Development of a machine learning-based diagnostic model using hematological parameters to differentiate periductal mastitis from granulomatous lobular mastitis.

ObjectiveNonpuerperal mastitis (NPM) is an inflammatory condition, including periductal mastitis (PDM) and granulomatous lobular mastitis (GLM). The clinical manifestations of PDM and GLM are highly similar, posing significant challenges in their differentiation. Currently, there is a paucity of diagnostic models for distinguishing PDM from GLM. The objective of this research is to create and verify a model that can distinguish between PDM and GLM.MethodsThis study retrospectively collected laboratory data from 60 patients with PDM and 60 patients with GLM, and randomly assigned these patients into a training group (80%) and a testing group (20%). Additionally, 20 patients with NPM from another center were included as an external validation group. Five machine learning (ML) algorithms (Logistic Regression, XGBoost, Random Forest, AdaBoost, GNB) were combined to differentiate PDM from GLM. The performance of the models was evaluated using the area under the curve (AUC), and the model with the highest AUC in the testing group was selected as the best model.ResultsThe logistic regression model emerged as the optimal ML approach for distinguishing PDM from GLM, primarily utilizing six variables (RDW, mean platelet volume, C4, IFN-γ, PT, and DD). In the training group, the model achieved an AUC of 0.827, and similarly, in the testing group, it yielded an AUC of 0.807. Addition, both the training and testing groups achieved an accuracy, sensitivity, and specificity of over 0.7. Notably, the model also performed effectively in the external validation group, with an AUC of 0.750.ConclusionThis study established a hematological model to distinguish PDM from GLM, facilitating early diagnosis and reducing misdiagnosis in NPM patients.