Endoscopy 2019; 51(04): 333-341
DOI: 10.1055/a-0756-8754
Original article
© Georg Thieme Verlag KG Stuttgart · New York

Computer-assisted diagnosis of early esophageal squamous cell carcinoma using narrow-band imaging magnifying endoscopy

Yuan-Yuan Zhao*
1  Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
,
Di-Xiu Xue*
2  Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China
,
Ya-Lei Wang
1  Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
,
Rong Zhang
3  Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei, China
,
Bin Sun
1  Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
,
Yong-Ping Cai
4  Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
,
Hui Feng
1  Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
,
Yi Cai
1  Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
,
Jian-Ming Xu
1  Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
› Author Affiliations
Further Information

Publication History

submitted 17 November 2017

accepted after revision 20 August 2018

Publication Date:
23 November 2018 (eFirst)

Abstract

Background We developed a computer-assisted diagnosis model to evaluate the feasibility of automated classification of intrapapillary capillary loops (IPCLs) to improve the detection of esophageal squamous cell carcinoma (ESCC).

Methods We recruited patients who underwent magnifying endoscopy with narrow-band imaging for evaluation of a suspicious esophageal condition. Case images were evaluated to establish a gold standard IPCL classification according to the endoscopic diagnosis and histological findings. A double-labeling fully convolutional network (FCN) was developed for image segmentation. Diagnostic performance of the model was compared with that of endoscopists grouped according to years of experience (senior > 15 years; mid level 10 – 15 years; junior 5 – 10 years).

Results Of the 1383 lesions in the study, the mean accuracies of IPCL classification were 92.0 %, 82.0 %, and 73.3 %, for the senior, mid level, and junior groups, respectively. The mean diagnostic accuracy of the model was 89.2 % and 93.0 % at the lesion and pixel levels, respectively. The interobserver agreement between the model and the gold standard was substantial (kappa value, 0.719). The accuracy of the model for inflammatory lesions (92.5 %) was superior to that of the mid level (88.1 %) and junior (86.3 %) groups (P < 0.001). For malignant lesions, the accuracy of the model (B1, 87.6 %; B2, 93.9 %) was significantly higher than that of the mid level (B1, 79.1 %; B2, 90.0 %) and junior (B1, 69.2 %; B2, 79.3 %) groups (P < 0.001).

Conclusions Double-labeling FCN automated IPCL recognition was feasible and could facilitate early detection of ESCC.

* These authors contributed equally to this work.


Tables e1, e3, e6