Lin Po-Hsiang, Hsieh Jer-Guang, Yu Hsien-Chung, Jeng Jyh-Horng, Hsu Chiao-Lin, Chen Chien-Hua, Wu Pin-Chieh
Department of Emergency Medicine, Kaohsiung Veterans General Hospital, Kaohsiung 813, Taiwan.
Department of Electrical Engineering, I-Shou University, Kaohsiung 840, Taiwan.
Int J Environ Res Public Health. 2021 May 17;18(10):5332. doi: 10.3390/ijerph18105332.
Determining the target population for the screening of Barrett's esophagus (BE), a precancerous condition of esophageal adenocarcinoma, remains a challenge in Asia. The aim of our study was to develop risk prediction models for BE using logistic regression (LR) and artificial neural network (ANN) methods. Their predictive performances were compared. We retrospectively analyzed 9646 adults aged ≥20 years undergoing upper gastrointestinal endoscopy at a health examinations center in Taiwan. Evaluated by using 10-fold cross-validation, both models exhibited good discriminative power, with comparable area under curve (AUC) for the LR and ANN models (Both AUC were 0.702). Our risk prediction models for BE were developed from individuals with or without clinical indications of upper gastrointestinal endoscopy. The models have the potential to serve as a practical tool for identifying high-risk individuals of BE among the general population for endoscopic screening.
确定巴雷特食管（BE）的筛查目标人群是亚洲面临的一项挑战，BE是食管腺癌的一种癌前病变。我们研究的目的是使用逻辑回归（LR）和人工神经网络（ANN）方法开发BE的风险预测模型。比较了它们的预测性能。我们回顾性分析了台湾一家健康检查中心9646名年龄≥20岁接受上消化道内镜检查的成年人。通过10倍交叉验证评估，两个模型均表现出良好的判别能力，LR模型和ANN模型的曲线下面积（AUC）相当（两个AUC均为0.702）。我们的BE风险预测模型是根据有或无上消化道内镜临床指征的个体开发的。这些模型有可能作为一种实用工具，用于在普通人群中识别BE的高危个体以进行内镜筛查。
