Work

Development and validation of a cytokine-based diagnostic model for Kikuchi–Fujimoto disease

Year: 2026

Type: article

Abstract: Objective The diagnosis of Kikuchi–Fujimoto disease (KFD) is challenging, which is mainly based on typical histopathological findings from a lymph node biopsy. We aimed to develop and validate a predictive model for the diagnosis of KFD based on cytokine profiles and other laboratory findings. Methods This study included pediatric patients with KFD and controls with other febrile diseases in a retrospective training cohort ( n = 673) and a prospective validation cohort ( n = 116). Clinical data, laboratory parameters, and serum cytokines were collected. Variables with P < 0.05 in univariate analysis and acceptable collinearity were entered into multivariable logistic regression with stepwise selection. A nomogram was constructed based on the final model. Model performance was evaluated using ROC analysis, calibration curves, Hosmer–Lemeshow test, Brier score, and decision curve analysis. Results The final model included age, WBC, neutrophil, hemoglobin, platelets, ESR, IL-10, and IFN-γ/IL-6, with IFN-γ/IL-6 modeled as a 4-knot restricted cubic spline. The derived nomogram showed excellent performance in the training cohort (AUC = 0.989, sensitivity 97.5%, specificity 94.4%) and maintained robust discrimination in the external validation cohort (AUC = 0.971, sensitivity 94.1%, specificity 93.9%). Calibration was good in both cohorts (Hosmer–Lemeshow P = 0.99 and 0.19, external calibration slope 0.88), with low Brier scores (0.025 and 0.060). The model achieved high predictive accuracy, with 0.966 ( κ = 0.836) in the training cohort and 0.931 ( κ = 0.831) in the external cohort. Conclusions This study proposes the first validated diagnostic model for KFD, demonstrating excellent performance and highlighting the IFN-γ/IL-6 ratio as a key predictor. With further validation, it may serve as a practical decision-support tool to guide clinical management.

Source: Frontiers in Immunology

DOI: 10.3389/fimmu.2026.1701510