A Nomogram for Individual Prediction of Poor Prognosis After Radical Surgery in Patients with Primary Pancreatic Duct Adenocarcinoma

Abstract

Background: Pancreatic cancer is a highly malignant tumor characterized by poor prognosis. TNM stage cannot always provide accurate prediction of prognosis, which is vital for individualized treatment. Therefore, a novel way to identify patients with poor prognosis after radical surgery is urgently needed. Material/Methods: The nomogram was established based on a discovery cohort that included 554 patients with PDAC who had received radical surgery from 2012 to 2016. The clinicopathological data were collected. Poor prognosis was evaluated using 25 features, in which appropriate features for a prediction model were identified. A prediction model incorporating the selected features was established. The discriminative capacity was assessed by C-index, calibration by calibration plot, and clinical usefulness by decision curve. The bootstrapping approach was used to perform internal validation. Results: Characteristics included in the nomogram were coronary artery disease and stroke history, elevated CA125, AJCC stage >II, R0 resection, operating time >6 h, poor differentiation, nerve invasion, length of stay >30 days, and postoperative complications. A C-index of 0.713 indicated good discrimination of the prediction model, and the calibration curve showed acceptable calibration. Survival analysis showed that this model had better discriminative capacity than the AJCC staging system and could distinguish relatively good prognosis from poor prognosis in patients at stage II (especially IIa) and IV. Conclusions: Our study presents a valid and practical model to predict prognosis of pancreatic cancer patients, which contributes to individualized therapy by assisting surgeons to predict poor prognosis in patients who received radical surgery.

Development and Validation of a Novel Model for Predicting the 5-Year Risk of Type 2 Diabetes in Patients with Hypertension: A Retrospective Cohort Study

“…The LASSO logistic regression algorithm can be used for regression analysis of high-dimensional data [ 15 ]. In this study, the LASSO logistic regression algorithm was used to select the most important prediction features in the derivation data set [ 16 ]. All category variables are converted to dummy variables.…”