Background Survival times differ among patients with advanced gastric carcinoma. A precise and universal prognostic evaluation strategy has not yet been established. The current study aimed to construct a prognostic scoring model for mortality risk stratification in patients with advanced gastric carcinoma. Methods Patients with advanced gastric carcinoma from two hospitals (development and validation cohort) were included. Cox proportional hazards regression analysis was conducted to identify independent risk factors for survival. A prognostic nomogram model was developed using R statistics and validated both in bootstrap and external cohort. The concordance index and calibration curves were plotted to determine the discrimination and calibration of the model, respectively. The nomogram score and a simplified scoring system were developed to stratify patients in the two cohorts. Results Development and validation cohort was comprised of 401 and 214 gastric cancer patients, respectively. Mucinous or non-mucinous histology, ECOG score, bone metastasis, ascites, hemoglobin concentration, serum albumin level, lactate dehydrogenase level, carcinoembryonic antigen level, and chemotherapy were finally incorporated into prognostic nomogram. The concordance indices were 0.689 (95% CI: 0.664 ~ 0.714) and 0.673 (95% CI: 0.632 ~ 0.714) for bootstrap and external validation. 100 and 200 were set as the cut-off values of nomogram score, patients in development cohort were stratified into low-, intermediate- and high-risk groups with median overall survival time 15.8 (95% CI: 12.2 ~ 19.5), 8.4 (95% CI: 6.7 ~ 10.2), and 3.9 (95% CI: 2.7 ~ 5.2) months, respectively; the cut-off values also worked well in validation cohort with different survival time in subgroups. A simplified model was also established and showed good consistency with the nomogram scoring model in both of development and validation cohorts. Conclusion The prognostic scoring model and its simplified surrogate can be used as tools for mortality risk stratification in patients with advanced gastric carcinoma.

Background To evaluate whether the addition of taxanes to platinum and fluoropyrimidines in adjuvant chemotherapy would result in longer survival than platinum plus fluoropyrimidines in gastric cancer patients who received D2 gastrectomy. Methods Data of patients with gastric adenocarcinoma who received D2 gastrectomy and adjuvant chemotherapy with platinum plus fluoropyrimidines or taxanes, platinum plus fluoropyrimidines was retrospectively collected and analyzed. 1:1 Propensity score matching analysis was used to balance baseline characteristics between two groups. Survival curves were estimated using Kaplan-Meier method, and the differences were compared using the log-rank test. Results Four hundred twenty-five patients in the platinum plus fluoropyrimidines group and 177 patients in the taxanes, platinum plus fluoropyrimidines group were included into analysis. No statistical differences in disease-free survival and overall survival were observed between two groups. After propensity score matching, 172 couples of patients were matched, the baseline characteristics were balanced. The median disease-free survival were 15.8 months (95% CI, 9.3~22.4) in the platinum plus fluoropyrimidines group and 22.6 months (95% CI, 15.9~29.4) in the taxanes, platinum plus fluoropyrimidines group (HR = 0.63; 95% CI, 0.48~0.85; P = 0.002). The median overall survival was 25.4 months for patients in the platinum plus fluoropyrimidines group (95% CI, 19.4~31.3) and 33.8 months (95% CI, 23.5~44.2) for those in the taxanes, platinum plus fluoropyrimidines group (HR = 0.68; 95% CI, 0.53-0.87; log-rank test, P = 0.002). Conclusions For gastric adenocarcinoma patients, the adjuvant triplet combination of taxanes, platinum, and fluoropyrimidines regimen after D2 gastrectomy was superior to platinum plus fluoropyrimidines regimen in disease-free survival as well as overall survival. Trial registration This project has been registered in the Chinese Clinical Trial Registry (ChiCTR1800019978).

Purpose This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as any-cause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC. Trial Registration ChiCTR Identifier: ChiCTR1800019978

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Background Gastric cancer liver metastasis (GCLM) patients usually accompany by abnormal serum liver function tests (LFTs) more or less; however, the prognostic value of LFTs is not fully understood. This study aimed to develop a liver chemistry score (LCS) based on LFTs and incorporate it into prognosis determination for GCLM patients who received palliative chemotherapy. Methods Data were derived from hospitalized GCLM patients in two general hospitals in China. LCS was generated based on the results of LFTs by LASSO regression. Cutoff value of the score was determined by restricted cubic spline. The score was then incorporated into Cox regression analysis to construct a predictive nomogram; the model was then evaluated internally and externally by AUC of time‐dependent receiver operating characteristic curves (ROC) and calibration curves. Results Three hundred and thirty‐six and 72 patients were included in development and validation cohort, respectively. LASSO regression analysis in development cohort finally reached a two‐parametric LCS calculated on AST and ALP levels as 0.03343515 × ln (AST, U/L) + 0.02687997 × ln (ALP, U/L), and 0.232 was set as optimal cutoff value. Patients in low (LCS < 0.232) or high (LCS ≥ 0.232) score group experienced different survival times; median OS was 13.54 (95% CI: 11.1–15.6) months in the low LCS group and 7.3 (6.6–9.3) months in the high LCS group ( p < 0.001). A nomogram including LCS and other clinical parameters was constructed and showed superior performance than model not including LCS. AUC of 6‐month ROC improved from 0.647 (95% CI: 0.584–0.711) to 0.699 (0.638–0.759) in internal validation, and 0.837 (0.734–0.940) to 0.875 (0.784–0.966) in external validation. Conclusions Liver chemistry score is useful in determining the prognosis of gastric cancer patients with liver metastasis and may be helpful to clinicians in decision‐making.