Fig. 9 | Scientific Reports

Fig. 9

From: Interpretable machine learning analysis of clinicopathological and immunonutritional biomarkers for predicting lymph node metastasis in gastric cancer

Fig. 9

Area under the curve (AUC) curve analysis and restricted cubic spline (RCS) analysis for lymph node metastasis (LNM) prediction model. (A) ROC curves comparing training set (AUC = 0.829, 95% CI 0.804-0.854) and test set (AUC = 0.818, 95% CI 0.769–0.866) performance. (B–G) RCS curves showing the relationship between biomarker values and predicted probability of LNM across risk quartiles (K1–K4): (B) SII (systemic immune-inflammation index); (C) FIB (fibrinogen); (D) CEA (carcinoembryonic antigen); (E) unknown biomarker; (F) unknown biomarker; (G) MTD (maximum tumor diameter). Each RCS curve demonstrates the non-linear relationship between biomarker levels and LNM risk, with shaded areas representing confidence intervals.

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