Nomograms predicting prognosis of patients with pathological stages T1N2-3 and T3N0 gastric cancer

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.

The predictive effect of nomogram constructed based on peripheral blood biochemical indicators on the prognosis of patients with gastric cancer after surgery

“…The nomogram we constructed added SLR and GPR as one of the evaluation factors compared to the traditional TNM staging system. It was demonstrated that elevation of these two factors was associated with poor prognosis in gastric cancer patients, which is consistent with the results of some previous studies [23][24][25][26] . In gastric cancer patients treated with rst-line chemotherapy, some previous studies have shown that SLR and GPR are independent prognostic indicators [27][28] .…”

Prognostic artificial intelligence model to predict 5 year survival at 1 year after gastric cancer surgery based on nutrition and body morphometry

“…3 Many prognostic models using various nomograms, scoring systems, and artificial intelligence (AI) models, were developed to predict the overall survival of patients after surgery. [4][5][6][7] However, none of these models have been used extensively in clinical practices due to the limited accuracy in predicting the survival of patients in various situations.…”

“…We hypothesize that one of the main reasons for the inaccuracy is the limited number of prognostic variables available to build an adequate model. For simplicity and the uniform application of the prognostic models, prior models depended on a few known variables such as the TNM staging system, age, sex, tumour location, tumour histology, and the extent of the surgery 3,6,8 . However, recent studies demonstrated that additional variables could affect patient survival in gastric cancer, such as nutrition, sarcopenia, anaemia, and interval changes in these variables between pre‐operation and post‐operation 9,10 .…”

“…For simplicity and the uniform application of the prognostic models, prior models depended on a few known variables such as the TNM staging system, age, sex, tumour location, tumour histology, and the extent of the surgery. 3 , 6 , 8 However, recent studies demonstrated that additional variables could affect patient survival in gastric cancer, such as nutrition, sarcopenia, anaemia, and interval changes in these variables between pre‐operation and post‐operation. 9 , 10 Based on these findings, we assumed that a higher accuracy of prognostication could be achieved by an in‐depth analysis of as many variables as possible that could be easily derived from clinical data and body morphometry data derived from imaging.…”