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Development of a dynamic prediction model with the inclusion of time-dependent inflammatory biomarker enhances recurrence prediction after curative surgery for stage II or III gastric cancer

  • 0Graduate School of Medicine, International University of Health and Welfare, 4-1-26, Akasaka, Minato-ku, Tokyo 107-8402, Japan.
Japanese Journal of Clinical Oncology +

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May 23, 2025

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May 23, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study improves gastric cancer recurrence prediction by using time-dependent biomarkers. Landmarking 1.5, incorporating postoperative data up to one year, demonstrated superior accuracy in identifying high-risk patients.

Area Of Science

  • Oncology
  • Biomarkers
  • Surgical Oncology

Background

  • Gastric cancer recurrence prediction models often lack dynamic postoperative data.
  • Longitudinal biomarker data from follow-up visits are crucial for accurate risk assessment.

Purpose Of The Study

  • To enhance gastric cancer recurrence risk prediction by incorporating time-dependent biomarkers.
  • To develop and compare prediction models using a landmarking approach.

Main Methods

  • A multicenter study of 274 patients with stage II-III gastric cancer undergoing curative surgery.
  • Development of three models: baseline, landmarking 1.0, and landmarking 1.5, using preoperative and longitudinal postoperative biomarker data.
  • Comparison of model performance using concordance probability, calibration plots, Kaplan-Meier curves, and Net Reclassification Improvement.

Main Results

  • Landmarking 1.5, which includes time-dependent biomarkers up to one year post-surgery, showed superior predictive performance.
  • Key predictors included lymphatic venous invasion (LVI), pathological T (pT) and N (pN) stages, baseline prognostic nutritional index (PNI), S1 treatment status, and PNI-change.
  • 62 out of 274 patients (22.6%) experienced recurrence within three years.

Conclusions

  • Prediction models integrating postoperative biomarker changes can significantly improve clinical decision-making.
  • These models help precisely differentiate gastric cancer patients with high versus low recurrence risk.
  • Dynamic assessment of biomarkers post-surgery enhances the accuracy of recurrence prediction.

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