目的:目前中央性早熟(CPP)的诊断依赖于促性腺激素释放激素类似物(GnRHa)刺激试验,这需要多种侵入性血液采样程序。这项研究的目的是构建包含基础青春期激素水平的机器学习模型,垂体磁共振成像(MRI),和盆腔超声参数来预测早熟女孩对GnRHa刺激试验的反应。
方法:这项回顾性研究包括455名诊断为性早熟的女孩,这些女孩接受了经腹盆腔超声检查,回顾性分析脑MRI检查和GnRHa刺激试验.他们以8:2的比例随机分配到训练或内部验证集。开发了四个机器学习分类器来识别CPP女孩,包括逻辑回归,随机森林,光梯度增强(LightGBM),和极限梯度提升(XGBoost)。准确性,灵敏度,特异性,正预测值,负预测值,测量了模型的接收器工作特征下面积(AUC)和F1评分。
结果:参与者分为特发性CPP组(n=263)和非CPP组(n=192)。所有使用的机器学习分类器在区分CPP组和非CPP组方面都取得了良好的性能。验证集的曲线下面积(AUC)范围为0.72至0.81。XGBoost具有最高的诊断效能,敏感性为0.81,特异性为0.72,F1评分为0.80。基础青春期激素水平(包括黄体生成素,促卵泡激素,和雌二醇),平均卵巢体积,和几个子宫参数是模型中的预测因子。
结论:我们开发的机器学习预测模型对于预测对GnRHa刺激测试的反应具有良好的效果,可以帮助诊断CPP。
The current diagnosis of central precocious puberty (CPP) relies on the gonadotropin-releasing hormone analogue (GnRHa) stimulation test, which requires multiple invasive blood sampling procedures. The aim of this study was to construct machine learning models incorporating basal pubertal hormone levels, pituitary magnetic resonance imaging (MRI), and pelvic ultrasound parameters to predict the response of precocious girls to GnRHa stimulation test.
This retrospective study included 455 girls diagnosed with precocious puberty who underwent transabdominal pelvic ultrasound, brain MRI examinations and GnRHa stimulation testing were retrospectively reviewed. They were randomly assigned to the training or internal validation set in an 8:2 ratio. Four machine learning classifiers were developed to identify girls with CPP, including logistic regression, random forest, light gradient boosting (LightGBM), and eXtreme gradient boosting (XGBoost). The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, area under receiver operating characteristic (AUC) and F1 score of the models were measured.
The participates were divided into an idiopathic CPP group (n = 263) and a non-CPP group (n = 192). All machine learning classifiers used achieved good performance in distinguishing CPP group and non-CPP group, with the area under the curve (AUC) ranging from 0.72 to 0.81 in validation set. XGBoost had the highest diagnostic efficacy, with sensitivity of 0.81, specificity of 0.72, and F1 score of 0.80. Basal pubertal hormone levels (including luteinizing hormone, follicle-stimulating hormone, and estradiol), averaged ovarian volume, and several uterine parameters were predictors in the model.
The machine learning prediction model we developed has good efficacy for predicting response to GnRHa stimulation tests which could help in the diagnosis of CPP.