%) benign diseases. The eNPM-DM attained an overall accuracy of 88.7% (95% confidence interval (CI) 87.8%–89.5%) in detecting malignancies in the test set. In the prospective comparison phase, eNPM-DM outperformed the experts: the overall accuracy was 88.0% (95% CI 86.1%–89.6%) vs. 80.5% (95% CI 77.0%–84.0%). The eNPM-DM required less time (40 s vs. 110.0 ± 5.8 min) and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background, with an average DSC of 0.78 ± 0.24 and 0.75 ± 0.26 in the test and prospective test sets, respectively.
Conclusions
The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant, and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.
Cancer Communications   Epub date: 9/26/2018   doi:10.1186/s40880-018-0325-9   [ PDF Full-text ]   

CJC Wechat 微信公众号


 

Editorial Manager


CC adopts Editorial Manager to manage its submissions from Dec.18, 2014

 Submission Guidelines  

 

Reference style for  

 EndNote,
 Reference Manager



Editorial Manager


 

Year:

 

Month:

Advanced search

Subscription


CC is now published by BioMed Central

© Cancer Communications

651 Dongfeng Road East, Guangzhou 510060, P. R. China