ArticleJapanese journal of clinical oncology2025
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.
Article in Japanese journal of clinical oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Gastric Cancer: Diagnostic, Prognostic, and Predictive Developments, Evidence Maturity, and Translational Challenges.Cancer reports (Hoboken, N.J.) · 2026Review
- Development of a Clinicopathological Prognostic Model and Risk Classification to Predict Disease-Free Survival in Patients with Gastric Adenocarcinoma Following Neoadjuvant Chemotherapy and Curative Gastrectomy.Current oncology (Toronto, Ont.) · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundPostoperative 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.
methodsThis 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.
resultsThe 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.
conclusionsPrediction 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.
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