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.