ArticleCancer biomarkers : section A of Disease markers2024
Machine learning identifies a 5-serum cytokine panel for the early detection of chronic atrophy gastritis patients.
Article in Cancer biomarkers : section A of Disease markers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bibliometric analysis: a study of the chronic atrophic gastritis in gastric cancer (2015-2024).Frontiers in oncology · 2025Pooled it
- Development and validation of an interpretable machine learning model for non-invasive screening of precancerous gastric lesions using symptom and lifestyle data: a multicentre cohort study.EClinicalMedicine · 2026Article
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17 authors.
Funding
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Abstract
backgroundChronic atrophy gastritis (CAG) is a high-risk pre-cancerous lesion for gastric cancer (GC). The early and accurate detection and discrimination of CAG from benign forms of gastritis (e.g. chronic superficial gastritis, CSG) is critical for optimal management of GC. However, accurate non-invasive methods for the diagnosis of CAG are currently lacking. Cytokines cause inflammation and drive cancer transformation in GC, but their utility as a diagnostic for CAG is poorly characterized.
methodsBlood samples were collected, and 40 cytokines were quantified using a multiplexed immunoassay from 247 patients undergoing screening via endoscopy. Patients were divided into discovery and validation sets. Each cytokine importance was ranked using the feature selection algorithm Boruta. The cytokines with the highest feature importance were selected for machine learning (ML), using the LightGBM algorithm.
resultsFive serum cytokines (IL-10, TNF-α, Eotaxin, IP-10 and SDF-1a) that could discriminate between CAG and CSG patients were identified and used for predictive model construction. This model was robust and could identify CAG patients with high performance (AUC = 0.88, Accuracy = 0.78). This compared favorably to the conventional approach using the PGI/PGII ratio (AUC = 0.59).
conclusionUsing state-of-the-art ML and a blood-based immunoassay, we developed an improved non-invasive screening method for the detection of precancerous GC lesions.
fundingSupported in part by grants from: Jiangsu Science and Technology Project (no. BK20211039); Top Talent Support Program for young and middle-aged people of Wuxi Health Committee (BJ2023008); Medical Key Discipline Program of Wuxi Health Commission (ZDXK2021010), Wuxi Science and Technology Bureau Project (no. N20201004); Scientific Research Program of Wuxi Health Commission (Z202208, J202104).
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