Nomogram for predicting 10-year postoperative recurrence of stage I gastric cancer.

Background: With the advancement of various auxiliary examination techniques, the detection rate of stage I gastric cancer has gradually increased, and its clinical first-choice treatment is surgery. Although patients with stage I gastric cancer generally have a good postoperative survival rate, there is still a certain probability of recurrence. Given the large number of gastric cancer cases, there is a vast population of patients with stage I disease. We are aiming to identify the risk factors for postoperative recurrence of stage I gastric cancer and to establish a reliable predictive model to assess the risk of recurrence in the population for clinical practice.

Methods: In this retrospective cohort study, we utilized the Surveillance, Epidemiology, and End Results (SEER) database to investigate predictive factors for recurrence among stage I gastric cancer patients who underwent curative gastrectomy between 2000 and 2018. The cohort was divided into training and validation sets for the development and validation of a nomogram. Prognostic factors were evaluated through univariate and multivariate Cox regression analyses. Significant variables identified by the concordance index (C-index) and calibration plots were used to construct nomograms predicting the probability of 5- and 10-year recurrence.

Results: Risk factors for recurrence included sex, age, race, histology, tumor size, American Joint Committee on Cancer Tumor (AJCC T) and primary site, which were used to construct the nomogram. The C-index for both the training and validation cohorts indicated that the nomogram possessed good calibration and discrimination abilities in predicting the probability of 5- and 10-year recurrence after curative surgery for stage I gastric cancer.

Conclusions: This study established a reliable predictive model for recurrence following curative gastrectomy in stage I gastric cancer based on a population cohort. The findings of this study have the potential to significantly impact clinical practice by providing clinicians with tools for personalized risk assessment and for making informed treatment decisions.