Individualized prediction tool for patients with metastatic gastric signet cell carcinoma.

Gastric signet cell carcinoma (GSRC) is a special type of gastric cancer. Due to its high metastatic rate patients usually have a poor prognosis. Accurately predicting the survival time of these patients and selecting the best treatment strategy are urgent clinical questions. All patients included in the study were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The demographic and clinical information of these patients was subjected to Cox regression analysis using SPSS software. The risk factors that were subsequently screened were used to construct and validate the nomogram through R software. A total of 1003 patients were included in this study. After Cox regression analysis, six variables (T stage, surgery status, chemotherapy, metastases status of bone, liver and lung) were identified as risk factors that independently influenced the patients' prognosis. These variables were used to construct a nomogram, which was shown to have good predictive power by receiver operating characteristic curves and calibration curves. The AUC values for 3-, 6-, and 12-month OS prediction were 0.845, 0.793, and 0.751 in the training cohort, and 0.800, 0.735, and 0.693 in the validation cohort and decision curve analysis indicating that this nomogram could result in a good clinical benefit for the patient. We have successfully developed and validated a nomogram that accurately predicts 3-, 6- and 12-month overall survival in patients with metastatic GSRC. This can provide a theoretical basis for clinical practice and help clinicians to choose the best treatment strategy for their patients.