Yuan Xiang-Lei, Liu Wei, Liu Yan, Zeng Xian-Hui, Mou Yi, Wu Chun-Cheng, Ye Lian-Song, Zhang Yu-Hang, He Long, Feng Jing, Zhang Wan-Hong, Wang Jun, Chen Xin, Hu Yan-Xing, Zhang Kai-Hua, Hu Bing
Department of Gastroenterology, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, 610041, China.
School of Automation, Nanjing University of Information Science and Technology, Nanjing, China.
Surg Endosc. 2022 Nov;36(11):8651-8662. doi: 10.1007/s00464-022-09353-0. Epub 2022 Jun 15.
Intrapapillary capillary loop (IPCL) is an important factor for predicting invasion depth of esophageal squamous cell carcinoma (ESCC). The invasion depth is closely related to the selection of treatment strategy. However, diagnosis of IPCLs is complicated and subject to interobserver variability. This study aimed to develop an artificial intelligence (AI) system to predict IPCLs subtypes of precancerous lesions and superficial ESCC.
Images of magnifying endoscopy with narrow band imaging from three hospitals were collected retrospectively. IPCLs subtypes were annotated on images by expert endoscopists according to Japanese Endoscopic Society classification. The performance of the AI system was evaluated using internal and external validation datasets (IVD and EVD) and compared with that of the 11 endoscopists.
A total of 7094 images from 685 patients were used to train and validate the AI system. The combined accuracy of the AI system for diagnosing IPCLs subtypes in IVD and EVD was 91.3% and 89.8%, respectively. The AI system achieved better performance than endoscopists in predicting IPCLs subtypes and invasion depth. The ability of junior endoscopists to diagnose IPCLs subtypes (combined accuracy: 84.7% vs 78.2%, P < 0.0001) and invasion depth (combined accuracy: 74.4% vs 67.9%, P < 0.0001) were significantly improved with AI system assistance. Although there was no significant differences, the performance of senior endoscopists was slightly elevated.
The proposed AI system could improve the diagnostic ability of endoscopists to predict IPCLs classification of precancerous lesions and superficial ESCC.
乳头内毛细血管袢（IPCL）是预测食管鳞状细胞癌（ESCC）浸润深度的重要因素。浸润深度与治疗策略的选择密切相关。然而，IPCL的诊断复杂，且存在观察者间差异。本研究旨在开发一种人工智能（AI）系统，以预测癌前病变和浅表ESCC的IPCL亚型。
回顾性收集来自三家医院的窄带成像放大内镜图像。由内镜专家根据日本内镜学会分类对图像上的IPCL亚型进行标注。使用内部和外部验证数据集（IVD和EVD）评估AI系统的性能，并与11位内镜医师的性能进行比较。
共使用来自685例患者的7094张图像训练和验证AI系统。AI系统在IVD和EVD中诊断IPCL亚型的综合准确率分别为91.3%和89.8%。在预测IPCL亚型和浸润深度方面，AI系统的表现优于内镜医师。在AI系统的辅助下，初级内镜医师诊断IPCL亚型的能力（综合准确率：84.7%对78.2%，P < 0.0001）和浸润深度的能力（综合准确率：74.4%对67.9%，P < 0.0001）显著提高。虽然没有显著差异，但高级内镜医师的表现略有提升。
所提出的AI系统可提高内镜医师预测癌前病变和浅表ESCC的IPCL分类的诊断能力。
