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  1. Home
  3. Machine Learning Identifies A 5-serum Cytokine Panel For The Early Detection Of Chronic Atrophy Gastritis Patients.
  1. Home
  3. Machine Learning Identifies A 5-serum Cytokine Panel For The Early Detection Of Chronic Atrophy Gastritis Patients.

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Machine learning identifies a 5-serum cytokine panel for the early detection of chronic atrophy gastritis patients.

Fangmei An1,1, Yan Ge2,1, Wei Ye2

  • 1Department of Gastroenterology, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, National Clinical Research Center for Digestive Diseases (Xi 'an) Jiangsu Branch Wuxi, Jiangsu, China.

Cancer Biomarkers : Section a of Disease Markers
|September 13, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

A new blood test accurately detects chronic atrophy gastritis (CAG), a precancerous lesion for gastric cancer (GC). This non-invasive method shows high performance, improving early detection of high-risk GC patients.

Keywords:
Chronic Atrophy Gastritis (CAG)Chronic Superficial Gastritis (CSG)CytokinesMachine Learning (ML) AlgorithmsScreening

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Area of Science:

  • Biomarkers
  • Oncology
  • Medical Diagnostics

Background:

  • Chronic atrophy gastritis (CAG) is a significant precancerous lesion for gastric cancer (GC).
  • Accurate, non-invasive methods for early CAG detection are currently lacking.
  • Cytokine roles in GC pathogenesis are known, but their diagnostic utility for CAG is under-explored.

Purpose of the Study:

  • To develop and validate a non-invasive diagnostic method for chronic atrophy gastritis (CAG).
  • To identify specific serum cytokines that can discriminate between CAG and chronic superficial gastritis (CSG).
  • To assess the performance of a machine learning model for CAG detection using identified cytokines.

Main Methods:

  • Quantified 40 serum cytokines using multiplexed immunoassay in 247 patients.
  • Employed Boruta feature selection to rank cytokine importance.
  • Utilized LightGBM machine learning algorithm for predictive model construction.

    • Main Results:

    • Identified five key serum cytokines (IL-10, TNF-α, Eotaxin, IP-10, SDF-1a) differentiating CAG from CSG.
    • Developed a predictive model with high performance: AUC = 0.88, Accuracy = 0.78.
    • The developed model significantly outperformed the conventional PGI/PGII ratio (AUC = 0.59).

    Conclusions:

    • Developed a robust, non-invasive screening method for precancerous gastric cancer lesions using machine learning and serum cytokines.
    • This approach offers improved diagnostic accuracy compared to existing methods.
    • Highlights the potential of cytokine profiling for early gastric cancer detection.