ArticleFrontiers in oncology2023
A novel potential inflammation-nutrition biomarker for predicting lymph node metastasis in clinically node-negative colon cancer.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 citing papers in PubMed, 2 citations in OpenAlex.
- Prediction of lymph node metastasis in cervical cancer patients using AdaBoost machine learning model: analysis of risk factors.American journal of cancer research · 2025Article
- Diagnostic value of systemic immune-inflammation index and prognostic nutritional index combined with CEA in gastric cancer with lymph node metastasis.Frontiers in endocrinology · 2025Article
- Validated nomograms for non-metastatic colorectal cancer prognosis prediction: a population-based study.Frontiers in oncology · 2025Article
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Authors and funding
3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: The purpose of this study is to investigate the predictive significance of (platelet × albumin)/lymphocyte ratio (PALR) for lymph node metastasis (LNM) in patients with clinically node-negative colon cancer (cN0 CC). Methods: Data from 800 patients with primary CC who underwent radical surgery between March 2016 and June 2021 were reviewed. The non-linear relationship between PALR and the risk of LNM was explored using a restricted cubic spline (RCS) function while a receiver operating characteristic (ROC) curve was developed to determine the predictive value of PALR. Patients were categorized into high- and low-PALR cohorts according to the optimum cut-off values derived from Youden's index. Univariate and multivariate logistic regression analyses were used to identify the independent indicators of LNM. Sensitivity analysis was performed to repeat the main analyses with the quartile of PALR. Results: A total of eligible 269 patients with primary cN0 CC were retrospectively selected. The value of the area under the ROC curve for PALR for predicting LNM was 0.607. RCS visualized the uptrend linear relationship between PALR and the risk of LNM (p-value for non-linearity > 0.05). PALR (odds ratio = 2.118, 95% confidence interval, 1.182-3.786, p = 0.011) was identified as an independent predictor of LNM in patients with cN0 CC. A nomogram incorporating PALR and other independent predictors was constructed with an internally validated concordance index of 0.637. The results of calibration plots and decision curve analysis supported a good performance ability and the sensitivity analysis further confirmed the robustness of our findings. Conclusion: PALR has promising clinical applications for predicting LNM in patients with cN0 CC.
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