ArticleTechniques in coloproctology2024
A nomogram of inflammatory indexes for preoperatively predicting the risk of lymph node metastasis in colorectal cancer.
Article in Techniques in coloproctology, 2024. 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.
- The predictive value of contrast-enhanced CT characteristics combined with composite inflammatory markers for lymph node metastasis in T1 colorectal cancer.International journal of colorectal disease · 2026Article
- Physical activity and cancer survival: the role of peripheral blood immune and inflammatory markers in reducing mortality-a retrospective cohort study based on NHANES 1999-2018.Translational cancer research · 2026Article
- Triglyceride-to-glucose index and lymphocyte-to-monocyte ratio enhance prognostic accuracy for colorectal cancer patients: a multicenter machine learning cohort study.Translational cancer research · 2026Article
- Machine learning to develop and validate a model for predicting the risk of lymph node metastasis in colorectal cancer patients.Frontiers in oncology · 2026Article
- Prognostic value of fatty acid metabolism-related genes in colorectal cancer.International journal of clinical and experimental pathology · 2026Article
- Construction of a risk prediction model for early postoperative recurrence in stage II/III colorectal cancer.World journal of gastrointestinal oncology · 2025Article
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6 authors.
Funding
Abstract
purposeTo investigate the independent risk factors associated with the development of lymph node metastasis (LNM) in patients with colorectal cancer (CRC), focusing on preoperative systemic inflammatory indicators, and to construct a corresponding risk predictive model. MATERIALS AND
methodsThe clinical data of 241 patients with CRC who underwent surgery after the first diagnosis between January 2012 and December 2017 at our hospital were reviewed. A best logistic regression model was constructed by Lasso regression for multivariate analysis, from which a Nomogram was derived. Using bootstrap to conduct internal validation. The model's predictive performance and clinical practicability were evaluated using the receiver operating characteristic curve (ROC) curve, calibration curve, and decision curve analysis (DCA). External validation was conducted using retrospective data from 170 patients who underwent surgery between January 2020 and May 2022 at another hospital.
resultsCross-validation indicated smoking history, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), fibrinogen-albumin ratio (FAR), and fecal occult blood (FOB) as variables with non-zero coefficients. These factors were included in the logistic regression, and multivariate analysis confirmed that smoking history, NLR, LMR, FAR, and FOB were independent risk factors (P < 0.05). The ROC and calibration curve of the original model and external validation indicated strong predictive power of the model. DCA suggested the model's favorable clinical utility.
conclusionsThe model constructed in this study has robust predictive performance and clinical utility for the preoperative determination of CRC LMN, offering significant for clinical decision-making in patients with CRC.
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