Li Bing, Cai Shi-Lun, Tan Wei-Min, Li Ji-Chun, Yalikong Ayimukedisi, Feng Xiao-Shuang, Yu Hon-Ho, Lu Pin-Xiang, Feng Zhen, Yao Li-Qing, Zhou Ping-Hong, Yan Bo, Zhong Yun-Shi
Department of Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai 200032, China.
School of Computer Science, Fudan University, Shanghai 200433, China.
World J Gastroenterol. 2021 Jan 21;27(3):281-293. doi: 10.3748/wjg.v27.i3.281.
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.
To construct a computer-aided detection (CAD) system for application in NM-NBI to identify early ESCC and to compare it with our previously reported CAD system with endoscopic white-light imaging (WLI).
A total of 2167 abnormal NM-NBI images of early ESCC and 2568 normal images were collected 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 by 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 improvement in 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 ( = 0.028 and ≤ 0.001, respectively), while CAD-WLI had higher sensitivity than CAD-NBI ( = 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 using both CAD-WLI and CAD-NBI.
非放大窄带成像内镜检查(NM-NBI)已频繁用于食管鳞状细胞癌(ESCC)的常规筛查。然而,NBI对早期ESCC筛查的性能受操作者经验的显著影响。人工智能可能是弥补操作者经验不足的独特方法。
构建一种计算机辅助检测(CAD)系统,应用于NM-NBI以识别早期ESCC,并将其与我们先前报道的具有内镜白光成像(WLI)的CAD系统进行比较。
从三个机构(复旦大学附属中山医院、徐汇区中心医院和江湾医院)收集了总共2167张早期ESCC的异常NM-NBI图像和2568张正常图像作为训练数据集,并收集了316对图像,每对图像包括通过WLI和NBI获得的(同一部位)图像用于验证。20名内镜医师参与本研究,在有或没有CAD系统辅助的情况下审查验证图像。比较了两种CAD系统的诊断结果以及内镜医师诊断效能的提高情况,包括敏感性、特异性、准确性、阳性预测值和阴性预测值。
CAD-NBI的受试者操作特征曲线下面积为0.9761。对于验证数据集,CAD-NBI的敏感性、特异性、准确性、阳性预测值和阴性预测值分别为91.0%、96.7%、94.3%、95.3%和93.6%,而CAD-WLI的分别为98.5%、83.1%、89.5%、80.8%和98.7%。CAD-NBI显示出比CAD-WLI更高的准确性和特异性(分别为 = 0.028和 ≤ 0.001),而CAD-WLI的敏感性高于CAD-NBI( = 0.006)。通过同时使用CAD-WLI和CAD-NBI,内镜医师可将其诊断效能提高到最高水平,准确性、敏感性和特异性分别为94.9%、92.4%和96.7%。
用于筛查早期ESCC的CAD-NBI系统比CAD-WLI具有更高的准确性和特异性。内镜医师同时使用CAD-WLI和CAD-NBI可实现最佳诊断效能。