Nomograms of risk prediction and prognosis for the T1-T2 stage gastric cancer with lymph node metastasis: a population-based study.

BACKGROUND AND AIMS

Lymph node metastasis plays a crucial role in determining the appropriate treatment approach for patients with gastric cancer (GC), particularly those in the T1-T2 stage. Currently available diagnostic strategies for GC with lymph nodes have limited accuracy. The present research aimed to create and validate diagnostic and prognostic nomograms specifically tailored for the T1-T2 stage GC patients with LNM.

METHODS

We derived clinicopathological characteristics of patients diagnosed with GC from the Surveillance, Epidemiology, and End Results (SEER) database. We utilized univariate and multivariate logistic analyses to examine the risk factors linked with the occurrence of lymph node metastasis (LNM) in GC patients within the T1-T2 stage. Furthermore, the prognostic factors related to the T1-T2 stage GC patients with LNM were explored by univariate and multivariate cox analyses. Two nomograms were built by the risk factors screened above.

RESULTS

Ultimately, our study included 5,350 patients with T1-T2 stage GC. After identifying age, T stage, tumor size, primary site, grade, and histological type as risk factors for the LNM occurrence, we successfully developed a diagnostic nomogram utilizing these variables. Age, T stage, M stage, tumor size, primary site, grade, radiation, surgery, and chemotherapy were all independent prognostic factors that related to the T1 - T2 GC patients with LNM. The results of the AUC, calibration curve and decision curve analysis (DCA) showed excellent calibration performance and clinical applicability of the two nomograms. The Kaplan-Meier (K-M) curves clearly demonstrated a notable distinction in overall survival between low-risk and high-risk groups, highlighting the prognostic significance of the nomogram.

CONCLUSION

The establishment and validation of the two nomograms for T1-T2 GC patients with LNM were successful, serving as valuable tools for clinical decision-making and the formulation of personalized treatment approaches.