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Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies

查看全文 作　　者：Chaofeng [1,2,5,7]Li;Bingzhong [1,2]Jing;Liangru [1,3]Ke;Bin [1,2]Li;Weixiong [1,4]Xia;Caisheng [1,2]He;Chaonan [1,4]Qian;Chong [1,4]Zhao;Haiqiang [1,4]Mai;Mingyuan [1,4]Chen;Kajia [1,4]Cao;Haoyuan [1,4]Mo;Ling [1,4]Guo;Qiuyan [1,4]Chen;Linquan [1,4]Tang;Wenze [1,4]Qiu;Yahui [1,4]Yu;Hu [1,4]Liang;Xinjun [1,4]Huang;Guoying [1,4]Liu;Wangzhong [1,4]Li;Lin [1,4]Wang;Rui [1,4]Sun;Xiong [1,4]Zou;Shanshan [1,4]Guo;Peiyu [1,4]Huang;Donghua [1,4]Luo;Fang [1,4]Qiu;Yishan [1,4]Wu;Yijun [1,4]Hua;Kuiyuan [1,4]Liu;Shuhui [1,4]Lv;Jingjing [1,4]Miao;Yanqun [1,4]Xiang;Ying [1,6]Sun;Xiang [1,4,7]Guo;Xing [1,4,7]Lv 高影响力作者 机构地区：[1]State Key Laboratory of Oncology in South China,Collaborative Innovation Center of Cancer Medicine,Guangzhou 510060,P.R.China;[2]Department of Information,Sun Yat-Sen University Cancer Center,Guangzhou 510060,P.R.China;[3]Department of Radiology,Sun Yat-Sen University Cancer Center,Guangzhou 510060,P.R.China;[4]Department of Nasopharyngeal Carcinoma,Sun Yat-Sen University Cancer Center,Guangzhou 510060,P.R.China;[5]Precision Medicine Center,Sun Yat-Sen University Cancer Center,Guangzhou 510060,P.R.China;[6]Department of Radiotherapy,Sun Yat-Sen University Cancer Center,Guangzhou 510060,P.R.China;[7]Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy,Guangzhou 510060,P.R.China高影响力机构 出　　处：《Cancer Communications》索引2018年第38卷第1期,共11页高影响力期刊 基　　金：supported by the National Natural Science Foundation of China[Grant Nos.81572665,81672680,81472525,81702873];the International Cooperation Project of Science and Technology Plan of Guangdong Province[Grant No.2016A050502011];the Health&Medical Collaborative Innovation Project of Guangzhou City,China(Grant No.201604020003). 摘　　要：Background:Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperpla-sia,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 randomiza-tion.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 coef-ficient(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 segmenta-tion 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. 关 键 词：Nasopharyngeal malignancy Deep learning Differential diagnosis Automatic segmentation
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