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Development and validation of a novel blood-based biomarker for gastric cancer triage in chronic dyspepsia

Minji Seo1, Ka Man Cheung2, Serene J L Lam3

  • 1Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.

NPJ Digital Medicine
|April 17, 2026
PubMed

Insights

Routine blood tests, enhanced by machine learning into the RBT-GC biomarker, can effectively stratify gastric cancer risk. This approach significantly reduces the need for invasive scoping, optimizing endoscopy resource allocation for better patient outcomes.

Area of Science:

  • Oncology
  • Biomarkers
  • Machine Learning

Background:

  • Gastric cancer (GC) screening via endoscopy faces challenges due to high number-needed-to-scope (NNS).
  • Routine blood tests (RBT) have limited direct utility for GC screening.
  • Machine learning offers potential for repurposing RBT data for risk stratification.

Purpose of the Study:

  • To develop and validate a machine-learning-integrated biomarker (RBT-GC) for opportunistic gastric cancer risk stratification.
  • To optimize endoscopy resource allocation by identifying high-risk individuals.

Main Methods:

  • Analysis of 20 years of retrospective data (2000-2020) from the Hong Kong Hospital Authority.
  • Training a machine learning model using 24 RBT and demographic features from 210,463 subjects (2000-2015).
  • Validation of the RBT-GC model on an independent cohort of 90,479 subjects (2016-2020).

Main Results:

  • The RBT-GC model stratified the validation cohort into low-risk (0.3% GC prevalence), intermediate-risk (1.9%), and high-risk (14.0%) categories.
  • The model detected 12x more GC cases than CEA and 30x more than CA19.9.
  • Opportunistic RBT-GC risk stratification reduced the NNS from 44 to 7 in the high-risk group.

Conclusions:

  • Machine learning can repurpose standard blood tests into an effective triage tool for gastric cancer screening.
  • The RBT-GC biomarker offers an affordable, scalable solution to alleviate endoscopic burdens.
  • Optimized endoscopy resource allocation through RBT-GC can improve gastric cancer detection rates.

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