Objective:The purpose of this study was to develop a dynamic prediction model for patients with gastric signet ring cell cancer (GSRCC)following laparoscopic radical gastrectomy in order to improve the precision and usefulness of prognoses prediction for overall survival and disease-free survival.Methods:From 2011 to 2018, 914 National Cancer Center patients participated in the study. To find independent prognostic indicators and create a prognostic nomogram model, univariate and multivariate regression analyses were performed. Calibration curves, receiver operating characteristic curves, and consistency indices were used to assess the model's performance. To make clinical application more convenient, two web-based prediction tools were created.Results:A training set of 639 cases and a validation set of 275 instances were randomly selected from among the patients. Important predictive variables such as age, tumor size, location, pN and pT staging, and postoperative chemotherapy were all incorporated in the model (all P<0.05). The model's consistency index and area under the receiver operating characteristic curves were both higher than 0.7, and the calibration curves demonstrated a good fit between the expected and actual values, indicating high accuracy and consistency in postoperative survival prediction for patients with gastric signet ring cell carcinoma. Conclusion:We successfully developed two dynamic prediction models in this study, which improved its clinical practicability using web-based tools and is anticipated to be crucial to clinical practice going forward.

Objective:The purpose of this study was to develop a dynamic prediction model for patients with gastric signet ring cell cancer (GSRCC)following laparoscopic radical gastrectomy in order to improve the precision and usefulness of prognoses prediction for overall survival and disease-free survival.Methods:From 2011 to 2018, 914 National Cancer Center patients participated in the study. To find independent prognostic indicators and create a prognostic nomogram model, univariate and multivariate regression analyses were performed. Calibration curves, receiver operating characteristic curves, and consistency indices were used to assess the model's performance. To make clinical application more convenient, two web-based prediction tools were created.Results:A training set of 639 cases and a validation set of 275 instances were randomly selected from among the patients. Important predictive variables such as age, tumor size, location, pN and pT staging, and postoperative chemotherapy were all incorporated in the model (all P<0.05). The model's consistency index and area under the receiver operating characteristic curves were both higher than 0.7, and the calibration curves demonstrated a good fit between the expected and actual values, indicating high accuracy and consistency in postoperative survival prediction for patients with gastric signet ring cell carcinoma. Conclusion:We successfully developed two dynamic prediction models in this study, which improved its clinical practicability using web-based tools and is anticipated to be crucial to clinical practice going forward.

Liver transplantation makes it possible to cure portal hypertension thoroughly.A new pattern of combined application of multiple treatments based on liver transplantation has emerged.Conventional surgical treatments such as splenectomy,devascularization and transjugular intrahepatic portosystemic shunting have important effects on the prognosis of liver transplant recipients.Portal vein stenosis,thrombosis as well as small-for-size liver graft lead to post-transplant portal hypertension and liver dysfunction.The author in this article provides an overview of the effects of various conventional treatments on the prognosis of recipients and the treatment of portal hypertension after liver transplantation,aiming to seek for optimal strategy.