Background: Inflammatory and nutritional biomarkers are associated with survival in gastric cancer; however, their incremental prognostic value beyond routinely available clinical factors and the stability of their associations over time remain uncertain. We compared six routinely available biomarkers within a common clinically adjusted framework and evaluated their associations with overall survival (OS), model performance, temporal variation, and internal robustness. Methods: This retrospective single-center cohort study included 255 patients with gastric adenocarcinoma. A prespecified parsimonious Cox model comprising age, sex, Eastern Cooperative Oncology Group performance status, and metastatic disease at diagnosis served as the clinical reference model. The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), prognostic nutritional index (PNI), and hemoglobin–albumin–lymphocyte–platelet (HALP) score were added separately. Model fit and discrimination, multiple-testing-adjusted associations, time-varying effects, bootstrap internal validation, and influence-diagnostic sensitivity analyses were assessed. Results: During follow-up, 227 patients (89.0%) died. Median OS was 11.66 months (95% confidence interval [CI], 9.63–13.47), and reverse Kaplan–Meier median follow-up was 88.51 months (95% CI, 69.72–110.59). After adjustment for the prespecified clinical factors, higher NLR, PLR, and SII were associated with increased mortality, whereas higher LMR, PNI, and HALP were associated with lower mortality; all associations remained significant after Benjamini–Hochberg correction (q ≤ 0.007). The clinical model had a C-index of 0.678. Biomarker addition yielded C-indices ranging from 0.682 to 0.695, with the largest numerical increase observed for PNI (ΔC-index, +0.017). The HALP-extended model yielded the numerically lowest Akaike information criterion. Exploratory time-specific estimates suggested stronger biomarker–mortality associations during early follow-up, although the interaction tests did not remain significant after multiplicity correction. Bootstrap-corrected C-indices were 0.689 for PNI, 0.686 for HALP, and 0.682 for SII, with low optimism and successful convergence of all 1000 resamples. Influence-diagnostic sensitivity analyses preserved the direction and statistical significance of these associations. Conclusions: Inflammatory and nutritional biomarkers provided additional prognostic information beyond the prespecified clinical factors. Although all six biomarkers remained associated with OS when evaluated separately, their incremental contribution to overall discrimination was modest. PNI and HALP yielded the most favorable numerical model-performance estimates; however, the absolute differences were modest, and formal paired bootstrap comparisons identified no significant differences between the biomarker-extended models. External and longitudinal validation is required before routine prognostic implementation.

Background: Prognostic assessment after neoadjuvant chemotherapy and curative gastrectomy remains challenging in patients with gastric adenocarcinoma because postoperative outcomes are influenced by both pretreatment disease burden and pathological response. We aimed to develop and internally validate a clinicopathological prognostic model and a simple postoperative risk classification for predicting disease-free survival (DFS). Methods: This single-center retrospective cohort study included patients with gastric adenocarcinoma who underwent neoadjuvant chemotherapy followed by curative gastrectomy. Pretreatment clinicopathological variables, Becker tumor regression grade (TRG), and serum tumor markers were evaluated. Logistic regression was used to identify predictors of favorable pathological response, whereas Cox proportional hazards regression was performed to identify independent prognostic factors for disease-free survival (DFS). Sequential prognostic models were developed and internally validated using 1000 bootstrap resamples. A simplified postoperative clinicopathological risk classification based on pretreatment clinical N stage and Becker tumor regression grade was additionally developed to facilitate clinical interpretation and postoperative risk stratification. Results: A total of 109 patients were included. Favorable pathological response (Becker TRG1–2) was achieved in 68 patients (62.4%), whereas 41 patients (37.6%) had minimal or no pathological response (TRG3). In multivariable logistic regression analysis, pretreatment clinical T stage (cT4 vs. cT1–3) and clinical N stage (cN2–3 vs. cN0–1) were independently associated with a lower likelihood of achieving a favorable pathological response. For disease-free survival, pretreatment clinical N stage, Becker tumor regression grade, and log10-transformed CA19-9 remained independent prognostic factors in the multivariable Cox model. Sequential model development demonstrated progressive improvement in model discrimination, with the optimism-corrected Harrell’s C-index increasing from 0.697 for the clinical N stage model to 0.770 for the final model incorporating clinical N stage, Becker tumor regression grade, and CA19-9. Bootstrap internal validation demonstrated minimal optimism, and calibration analysis showed good agreement between predicted and observed disease-free survival. A simple postoperative clinicopathological risk classification successfully stratified patients into distinct prognostic groups. Conclusions: A clinicopathological prognostic model integrating pretreatment clinical N stage, Becker tumor regression grade, and serum CA19-9 demonstrated improved prognostic discrimination for disease-free survival compared with clinical N stage alone. The derived postoperative risk classification may provide a simple framework for postoperative risk stratification and could assist in individualizing postoperative surveillance. External validation is warranted before routine clinical implementation.