This study aimed to develop and evaluate a machine learning model utilizing non-invasive clinical parameters for the classification of endometrial non-benign lesions, specifically atypical hyperplasia (AH) and endometrioid carcinoma (EC), in postmenopausal women. Our study collected clinical parameters from a cohort of 999 patients with postmenopausal endometrial lesions and conducted preprocessing to identify 57 relevant characteristics from these irregular clinical data. To predict the presence of postmenopausal endometrial non-benign lesions, including atypical hyperplasia and endometrial cancer, we employed various models such as eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), as well as two ensemble models. Additionally, a test set was performed on an independent dataset consisting of 152 patients. The performance evaluation of all models was based on metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F1 score. The RF model demonstrated superior recognition capabilities for patients with non-benign lesions compared to other models. In the test set, it attained a sensitivity of 88.1% and an AUC of 0.93, surpassing all alternative models evaluated in this study. , we have integrated this model into our hospital's Clinical Decision Support System (CDSS) and implemented it within the outpatient electronic medical record system to continuously validate and optimize its performance. We have trained a model and deployed a system with high discriminatory power that may provide a novel approach to identify patients at higher risk of postmenopausal endometrial non-benign lesions who may benefit from more tailored screening and clinical intervention.

中文翻译：

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通过基于临床参数的机器学习模型对绝经后子宫内膜非良性病变进行风险分类

本研究旨在开发和评估一种机器学习模型，利用非侵入性临床参数对绝经后妇女的子宫内膜非良性病变进行分类，特别是非典型增生（AH）和子宫内膜样癌（EC）。我们的研究收集了 999 名绝经后子宫内膜病变患者的临床参数，并进行预处理，从这些不规则的临床数据中识别出 57 个相关特征。为了预测绝经后子宫内膜非良性病变（包括不典型增生和子宫内膜癌）的存在，我们采用了各种模型，例如极限梯度提升（XGBoost）、随机森林（RF）、逻辑回归（LR）、支持向量机（SVM） ，反向传播神经网络（BPNN），以及两个集成模型。此外，还对包含 152 名患者的独立数据集进行了测试集。所有模型的性能评估均基于受试者工作特征曲线下面积 (AUC)、敏感性、特异性、精确度和 F1 评分等指标。与其他模型相比，RF 模型对非良性病变患者表现出卓越的识别能力。在测试集中，它的灵敏度为 88.1%，AUC 为 0.93，超过了本研究中评估的所有替代模型。我们将此模型集成到我院的临床决策支持系统（CDSS）中，并在门诊电子病历系统中实施，不断验证和优化其性能。我们训练了一个模型并部署了一个具有高辨别能力的系统，该系统可能提供一种新方法来识别绝经后子宫内膜非良性病变风险较高的患者，这些患者可能会受益于更有针对性的筛查和临床干预。