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Machine learning-based dynamic CEA trajectory and prognosis in gastric cancer

  • 0Department of General Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, 26 Yuancun Erheng Road, Guangzhou, 510655, China. chenyhe@mail2.sysu.edu.cn.
Bmc Cancer +

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July 27, 2025

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July 27, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Dynamic carcinoembryonic antigen (CEA) levels, not just static values, predict gastric cancer prognosis. Higher CEA trajectories indicate worse survival, necessitating closer patient monitoring.

Area Of Science

  • Oncology
  • Biomarkers
  • Cancer Research

Background

  • Carcinoembryonic antigen (CEA) is a known prognostic marker in gastric cancer.
  • The prognostic significance of dynamic CEA level changes over time remains under-explored.

Purpose Of The Study

  • To investigate the prognostic value of perioperative CEA level trajectories in gastric cancer patients.
  • To identify distinct CEA patterns and their association with patient survival outcomes.

Main Methods

  • Analysis of perioperative CEA levels (pre-surgery, early post-surgery, late post-surgery) in 578 gastric cancer patients.
  • K-means clustering to define CEA trajectories.
  • Kaplan-Meier analysis and Cox regression to assess survival differences.

Main Results

  • Three distinct CEA trajectories (high, medium, low) were identified.
  • Higher CEA trajectories correlated with significantly worse disease-free survival (DFS) and overall survival (OS).
  • The high CEA trajectory group showed over double the mortality risk compared to the low trajectory group (HR 2.64).

Conclusions

  • Dynamic CEA trajectories are significant independent prognostic factors in gastric cancer.
  • Patients with higher CEA trajectories require enhanced monitoring due to poorer prognosis.
  • Monitoring CEA trends offers valuable insights beyond static measurements for gastric cancer management.

Keywords:

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