Nomogram prediction of overall survival and prognostic factors in Signet ring cell carcinoma: Based on the SEER database

Signet ring cell carcinoma (SRCC) is a special type of poorly differentiated adenocarcinoma. This study aimed to create a nomogram to predict survival outcomes in SRCC individuals. Joinpoint regression method was used to analyze cancer incidence trends and survival. Cox proportional hazards regression identified independent prognostic factors affecting survival, which were then used to construct the nomogram. The diagnostic accuracy of the model was tested with receiver operating characteristic curves. Clinical utility of the model was validated through decision curve analysis, and predictive accuracy was assessed with calibration curves. Using the Kaplan-Meier curve to evaluate the survival time under the special risk factor. SRCC was most commonly observed in the stomach, followed by the colon and rectum, with a decreasing trend in incidence. Our data came from the Surveillance, Epidemiology, and End Results database, including 10,570 patients who were randomly divided into a training set (n = 7402, 70%) and a validation set (n = 3168, 30%). Cox regression identified age, race/ethnicity, TNM staging, surgery, chemotherapy, and tumor size as independent prognostic factors, which were used to develop the nomogram. In the training group, the nomogram demonstrated strong predictive effectiveness for 1-year, 3-year, and 5-year survival, with areas under the receiver operating characteristic curve of 0.806 (95% confidence interval [CI]: 0.795-0.817), 0.811 (95% CI: 0.801-0.821), and 0.811 (95% CI: 0.800-0.822), respectively. In the validation group, the areas under the receiver operating characteristic curve were 0.810 (95% CI: 0.794-0.826) for 1-year, 0.828 (95% CI: 0.813-0.843) for 3-year, and 0.826 (95% CI: 0.810-0.842) for 5-year. The decision curve analysis confirmed the clinical applicability of the nomogram, and the calibration curves demonstrated strong consistency between predicted and observed survival. This study present a robust predictive model that offers a personalized and simplified approach to forecast survival outcomes in SRCC patients.