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

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 ( < 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 ( < 0.001).

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