# 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

## TL;DR

A new model using postoperative biomarker data improves prediction of gastric cancer recurrence after surgery.

## Contribution

A dynamic prediction model incorporating time-dependent biomarkers outperforms static models for recurrence risk.

## Key findings

- Landmarking 1.5 model showed superior performance across four prediction metrics.
- Time-dependent biomarkers improved recurrence prediction accuracy.
- Models using postoperative data better distinguish high- and low-risk patients.

## Abstract

Postoperative recurrence prediction models for gastric cancer often rely on preoperative or immediate postoperative data, overlooking time-dependent biomarkers from follow-up visits. By incorporating longitudinal biomarker data through a landmarking approach, this study aims to enhance recurrence risk prediction.

This multicenter study included patients who underwent curative surgery for stage II–III gastric cancer from January 2010 to December 2016 in three hospitals in Tokyo, Japan. Their demographic, clinical, and biomarker data were collected from medical records. Biomarkers were collected at surgery and 3, 6, 9, and 12 months postoperatively. Three prediction models—baseline model, landmarking 1.0, and landmarking 1.5—were developed and compared in terms of their prediction accuracy using four measures: concordance probability, calibration plot, Kaplan–Meier curves stratified with predicted risk, and Net Reclassification Improvement. The models aimed to predict recurrence within three years after surgery, with predictions made one year postsurgery.

The study included 274 patients with gastric cancer, with 62 (22.6%) events occurring within three years. As a result of the variable selection process, lymphatic venous Invasion (LVI), pathological T (pT) stage, pathological N (pN) stage, and baseline prognostic nutritional index (PNI) were chosen. Additionally, in landmarking 1.0 and 1.5, S1 treatment status and PNI-change were also selected as time-dependent predictors. Landmarking 1.5, which incorporates time-dependent biomarkers until one year postsurgery, showed superior performance to the other models in all four measures.

Prediction models incorporating postoperative information could serve as a decision-making tool in clinical practice to more precisely distinguish between patients with high and low risk of recurrence.

Dynamic prediction models using postoperative data outperformed standard models relying on surgery-time information in predicting gastric cancer recurrence within 3 years, showing superior accuracy across four metrics.