4.6 Article

Prognostic Value of Combined LMR and CEA Dynamic Monitoring in Postoperative Colorectal Cancer Patients

期刊

JOURNAL OF INFLAMMATION RESEARCH
卷 16, 期 -, 页码 4229-4250

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DOVE MEDICAL PRESS LTD
DOI: 10.2147/JIR.S422500

关键词

colorectal cancer; postoperative recurrence; predictive model; inflammatory markers

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This study investigated the clinical significance of dynamic changes in the lymphocyte-to-monocyte ratio (LMR) and neutrophil-lymphocyte ratio (NLR) in the prediction of postoperative recurrence in patients with colorectal cancer (CRC). The findings suggest that dynamic changes in LMR can be used as a biomarker for predicting CRC recurrence and, when combined with CEA, can improve the predictive performance for detecting CRC recurrence.
Purpose: We aim to investigate the clinical significance of dynamic changes in the lymphocyte-to-monocyte ratio (LMR) and neutrophil-lymphocyte ratio (NLR) in peripheral blood at different time points combined with CEA in the prediction of postoperative recurrence-in-patients with colorectal cancer (CRC). Patients and Methods: This study collected 357 patients with stage I-III CRC between 2016 and April 2018. The dynamic changes from preoperative to postoperative LMR (p-LMR-p) and NLR (p-NLR-p) were analyzed using COX regression for multivariate analysis. Logistic regression was used to investigate whether the dynamic changes from post-treatment to pre-end of follow-up LMR (p-LMR-f) and NLR (p-NLR-f) were independent risk factors for CRC recurrence and to construct a predictive model. Internal validation using bootstrapping was performed to validate the discrimination ability of the model. The models' discriminative effect, calibration degree, and clinical utility were assessed. Results: In both the total cohort and the adjuvant therapy group, the dynamic changes of p-LMR-p (High-High vs Low-Low: p=0.006; HR:2.210, 95% CI: 1.256-3.890) were found to be independent prognostic factors for recurrence-free survival (RFS) in CRC patients. Additionally, logistic regression analysis revealed that N stage, CEA, LMR of pre-end of follow-up (f-LMR), and p-LMR-f were independent risk factors for CRC recurrence. In the total cohort, the p-LMR-f had an area under the curve (AUC) of 0.704, with a sensitivity of 64% and a specificity of 75.3%. By combining p-LMR-f with CEA, a predictive model was constructed, which showed an AUC of 0.913 (0.986-0.913) in the total cohort and an AUC of 0.924 (0.902-0.924) in the adjuvant therapy group during internal validation using bootstrapping. Conclusion: Dynamic changes in LMR can be used to predict the prognosis of CRC and serve as a biomarker for predicting CRC recurrence. Combined with CEA, it can improve the predictive performance for detecting CRC recurrence.

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