biostudies-literatureFan ZKey Project supported by Medical Science and technology development Foundation, Nanjing Department of HealthNational Natural Science Foundation of ChinaNatural Science Foundation of Jiangsu Province21795https://www.ebi.ac.uk/biostudies/studies/S-EPMC9758153biostudies-literatureUnknown12(1)Non-invasive and cost-effective diagnosis of gastric cancer is essential to improve outcomes. Aim of the study was to establish a neural network model based on patient demographic data and serum biomarker panels to aid gastric cancer diagnosis. A total of 295 patients hospitalized in Nanjing Drum Tower hospital diagnosed with gastric cancer based on tissue biopsy, and 423 healthy volunteers were included in the study. Demographical information and tumor biomarkers were obtained from Hospital Information System (HIS) as original data. Pearson's correlation analysis was applied on 574 individuals' data (training set, 229 patients and 345 healthy volunteers) to analyze the relationship between each variable and the final diagnostic result. And independent sample t test was used to detect the Scientific reportsDevelopment and validation of an artificial neural network model for non-invasive gastric cancer screening and diagnosis.PMC975815382002242BK20200135YKK20054Gu XFan ZGuo YMiao WHuang RfalseDevelopment and validation of an artificial neural network model for non-invasive gastric cancer screening and diagnosis.Non-invasive and cost-effective diagnosis of gastric cancer is essential to improve outcomes. Aim of the study was to establish a neural network model based on patient demographic data and serum biomarker panels to aid gastric cancer diagnosis. A total of 295 patients hospitalized in Nanjing Drum Tower hospital diagnosed with gastric cancer based on tissue biopsy, and 423 healthy volunteers were included in the study. Demographical information and tumor biomarkers were obtained from Hospital Information System (HIS) as original data. Pearson's correlation analysis was applied on 574 individuals' data (training set, 229 patients and 345 healthy volunteers) to analyze the relationship between each variable and the final diagnostic result. And independent sample t test was used to detect the 2022-01-01T00:00:00Z2022 Dec2025-04-04T19:49:32.711Z2025-04-04T19:49:32.711ZS-EPMC97581533652666410.1038/s41598-022-26477-4