Comparative Analysis of Novel Inflammatory Indices for Predicting Relapse in Granulomatous Mastitis: Lymphocyte-to-Monocyte Ratio Emerges as the Superior Biomarker
Bratislava Medical Journal, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s44411-026-00793-x
- Dergi Adı: Bratislava Medical Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Ultimate (EBSCO)
- Anahtar Kelimeler: Granulomatous mastitis, Lymphocyte-to-monocyte ratio, Inflammatory indices, Relapse, Biomarkers
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Purpose: Granulomatous mastitis (GM) is a chronic inflammatory breast disease with high relapse rates. We aimed to identify novel inflammatory indices predicting GM relapse, establish optimal cut-off values, and determine independent predictors through multivariate analysis. Methods: This retrospective cohort included 101 histopathologically confirmed GM patients between September 2016 and June 2022. Patients were divided into relapse (n = 26) and non-relapse (n = 75) groups. Multiple inflammatory indices including the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammation response index (SIRI), pan-immune-inflammation value (PIV), systemic immune-inflammation index (SII), prognostic nutritional index (PNI), and C-reactive protein-albumin-lymphocyte (CALLY) index were calculated. Disease severity was assessed using the M-score. Receiver operating characteristic (ROC) curve analysis determined optimal cut-off values, and binary logistic regression identified independent predictors. Results: During median 42-month follow-up, relapse occurred in 25.7% of patients. Relapse patients had significantly lower LMR (3.25 ± 1.04 vs. 4.33 ± 2.04, p < 0.001) and higher SIRI (1.88 ± 1.54 vs. 1.07 ± 0.91, p = 0.002) and PIV (579.96 ± 408.90 vs. 307.24 ± 336.02, p = 0.003). ROC analysis showed LMR ≤ 4.02 had highest discriminatory ability (AUC: 0.733, sensitivity: 92.3%, specificity: 61.3%). Multivariate analysis, employing dichotomized inflammatory indices and addressing multicollinearity, identified categorized LMR (OR: 0.504, 95% CI: 0.328–0.774, p = 0.002) as the sole independent predictor, with improved model performance (Nagelkerke R²=0.420, classification accuracy: 78.2%). Conclusion: LMR is a valuable independent biomarker for predicting GM relapse. This cost-effective index can guide risk stratification and clinical decision-making.