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A Step Toward a Global Consensus on Gastric Cancer Resectability Integrating Artificial Intelligence-Based Consensus Modelling

  • 0Department of Surgical Oncology, Medical University of Lublin, 20-080 Lublin, Poland.
Cancers +

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August 28, 2025

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August 28, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Artificial intelligence (AI) can help standardize gastric cancer (GC) resectability criteria among surgeons globally. The Intercontinental Criteria of Resectability for Gastric Cancer (ICRGC) project used AI to align expert opinions and improve decision-making in complex cases.

Area Of Science

  • Surgical Oncology
  • Artificial Intelligence in Medicine
  • Gastric Cancer Management

Background

  • Surgical resection is key for curative treatment of locally advanced gastric cancer (GC).
  • Global variability exists in defining resectability, especially with multivisceral invasion, positive peritoneal cytology (CY1), or oligometastatic disease.
  • The Intercontinental Criteria of Resectability for Gastric Cancer (ICRGC) project was initiated to address these challenges.

Purpose Of The Study

  • To develop standardized resectability criteria for gastric cancer (GC) using expert surgical input and artificial intelligence (AI).
  • To assess the concordance between expert surgeons and AI-driven recommendations in complex GC resectability scenarios.
  • To evaluate AI's potential to harmonize global surgical practices in GC management.

Main Methods

  • A two-stage prospective survey was conducted among 58 surgical oncologists at the 2024 European Gastric Cancer Association (EGCA) meeting.
  • Participants completed a questionnaire on resectability, strategy, and quality metrics, followed by a review of AI-generated responses.
  • Concordance between human and AI responses was analyzed, and changes in surgeon opinions post-AI exposure were statistically evaluated.

Main Results

  • High agreement was found between surgeons and AI on distinguishing technical from oncological resectability (79%).
  • Significant alignment was observed in complex cases like cT4b (61% support for high-volume centers), CY1 (54%), and N3 nodal disease (63%).
  • AI exposure led to guideline-consistent shifts in surgeon decisions, with 27.1% changing answers, notably in surgical margin definition (p=0.015) and anatomical criteria (p<0.05).

Conclusions

  • AI-driven consensus modeling effectively replicates expert reasoning in complex surgical oncology cases.
  • The ICRGC project demonstrates AI's potential to serve as a catalyst for harmonizing global surgical practices in gastric cancer.
  • AI-supported consensus modeling can complement expert judgment, enhancing decision-making consistency in ambiguous or controversial GC resectability scenarios.

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