AUTHOR=Yin Yi , Bai Luyuan , Mu Xinyue , Zhang Shan , Zhai Panpan 

TITLE=A multi-algorithm prognostic model combining inflammatory indices and surgical features in distal cholangiocarcinoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1625703

DOI=10.3389/fonc.2025.1625703

ISSN=2234-943X

ABSTRACT=BackgroundDerived neutrophil-to-lymphocyte ratio (dNLR) is an emerging blood-based inflammatory biomarker previously reported to have prognostic value in various malignancies. This study aimed to investigate the prognostic significance of dNLR in patients with distal cholangiocarcinoma (dCCA) after curative resection.MethodsClinicopathological data of patients with dCCA in our hospital from Jan.2014 to Jun.2024 was analyzed retrospectively. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of dNLR and to identify the optimal cutoff. Survival differences between groups stratified by dNLR were compared using Kaplan-Meier analysis. Candidate variables were screened through univariate analysis using Kaplan-Meier, random forest, Recursive Feature Elimination (RFE) and least absolute shrinkage and selection operator (LASSO) regression models. Multivariate Cox regression analysis identified independent prognostic factors, which were subsequently integrated into a predictive model visualized via a nomogram. Model performance was assessed using ROC curves, calibration curves, and decision curve analysis (DCA).ResultsA total of 177 patients were enrolled in this study. ROC analysis revealed an area under the curve (AUC) of 0.707 for dNLR in predicting postoperative survival, with an optimal cutoff value of 1.60. Patients stratified into a low-dNLR group (≤ 1.60) demonstrated significantly improved recurrence-free survival (41 months) and overall survival (17 months) compared to those in the high-dNLR group (> 1.60) (p < 0.05). Univariate and multivariate combined with 3 machine learning analyses identified preoperative dNLR > 1.60 as an independent adverse prognostic factor for postoperative outcomes, incorporating with other independent predictors (preoperative total bilirubin, carbohydrate antigen 19–9 levels, T-stage, portal venous system invasion, and lymph node metastasis) further enhanced the predictive accuracy of the prognostic model.ConclusionA preoperative dNLR > 1.60 is an independent risk factor associated with poor prognosis in patients with dCCA. The clinical prediction model based on machine learning incorporating dNLR effectively predicts postoperative outcomes in this patient population.