已发表论文

胆囊癌患者术前全身炎症生物标志物的预后价值及诺模图的建立

 

Authors Deng Y, Zhang F, Yu X, Huo CL, Sun ZG, Wang S

Received 3 June 2019

Accepted for publication 16 September 2019

Published 21 October 2019 Volume 2019:11 Pages 9025—9035

DOI https://doi.org/10.2147/CMAR.S218119

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Cristina Weinberg

Peer reviewer comments 3

Editor who approved publication: Dr Beicheng Sun

Background and aim: Preoperative systemic inflammatory biomarkers, including neutrophil to lymphocyte ratio (NLR), derived neutrophil to lymphocyte ratio (dNLR), platelet to lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR) have been developed to predict patient outcome in several types of carcinomas. The aim of this study was to investigate the potential prognostic value of NLR, dNLR, PLR, and LMR, and establish a prognostic nomogram in postoperative GBC patients who underwent radical cholecystectomy.
Methods: 169 GBC patients were retrospectively enrolled in the present study. ROC curve analysis was used to determine the optimal cut-off values of systemic inflammatory biomarkers. The prognostic value of those biomarkers was investigated according to the Kaplan-Meier method and Cox regression model. A relevant prognostic nomogram was established.
Results: Results showed that NLR, dNLR, PLR, and LMR were significantly associated with overall survival (OS); whereas, NLR and LMR were retained as independent indicators. Based on these independent predictors including tumor differentiation, T stage, N stage, CEA, NLR, and LMR, a nomogram was generated with an accuracy of 0.801.
Conclusion: Based on our findings, the predictive nomogram could accurately predict individualized survival probability of postoperative GBC patients, and might support clinicians in treatment optimization and clinical decision-making.
Keywords: gallbladder carcinoma, systemic inflammatory biomarker, prognosis, overall survival, nomogram




Figure 2 Kaplan-Meier curves for cumulative OS of the study population (A) and...