Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma.

OBJECTIVE: To screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it. METHODS: Clinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group. RESULTS: The patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model. CONCLUSION: The study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate.

Preoperative CA19-9 and GGT ratio as a prognostic indicator in ampullary carcinoma.

BACKGROUND AND AIMS: In recent years, more and more inflammatory indicators have been studied to predict the long-term survival of patients with ampullary carcinoma (AC) after radical resection, but these prognostic indicators are still controversial. Therefore, based on previous inflammation scores, this study established a novel, easily accessible, more feasible and more predictive prognostic marker [Carbohydrate antigen199 to gamma-glutamyltransferase ratio (CA19-9/GGT)] to better assess the prognostic significance in AC patients undergoing radical resection. METHODS: Overall survival (OS) and recurrence-free survival (RFS) were analyzed by Cox regression model. Correlation between CA19-9/GGT and clinicopathological variables were analyzed by Chi-squared test, Fisher ' s exact test, independent sample t test and Mann-Whitney U test. The performance of prognostic indexes is compared by the consistency index (C-index). The prediction accuracy of nomogram is further confirmed by calibration curve and decision curve analysis (DCA). RESULTS: CA19-9/GGT was an independent risk factor affecting OS [P = 0.001, hazard ratio (HR) 2.459, 95% confidence intervals (CI) 1.450-4.167] and RFS (P = 0.002, HR 2.333, 95% CI 1.371-3.971) in multivariate analysis. The optimal cut-off value of CA19-9/GGT was 0.14. In CA19-9/GGT correlation analysis, high risk group (> 0.14) was significantly associated with poor prognosis. The predictive performance of CA19-9/GGT (OS: C-index = 0.753, RFS: C-index = 0.745) was confirmed to be superior to other prognostic indicators according to the C-index. Compared with the simple AJCC staging system, the Nomogram prediction model (OS: C-index = 0.787, RFS: C-index = 0.795) established by the combination of CA19-9/GGT and AJCC 8th TNM staging system has higher prediction accuracy. CONCLUSIONS: CA19-9/GGT was an independent prognostic indicator after radical resection of AC. Incorporating CA19-9/GGT into the AJCC TNM staging system optimized the prediction accuracy of the TNM staging system, and further verified the predictive value of CA19-9/GGT.