Establishment of a Gastric Cancer Diagnostic Model Based on the Combined Evaluation of Peripheral Blood Immune-inflammatory Nutritional Indicators and Tumor Markers.

This study aimed to develop a non-invasive diagnostic model for gastric cancer (GC) by combining serum immune-inflammatory-nutrition indicators with traditional tumor markers to improve early diagnosis efficiency. Our retrospective analysis included 3,464 participants, comprising 833 GC patients and 2,631 chronic gastritis controls, recruited between January 2018 and July 2025. The dataset was split into training and validation sets at a 7 : 3 ratio, and least absolute shrinkage and selection operator (LASSO) regression was utilized to screen for characteristic variables. Of the candidate variables, seven key features-age, sex, body mass index, carcinoembryonic antigen, carbohydrate antigen 199, systemic immune-inflammation index/albumin (SII/ALB), and prognostic nutritional index (PNI)-were identified and integrated into a multivariate logistic regression model. The analysis indicated that advanced age (OR, 1.23; 95% CI, 1.20-1.26) and elevated SII/ALB ratios (OR, 2.63; 95% CI, 1.93-3.58) were significant risk factors, whereas higher PNI values (OR, 0.12; 95% CI, 0.09-0.17) were associated with a reduced risk. The model demonstrated excellent diagnostic performance, achieving an area under the curve of 0.93 (95% CI, 0.91-0.94) in the training set and 0.92 (95% CI, 0.90-0.94) in the validation set. Subgroup analysis further revealed that the seven key markers were significantly associated with Lauren classification, histological grade, and tumor-node-metastasis stage, progressively worsening with disease advancement. Our study underscores the high accuracy and clinical utility of this multimodal model, providing a reliable, non-invasive tool for the early screening and personalized pretreatment assessment of gastric cancer.