ABSTRACT
BACKGROUND: The prognostic value of quantitative assessments of the number of retrieved lymph nodes (RLNs) in gastric cancer (GC) patients needs further study. AIM: To discuss how to obtain a more accurate count of metastatic lymph nodes (MLNs) based on RLNs in different pT stages and then to evaluate patient prognosis. METHODS: This study retrospectively analyzed patients who underwent GC radical surgery and D2/D2+ LN dissection at the Cancer Hospital of Harbin Medical University from January 2011 to May 2017. Locally weighted smoothing was used to analyze the relationship between RLNs and the number of MLNs. Restricted cubic splines were used to analyze the relationship between RLNs and hazard ratios (HRs), and X-tile was used to determine the optimal cutoff value for RLNs. Patient survival was analyzed with the Kaplan-Meier method and log-rank test. Finally, HRs and 95% confidence intervals were calculated using Cox proportional hazards models to analyze independent risk factors associated with patient outcomes. RESULTS: A total of 4968 patients were included in the training cohort, and 11154 patients were included in the validation cohort. The smooth curve showed that the number of MLNs increased with an increasing number of RLNs, and a nonlinear relationship between RLNs and HRs was observed. X-tile analysis showed that the optimal number of RLNs for pT1-pT4 stage GC patients was 26, 31, 39, and 45, respectively. A greater number of RLNs can reduce the risk of death in patients with pT1, pT2, and pT4 stage cancers but may not reduce the risk of death in patients with pT3 stage cancer. Multivariate analysis showed that RLNs were an independent risk factor associated with the prognosis of patients with pT1-pT4 stage cancer (P = 0.044, P = 0.037, P = 0.003, P < 0.001). CONCLUSION: A greater number of RLNs may not benefit the survival of patients with pT3 stage disease but can benefit the survival of patients with pT1, pT2, and pT4 stage disease. For the pT1, pT2, and pT4 stages, it is recommended to retrieve 26, 31 and 45 LNs, respectively.
ABSTRACT
BACKGROUND: Inflammatory indices are considered to be potential prognostic biomarkers for patients with gastric cancer (GC). However, there is no evidence defining the prognostic significance of inflammatory indices for GC with different tumor infiltrative pattern (INF) types. AIM: To evaluate the significance of inflammatory indices and INF types in predicting the prognosis of patients with GC. METHODS: A total of 962 patients who underwent radical gastrectomy were retrospectively selected for this study. Patients were categorized into the expansive growth type (INFa), the intermediate type (INFb), and the infiltrative growth type (INFc) groups. The cutoff values of inflammatory indices were analyzed by receiver operating characteristic curves. The Kaplan-Meier method and log-rank test were used to analyze overall survival (OS). The chi-square test was used to analyze the association between inflammatory indices and clinical characteristics. The independent risk factors for prognosis in each group were analyzed by univariate and multivariate analyses based on logistic regression. Nomogram models were constructed by R studio. RESULTS: The INFc group had the worst OS (P < 0.001). The systemic immune-inflammation index (P = 0.039) and metastatic lymph node ratio (mLNR) (P = 0.003) were independent risk factors for prognosis in the INFa group. The platelet-lymphocyte ratio (PLR) (P = 0.018), age (P = 0.026), body mass index (P = 0.003), and postsurgical tumor node metastasis (pTNM) stage (P < 0.001) were independent risk factors for prognosis in the INFb group. The PLR (P = 0.021), pTNM stage (P = 0.028), age (P = 0.021), and mLNR (P = 0.002) were independent risk factors for prognosis in the INFc group. The area under the curve of the nomogram model for predicting 5-year survival in the INFa group, INFb group, and INFc group was 0.787, 0.823, and 0.781, respectively. CONCLUSION: The outcome of different INF types GC patients could be assessed by nomograms based on different inflammatory indices and clinicopathologic features.
ABSTRACT
BACKGROUND: Patients with pathological stages T1N2-3 (pT1N2-3) and pT3N0 gastric cancer (GC) have not been routinely included in the target population for postoperative chemotherapy according to the Japanese Gastric Cancer Treatment Guideline, and their prognosis is significantly different. AIM: To identify the high-risk patients after radical surgery by analyzing biomarkers and clinicopathological features and construct prognostic models for them. METHODS: A total of 459 patients with pT1N2-3/pT3N0 GC were retrospectively selected for the study. The Chi-square test was used to analyze the differences in the clinicopathological features between the pT1N2-3 and pT3N0 groups. The Kaplan-Meier analysis and log-rank test were used to analyze overall survival (OS). The independent risk factors for patient prognosis were analyzed by univariate and multivariate analyses based on the Cox proportional hazards regression model. The cutoff values of continuous variables were identified by receiver operating characteristic curve. The nomogram models were constructed with R studio. RESULTS: There was no statistically significant difference in OS between the pT1N2-3 and pT3N0 groups (P = 0.374). Prealbumin (P = 0.040), carcino-embryonic antigen (CEA) (P = 0.021), and metastatic lymph node ratio (mLNR) (P = 0.035) were independent risk factors for prognosis in the pT1N2-3 group. Age (P = 0.039), body mass index (BMI) (P = 0.002), and gastrectomy (P < 0.001) were independent risk factors for prognosis in the pT3N0 group. The area under the curve values of the nomogram models for predicting the 5-year prognosis of the pT1N2-3 group and pT3N0 group were 0.765 and 0.699, respectively. CONCLUSION: Nomogram model combining prealbumin, CEA, and mLNR levels can be used to predict the prognosis of pT1N2-3 GC. Nomogram model combining age, BMI, and gastrectomy can be used to predict the prognosis of pT3N0 GC.
