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Evaluation of Systemic Inflammatory Markers in Predicting Malignancy in Parotid Gland Tumors
Ozcelik Erdem Rukiye1, Yegin Onur1, Eryılmaz Mehmet Akif1
1Department of Otorhinolaryngology, Faculty of Medicine, Necmettin Erbakan University, Konya, Turkey.
Objectives:
To evaluate the diagnostic value of preoperative hematologic inflammatory indices in distinguishing benign from malignant parotid gland tumors and to assess their potential as adjunctive diagnostic tools.
Methods:
This retrospective observational study included 259 patients who underwent parotidectomy at the Department of Otorhinolaryngology, Necmettin Erbakan University Faculty of Medicine, from January 2020 to January 2025. Demographic data, histopathologic diagnoses, and preoperative parameters were reviewed. Inflammatory indices (NLR, PLR, SII, SIRI, WMR, and NP/LHb) were calculated. Comparative analyses among pleomorphic adenoma, Warthin's tumor, and malignant tumors were performed using one-way ANOVA, Kruskal-Wallis, and post hoc tests. Receiver operating characteristic (ROC) analysis was used to determine discriminative performance.
Results:
Of the 259 patients, 101 had pleomorphic adenoma, 107 had Warthin tumor, and 51 had malignant tumors. Warthin tumors demonstrated significantly higher white blood cell, neutrophil, lymphocyte, monocyte, and hemoglobin levels than both pleomorphic adenoma and malignant tumors (p < 0.001). Malignant tumors were associated with older age, larger tumor size and volume, higher NLR and PLR values, and lower lymphocyte, monocyte, and hemoglobin levels (all p < 0.05). ROC analysis identified age (cut-off: 60.5 years; AUC = 0.642) and tumor volume (cut-off: 24.75 cm3; AUC = 0.664) as the strongest predictors of malignancy. An NLR cut-off value of 1.62 yielded 86% sensitivity and 31% specificity for differentiating malignant from benign lesions.
Conclusions:
Preoperative hematologic indices-NLR and PLR-may serve as inexpensive, accessible adjunctive tools in evaluating parotid masses. Integration of inflammatory indices into radiomics-based or nomogram-driven diagnostic models has the potential to improve diagnostic accuracy.
Level Of Evidence:
N/A.
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