Published online Jan 21, 2021. doi: 10.3748/wjg.v27.i3.281
Peer-review started: October 9, 2020
First decision: November 23, 2020
Revised: December 5, 2020
Accepted: December 22, 2020
Article in press: December 22, 2020
Published online: January 21, 2021
Non-magnifying endoscopy with narrow-band imaging (NM-NBI) has been frequently used in routine screening of esophagus squamous cell carcinoma (ESCC). The performance of NBI for screening of early ESCC is, however, significantly affected by operator experience. Artificial intelligence may be a unique approach to compensate for the lack of operator experience.
In our previous research, we reported a novel system of computer-aided detection (CAD) to localize and identify early ESCC under conventional endoscopic white-light imaging (WLI) with sensitivity of above 97%. The construction of another CAD system for application in NM-NBI was the next step in the continuation of our research.
To construct a CAD system for application in NM-NBI to identify early ESCC and compare it with our previously reported CAD system with endoscopic WLI.
We collected a total of 2167 abnormal NM-NBI images of early ESCC and 2568 normal images from three institutions (Zhongshan Hospital of Fudan University, Xuhui Hospital, and Kiang Wu Hospital) as the training dataset, and 316 pairs of images, each pair including images obtained with WLI and NBI (same part), were collected for validation. Twenty endoscopists participated in this study to review the validation images with or without the assistance of the CAD systems. The diagnostic results of the two CAD systems and the improvement in the diagnostic efficacy of endoscopists were compared in terms of sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.
The area under receiver operating characteristic curve for CAD-NBI was 0.9761. For the validation dataset, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CAD-NBI were 91.0%, 96.7%, 94.3%, 95.3%, and 93.6%, respectively, while those of CAD-WLI were 98.5%, 83.1%, 89.5%, 80.8%, and 98.7%, respectively. CAD-NBI showed superior accuracy and specificity than CAD-WLI (P = 0.028 and P ≤ 0.001, respectively), while CAD-WLI had higher sensitivity than CAD-NBI (P = 0.006). By using both CAD-WLI and CAD-NBI, the endoscopists could improve their diagnostic efficacy to the highest level, with accuracy, sensitivity, and specificity of 94.9%, 92.4%, and 96.7%, respectively.
The CAD-NBI system for screening early ESCC has higher accuracy and specificity than CAD-WLI. Endoscopists can achieve the best diagnostic efficacy by using both CAD-WLI and CAD-NBI.
According to the results, the two CAD systems had different advantages in avoiding missed diagnosis and excessive biopsy, which could help endoscopists, especially those with less experience, in screening of early ESCC more efficiently. As the two CAD systems have unique characteristics, we plan to develop a multichannel deep neural network to extract and combine the features of NBI and WLI simultaneously in our future work.