Shaoshan Cao, Niannian Chen, Ying Ma
Department of Obstetrics and Gynecology, Mianyang Central Hospital, University of Electronic Science and Technology of China, Mianyang, 621000, Sichuan, China.
School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621000, China.
Sci Rep. 2025 Jan 6;15(1):981. doi: 10.1038/s41598-024-82373-z.
Objective Endometrial lesions are a frequent complication following breast cancer, and current diagnostic tools have limitations. This study aims to develop a machine learning-based nomogram model for predicting the early detection of endometrial lesions in patients. The model is designed to assess risk and facilitate individualized treatment strategies for premenopausal breast cancer patients. Method A retrospective study was conducted on 224 patients who underwent diagnostic curettage post-tamoxifen (TAM) therapy between November 2012 and November 2023. These patients exhibited signs of endometrial abnormalities or symptoms such as colporrhagia. Clinical data were collected and analyzed using R software (version 4.3.2) to identify factors influencing the occurrence of endometrial lesions and evaluate their predictive values. Three machine learning methods were employed to develop a risk prediction model, and their performances were compared. The best-performing model was selected to construct a nomogram of endometrial lesions. Internal validation was conducted using the bootstrap method, and the model's accuracy and fit were assessed using the concordance index (C-index) and calibration curves. Results Independent risk factors for endometrial lesions included ultrasound characteristics, duration of TAM therapy, presence of colporrhagia, and endometrial thickness (P < 0.05). Among the machine learning methods compared, the LASSO regression integrated with a multifactorial logistic regression model demonstrated strong performance, with a concordance index (C-index) of 0.874 and effective calibration (mean absolute error of conformity: 0.014). This model achieved an accuracy of 0.853 and a precision of 0.917, with a training set AUC of 0.874 (95% CI: 0.794-0.831) and a test set AUC of 0.891 (95% CI: 0.777-1.000), closely aligning the predicted risk with the actual observed risk. Conclusion The developed prediction model is effective in evaluating endometrial lesions in premenopausal breast cancer patients. This model offers a theoretical foundation for improving clinical predictions and devising tailored treatment strategies for this patient group.
目的 子宫内膜病变是乳腺癌后的常见并发症，当前的诊断工具存在局限性。本研究旨在开发一种基于机器学习的列线图模型，用于预测患者子宫内膜病变的早期检测。该模型旨在评估风险，并为绝经前乳腺癌患者制定个体化治疗策略。方法 对2012年11月至2023年11月间接受他莫昔芬（TAM）治疗后诊断性刮宫的224例患者进行回顾性研究。这些患者表现出子宫内膜异常迹象或诸如阴道出血等症状。收集临床数据并使用R软件（版本4.3.2）进行分析，以确定影响子宫内膜病变发生的因素并评估其预测价值。采用三种机器学习方法开发风险预测模型，并比较它们的性能。选择性能最佳的模型构建子宫内膜病变列线图。使用自助法进行内部验证，并使用一致性指数（C指数）和校准曲线评估模型的准确性和拟合度。结果 子宫内膜病变的独立危险因素包括超声特征、TAM治疗持续时间、阴道出血情况和子宫内膜厚度（P < 0.05）。在比较的机器学习方法中，与多因素逻辑回归模型相结合的LASSO回归表现出色，一致性指数（C指数）为0.8
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