Li Gaosha, Yu Qian, Dong Feng, Wu Zhaoxia, Fan Xijing, Zhang Lingling, Yu Ying
Department of Clinical Laboratory, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, China.
Department of Laboratory Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
PLoS One. 2025 Jan 16;20(1):e0315406. doi: 10.1371/journal.pone.0315406. eCollection 2025.
Non-puerperal mastitis (NPM) is an inflammatory breast disease affecting women during non-lactation periods, and it is prone to relapse after being cured. Accurate prediction of its recurrence is crucial for personalized adjuvant therapy, and pathological examination is the primary basis for the classification, diagnosis, and confirmation of non-puerperal mastitis. Currently, there is a lack of recurrence models for non-puerperal mastitis. The aim of this research is to create and validate a recurrence model using machine learning for patients with non-puerperal mastitis.
We retrospectively collected laboratory data from 120 NPM patients, dividing them into a non-recurrence group (n = 59) and a recurrence group (n = 61). Through random allocation, these individuals were split into a training cohort and a testing cohort in a 90%:10% ratio for the purpose of building the model. Additionally, data from 25 NPM patients from another center were collected to serve as an external validation cohort for the model. Univariate analysis was used to examine differential indicators, and variable selection was conducted through LASSO regression. A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. The finally selected model was interpreted and evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, Decision curve analysis (DCA), and Shapley Additive Explanations (SHAP) plots.
The logistic regression model emerged as the optimal model for predicting recurrence of NPM with machine learning, primarily utilizing three variables: FIB, bacterial infection, and CD4+ T cell count. The model showed an AUC of 0.846 in the training cohort and 0.833 in the testing cohort. The calibration curve indicated excellent calibration of the model. DCA revealed that the model possessed favorable clinical utility. Furthermore, the model effectively achieved in the external validation group, with an AUC of 0.825.
The machine learning model developed in this study, serving as an effective tool for predicting NPM recurrence, aids doctors in making more individualized treatment decisions, thereby enhancing therapeutic efficacy and reducing the risk of recurrence.
非产褥期乳腺炎（NPM）是一种在非哺乳期影响女性的炎性乳腺疾病，治愈后容易复发。准确预测其复发对于个性化辅助治疗至关重要，而病理检查是非产褥期乳腺炎分类、诊断和确诊的主要依据。目前，缺乏针对非产褥期乳腺炎的复发模型。本研究的目的是使用机器学习为非产褥期乳腺炎患者创建并验证一个复发模型。
我们回顾性收集了120例NPM患者的实验室数据，将他们分为非复发组（n = 59）和复发组（n = 61）。通过随机分配，将这些个体按照90%:10%的比例分为训练队列和测试队列，用于构建模型。此外，收集了来自另一个中心的25例NPM患者的数据作为模型的外部验证队列。采用单因素分析来检查差异指标，并通过LASSO回归进行变量选择。采用四种机器学习算法（XGBoost、逻辑回归、随机森林、AdaBoost）组合来预测NPM复发，并选择测试集中曲线下面积（AUC）最高的模型作为最佳模型。使用受试者工作特征（ROC）曲线、校准曲线、决策曲线分析（DCA）和Shapley加性解释（SHAP）图对最终选定的模型进行解释和评估。
逻辑回归模型成为使用机器学习预测NPM复发的最佳模型，主要利用三个变量：纤维蛋白原（FIB）、细菌感染和CD4 + T细胞计数。该模型在训练队列中的AUC为0.846，在测试队列中的AUC为0.833。校准曲线表明模型具有良好的校准。DCA显示该模型具有良好的临床实用性。此外，该模型在外部验证组中也有效实现，AUC为0.825。
本研究开发的机器学习模型作为预测NPM复发的有效工具，有助于医生做出更个性化的治疗决策，从而提高治疗效果并降低复发风险。
