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Predictability of peripheral blood inflammatory markers in prediction of lymph node metastasis in gastric cancer
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DOI：10.1177/17562848261451338.png)
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Abstract
En 中文
<jats:sec>
<jats:title>Background:</jats:title>
<jats:p>Gastric cancer (GC) has high incidence and mortality rates worldwide, and lymph node metastasis (LNM) is a key determinant of prognosis and treatment strategy.</jats:p>
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<jats:sec>
<jats:title>Objectives:</jats:title>
<jats:p>In this study, we aimed to investigate the association between eight inflammatory markers and LNM in GC and develop a prognostic model for LNM using inflammatory indicators.</jats:p>
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<jats:title>Design:</jats:title>
<jats:p>This was a retrospective multicenter study.</jats:p>
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<jats:title>Methods:</jats:title>
<jats:p>Clinical, pathological, and laboratory data from 868 GC patients were analyzed. Independent risk factors for LNM were identified through univariate logistic regression (LR), least absolute shrinkage and selection operator regression, and multivariable LR. Fourteen machine learning models were constructed using 10-fold cross-validation, and model performance was evaluated by calculation of the area under the receiver operating curve (AUC).</jats:p>
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<jats:title>Results:</jats:title>
<jats:p>Five clinical and pathological characteristics were identified as independent risk factors for LNM in GC: Glasgow prognostic score (GPS), T stage, histological differentiation, vascular invasion, and total lymph node count. The categorical boosting (CatBoost) model demonstrated the best performance (AUC: 0.808) of all machine learning models tested, followed by the random forest model (0.806). Conversely, the AdaBoost model had the lowest AUC score (0.727). Further analysis of the CatBoost model showed that at the optimal threshold (0.56), it correctly identified approximately 82.7% of patients with LNM and approximately 69.4% of LNM-negative patients.</jats:p>
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<jats:title>Conclusion:</jats:title>
<jats:p>These findings demonstrate the value of inflammatory markers, including the GPS, in prediction of LNM in patients with GC, as well as the ability of machine learning models to enhance prognostic accuracy, support informed treatment decisions, and improve patient outcomes.</jats:p>
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