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Purpose <p>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.</p> Methods <p>This retrospective cohort included 101 histopathologically confirmed GM patients between September 2016 and June 2022. Patients were divided into relapse (<i>n</i> = 26) and non-relapse (<i>n</i> = 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.</p> Results <p>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, <i>p</i> &lt; 0.001) and higher SIRI (1.88 ± 1.54 vs. 1.07 ± 0.91, <i>p</i> = 0.002) and PIV (579.96 ± 408.90 vs. 307.24 ± 336.02, <i>p</i> = 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, <i>p</i> = 0.002) as the sole independent predictor, with improved model performance (Nagelkerke R²=0.420, classification accuracy: 78.2%).</p> Conclusion <p>LMR is a valuable independent biomarker for predicting GM relapse. This cost-effective index can guide risk stratification and clinical decision-making.</p>

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Comparative Analysis of Novel Inflammatory Indices for Predicting Relapse in Granulomatous Mastitis: Lymphocyte-to-Monocyte Ratio Emerges as the Superior Biomarker

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Fahrettin Bıçakcı,
  • Özlem Kılıç,
  • Ezgi Çimen Güneş,
  • Dilek Tezcan,
  • Volkan Türkmen

摘要

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.