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. 2024 Feb 1:17:553-564.
doi: 10.2147/JIR.S443765. eCollection 2024.

Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators

Nan-Nan Du  1 , Jia-Mei Feng  1 , Shi-Jun Shao  1 , Hua Wan  1 , Xue-Qing Wu  1
Affiliations

Construction of a Multi-Indicator Model for Abscess Prediction in Granulomatous Lobular Mastitis Using Inflammatory Indicators

Nan-Nan Du et al. J Inflamm Res. .

Abstract

Background: Granulomatous lobular mastitis (GLM) is a chronic inflammatory breast disease, and abscess formation is a common complication of GLM. The process of abscess formation is accompanied by changes in multiple inflammatory markers. The present study aimed to construct a diagnosis model for the early of GLM abscess formation based on multiple inflammatory parameters.

Methods: Based on the presence or absence of abscess formation on breast magnetic resonance imaging (MRI), 126 patients with GLM were categorised into an abscess group (85 patients) and a non-abscess group (41 patients). Demographic characteristics and the related laboratory results for the 9 inflammatory markers were collected. Logistics univariate analysis and collinearity test were used for selecting independent variables. A regression model to predict abscess formation was constructed using Logistics multivariate analysis.

Results: The univariate and multivariate analysis showed that the N, ESR, IL-4, IL-10 and INF-α were independent diagnostic factors of abscess formation in GLM (P<0. 05). The nomogram was drawn on the basis of the logistics regression model. The area under the curve (AUC) of the model was 0.890, which was significantly better than that of a single indicator and the sensitivity and specificity of the model were high (81.2% and 85.40%, respectively). These results predicted by the model were highly consistent with the actual diagnostic results. The results of this calibration curve indicated that the model had a good value and stability in predicting abscess formation in GLM. The decision curve analysis (DCA) demonstrated a satisfactory positive net benefit of the model.

Conclusion: A predictive model for abscess formation in GLM based on inflammatory markers was constructed in our study, which may provide a new strategy for early diagnosis and treatment of the abscess stage of GLM.

Keywords: ROC curve; abscess formation; diagnostic model; granulomatous lobular mastitis; inflammation; risk factors.

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Conflict of interest statement

The authors declare no conflicts of interest in this work.

Figures

Figure 1
Figure 1
GLM abscess formation with honeycomb changes (left T2WI, right T1W1). The red arrows represent abscess formation with a honeycomb shape. High signal in T2W1, low signal in T1W1.
Figure 2
Figure 2
GLM with multiple irregular abscess formation (left T2WI, right T1W1). The red arrow indicates the location of the abscess cavity. High signal in T2W1, low signal in T1W1.
Figure 3
Figure 3
Statistical chart of missing values for GLM.
Figure 4
Figure 4
Nomogram of differential diagnosis of abscess formation in GLM.
Figure 5
Figure 5
ROC curves for prediction of GLM abscess formation by N, ESR, IL-4, IL-10, INF-α and the regression model.
Figure 6
Figure 6
Calibration curves for predictive models of abscess formation in GLM.
Figure 7
Figure 7
Decision curve of nomogram graph.
See this image and copyright information in PMC

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