ArticleJournal of inflammation research2023
Prognostic Value of Combined LMR and CEA Dynamic Monitoring in Postoperative Colorectal Cancer Patients.
Article in Journal of inflammation research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Various machine learning prediction models for short-term postoperative prognosis of colorectal cancer based on intraoperative hypothermia.American journal of cancer research · 2026Article
- The effects of two combined methods of P53 expression and preoperative serum CEA detection on the prognosis of colorectal cancer.Frontiers in oncology · 2025Article
- Near-infrared light-enhanced colorimetric signal amplification strategy for tumor marker detection based on MoSMikrochimica acta · 2024Article
- Prediction of pathological complete response and prognosis in locally advanced rectal cancer.World journal of gastrointestinal oncology · 2024Article
- A Longitudinal Dynamic Change in LMR Can Be a Biomarker for Recurrence in Fusobacterium Nucleatum-Positive Colorectal Cancer Patients.Journal of inflammation research · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
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Abstract
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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