Enhancing diagnostic accuracy: Role of stomach-specific serum biomarkers in real-world risk-based sequential screening for malignant gastric lesions
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objective: A risk-based sequential screening strategy, from questionnaire-based assessment to biomarker measurement and then to endoscopic examination, has the potential to enhance gastric cancer (GC) screening efficiency. We aimed to evaluate the ability of five common stomach-specific serum biomarkers to further enrich high-risk individuals for GC in the questionnaire-identified high-risk population. Methods: This study was conducted based on a risk-based screening program in Ningxia Hui Autonomous Region, China. We first performed questionnaire assessment involving 23,381 individuals (7,042 outpatients and 16,339 individuals from the community), and those assessed as "high-risk" were then invited to participate in serological assays and endoscopic examinations. The serological biomarker model was derived based on logistic regression, with predictors selected via the Akaike information criterion. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC). Results: IgG) antibodies. This model generated an AUC of 0.733 (95% confidence interval: 0.655-0.812) and demonstrated the best discriminative ability compared with previously developed serological biomarker models. As the risk cut-off value of our model rose, the detection rate increased and the number of endoscopies needed to detect one case decreased. Conclusions: IgG could be jointly used to further enrich high-risk individuals for GC among those selected by questionnaire assessment, providing insight for the development of a multi-stage risk-based sequential strategy for GC screening.
Authors: Yanna Chi, Hongrui Tian, Chao Shi, Zhen Liu, Xue Li, Miao Zhang, Jun Liu, Xianmei Chen, Wenlei Yang, Yaqi Pan, Huanyu Chen, Mengfei Liu, Shengjuan Hu, Zhonghu He, Yang Ke
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410313570