Performance of a Nomogram Based on the Integration of Inflammation Markers with Tumor Staging in Prognosis Prediction of Stage III Colorectal Cancer

Colorectal Cancer
21/08/2020

Cancer Manag Res. 2020 Aug 7;12:7077-7085. doi: 10.2147/CMAR.S263577. eCollection 2020.

ABSTRACT

INTRODUCTION: The aim of the present study was to evaluate a nomogram model for predicting the 5-year overall survival (OS) in lymph node-metastatic colorectal cancer (CRC) patients by combining inflammation markers with some traditional prognostic factors.

METHODS: A total of 399 patients with stage III (pTXN1-3M0) CRC operated from January 2007 to December 2012 were enrolled in this retrospective study. All patients underwent D2 lymphadenectomy in the hospital. A prognostic nomogram based on the integration of traditional prognostic factors and NLR (neutrophil-to-lymphocyte ratio) and PLR (platelet-to-lymphocyte ratio) was established and compared with the nomogram based on the traditional prognostic factors alone. ROC curves were further applied to verify the predictive accuracy of the established model.

RESULTS: Both NLR (P=0.00) and PLR (P=0.01) predicted the 5-year OS. In multivariate analysis, age, T3 category, T4 category, N2 category, N3 category, Pgp (P-glycoprotein), NLR and PLR are proven to be independent (all P≤0.05). The established nomogram showed better predictive power than that of traditional profile (c-index: 0.66 versus 0.63) in both training and validation cohorts. External assessment by ROC curve analysis demonstrated that the established model had a good prediction accuracy of 5-year OS in stage III CRC patients, with area under curve values of 0.657 and 0.629 in training and validating sets, respectively.

CONCLUSION: A nomogram based on the integration of traditional prognostic factors and inflammatory markers (NLR and PLR) could provide more precise long-term prognosis information for lymph node-metastatic CRC patients than the model based on traditional profile alone. This model might be useful for clinical application in personalized evaluation.