Nomogram for prognosis prediction in metastatic pancreatic cancer patients undergoing intra-arterial infusion chemotherapy: incorporating immune-inflammation scores and coagulation indicators.

Abstract

Background: Pancreatic cancer is one of the most malignant tumors with an inferior prognosis. This study aims to determine the prognostic significance of immune-inflammatory scores and coagulation indices in patients with metastatic pancreatic cancer(MPC) and develop a predictive nomogram.

Methods: This study retrospectively analyzed the clinical data of 384 patients with MPC who underwent intra-arterial infusion chemotherapy (IAIC). Patients were randomly divided into training and validation cohorts. Firstly, the optimal cutoff values for continuous variables were obtained in the training cohort. Then, survival analysis was performed to evaluate the impact of immune-inflammatory scores neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and coagulation indicators prothrombin time (PT), fibrinogen (FIB), and D-dimer on the overall survival (OS) of patients. Next, univariate analysis was utilized to identify prognostic factors, and a stepwise regression method was employed for variable selection to construct a nomogram based on the Cox proportional hazards model. Additionally, the predictive performance of the nomogram was assessed by the concordance index (C-index), the area under the ROC curve (AUC), and calibration curves. Finally, patients were stratified into risk groups based on the total score of the nomogram.

Results: The Kaplan-Meier survival curves indicated that immune-inflammatory scores NLR, PLR, SII, and coagulation indicators PT, FIB, and D-dimer were associated with OS. Through Cox regression analysis, a nomogram was ultimately constructed incorporating NLR, PLR, PT, alkaline phosphatase (ALP), carbohydrate antigen 125 (CA125), age, and ablation. The model demonstrated good discriminative ability, with a C-index of 0.722, and the AUC values at 6- and 12-month OS predictions were 0.828 and 0.851 in the training cohort, while in the validation cohort, the corresponding AUC values were 0.754 and 0.791, respectively. The calibration curves showed a good fit, confirming the stability of the model. A cutoff value of 353.3 was identified as optimal for risk stratification, with a statistically significant difference in OS between the high- and low-risk groups.

Conclusion: The nomogram based on immune-inflammatory scores, coagulation indicators, and other clinicopathological factors can effectively predict the OS of patients with MPC.