Objective:The previously integrated tumor-inflammation-nutrition(HI-GC)score has demonstrated dynamic monitoring value for recurrence and clinical decision-making in patients with postsurgical gastric cancer(GC).Howev...Objective:The previously integrated tumor-inflammation-nutrition(HI-GC)score has demonstrated dynamic monitoring value for recurrence and clinical decision-making in patients with postsurgical gastric cancer(GC).However,its failure to incorporate clinical-pathological factors limits its capacity for baseline risk assessment.This study aimed to develop a model that accurately identifies patients for adjuvant chemotherapy and dynamically evaluates recurrence risk.Methods:This retrospective,multicenter,longitudinal cohort study,spanning nine hospitals,included 7,085patients with GC post-radical gastrectomy.A baseline prognostic model was constructed using 117 machinelearning algorithms.The dynamic survival decision tree model(dy SDT)was employed to combine the baseline model with the HI-GC score.Results:A Cox regression model incorporating six factors was used to create a nomogram[Harrell's C-index:training cohort:0.765;95%confidence interval(95%CI):0.747,0.783;validation set:0.810;95%CI:0.747,0.783],including p T stage,positive lymph node ratio,p N stage,tumor size,age,and adjuvant chemotherapy.The best-performing machine learning model exhibited similar predictive accuracy to the nomogram(C-index:0.770).For the short-term dy SDT at 1 month,the mortality hazard ratios(HRs)for groups IIa,IIb,andⅢwere 2.61(95%CI:2.24,3.04),5.02(95%CI:4.15,6.06),and 8.88(95%CI:7.57,10.42),respectively,compared to group I.Stratified analysis revealed a significant interaction between adjuvant chemotherapy and overall survival in each subgroup(P<0.001).The long-term dy SDT at 1 year showed HRs of 3.25(95%CI:2.12,4.97)for group II,6.73(95%CI:4.29,10.56)for groupⅢa,and 17.88(95%CI:10.71,29.84)for groupⅢb.Conclusions:The dy SDT effectively stratifies mortality risk and provides valuable assistance in clinical decision-making after gastrectomy.展开更多
基金supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project(No.2023ZD0501400)the National Key R&D Program of China(No.2022YFC2505100)the National Natural Science Foundation of China(No.82202837,82421002 and 82350122)。
文摘Objective:The previously integrated tumor-inflammation-nutrition(HI-GC)score has demonstrated dynamic monitoring value for recurrence and clinical decision-making in patients with postsurgical gastric cancer(GC).However,its failure to incorporate clinical-pathological factors limits its capacity for baseline risk assessment.This study aimed to develop a model that accurately identifies patients for adjuvant chemotherapy and dynamically evaluates recurrence risk.Methods:This retrospective,multicenter,longitudinal cohort study,spanning nine hospitals,included 7,085patients with GC post-radical gastrectomy.A baseline prognostic model was constructed using 117 machinelearning algorithms.The dynamic survival decision tree model(dy SDT)was employed to combine the baseline model with the HI-GC score.Results:A Cox regression model incorporating six factors was used to create a nomogram[Harrell's C-index:training cohort:0.765;95%confidence interval(95%CI):0.747,0.783;validation set:0.810;95%CI:0.747,0.783],including p T stage,positive lymph node ratio,p N stage,tumor size,age,and adjuvant chemotherapy.The best-performing machine learning model exhibited similar predictive accuracy to the nomogram(C-index:0.770).For the short-term dy SDT at 1 month,the mortality hazard ratios(HRs)for groups IIa,IIb,andⅢwere 2.61(95%CI:2.24,3.04),5.02(95%CI:4.15,6.06),and 8.88(95%CI:7.57,10.42),respectively,compared to group I.Stratified analysis revealed a significant interaction between adjuvant chemotherapy and overall survival in each subgroup(P<0.001).The long-term dy SDT at 1 year showed HRs of 3.25(95%CI:2.12,4.97)for group II,6.73(95%CI:4.29,10.56)for groupⅢa,and 17.88(95%CI:10.71,29.84)for groupⅢb.Conclusions:The dy SDT effectively stratifies mortality risk and provides valuable assistance in clinical decision-making after gastrectomy.
