Establish a Scoring Model for High-Risk Population of Gastric Cancer and Study on the Pattern of Opportunistic Screening
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Updated: Dec 5, 2025
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Published on: February 16, 2024
Wei Tao1, Hai-Xia Wang2, Yu-Feng Guo1
1Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan 750004, China.
Objective:
To investigate and study the related risk factors of gastric cancer (GC) patients, to establish a high-risk scoring model of GC by multiple logistic regression analysis, and to explore the establishment of a GC screening mode with clinical opportunistic screening as the main method, and by using the pattern of opportunistic screening to establish the screening of high-risk GC patients and the choice of screening methods in the clinical outpatient work.
Methods:
Collected the epidemiological questionnaire of 99 GC cases and 284 non-GC patients (other chronic gastric diseases and normal) diagnosed by the General Hospital of Ningxia Medical University from October 2017 to March 2019. Serum pepsinogen (PG) levels were measured by enzyme-linked immunosorbent assay (ELISA) and confirmed Helicobacter pylori (Hp) infection in gastric mucosa tissues by Giemsa staining. Determined the high-risk factors and established a scoring model through unconditional logistic regression model analysis, and the ROC curve determined the cut-off value. Then, we followed up 26 patients of nongastric cancer patients constituted a validation group, which validated the model.
Results:
The high-risk factors of GC included age ≥ 55, male, drinking cellar or well water, family history of GC, Hp infection, PGI ≤ 43.6 μg/L, and PGI/PGII ≤ 2.1. Established the high-risk model: Y = A × age + 30 × gender + 30 × drinking water + 30 × Hp infection + 50 × family history of GC + B × PG level. The ROC curve determined that the cut-off value for high-risk GC population was ≥155, and the area under the curve (AUC) was 0.875, the sensitivity and specificity were 87.9% and 71.5%.
Conclusions:
According to the risk factors of GC, using statistical methods can establish a high-risk scoring model of GC, and the score ≥ 155 is divided into the screening cut-off value for high-risk GC population. Using this model for clinical outpatient GC screening is cost-effective and has high sensitivity and specificity.
