■恶性胸腔积液(MPE)和良性胸腔积液(BPE)的鉴别诊断提出了临床挑战。近年来,用于疾病诊断的人工智能(AI)机器学习模型的使用有所增加。
■本研究旨在基于常规实验室数据开发和验证用于早期区分MPE和BPE的诊断模型。
■这是一项回顾性观察性队列研究。
■总共2352例新诊断的胸腔积液(PE)患者,2008年1月至2021年3月,最终被录取。其中,1435、466和451名参与者被随机分配到培训中,验证,并以3:1:1的比例测试队列。临床参数,包括年龄,性别,和PE患者的实验室参数,被抽象出来进行分析。基于81个候选实验室变量,五种机器学习模型,即极端梯度提升(XGBoost)模型,逻辑回归(LR)模型,随机森林(RF)模型,支持向量机(SVM)模型,建立了多层感知器(MLP)模型。通过受试者工作特征(ROC)曲线评估了它们各自对MPE的诊断性能。
■在这五个模型中,XGBoost模型对MPE表现出最佳的诊断性能(训练中的曲线下面积(AUC):0.903、0.918和0.886,验证,和测试队列,分别)。此外,XGBoost模型优于胸膜液(PF)中的癌胚抗原(CEA)水平,血清,和PF/血清比率(AUC:训练队列中的0.726、0.699和0.692;验证队列中的0.763、0.695和0.731;测试队列中的0.722、0.729和0.693,分别)。此外,与CEA相比,XGBoost模型在诊断肺癌诱导的MPE时显示出更强的诊断能力和敏感性.
■利用常规实验室生物标志物的机器学习模型的开发显着增强了区分MPE和BPE的诊断能力。XGBoost模型成为诊断MPE的有价值的工具。
The differential diagnosis of malignant pleural effusion (MPE) and benign pleural effusion (BPE) presents a clinical challenge. In recent years, the use of artificial intelligence (AI) machine learning models for disease diagnosis has increased.
This study aimed to develop and validate a diagnostic model for early differentiation between MPE and BPE based on routine laboratory data.
This was a retrospective observational cohort study.
A total of 2352 newly diagnosed patients with pleural effusion (PE), between January 2008 and March 2021, were eventually enrolled. Among them, 1435, 466, and 451 participants were randomly assigned to the training, validation, and testing cohorts in a ratio of 3:1:1. Clinical parameters, including age, sex, and laboratory parameters of PE patients, were abstracted for analysis. Based on 81 candidate laboratory variables, five machine learning models, namely extreme gradient boosting (XGBoost) model, logistic regression (LR) model, random forest (RF) model, support vector machine (SVM) model, and multilayer perceptron (MLP) model were developed. Their respective diagnostic performances for MPE were evaluated by receiver operating characteristic (ROC) curves.
Among the five models, the XGBoost model exhibited the best diagnostic performance for MPE (area under the curve (AUC): 0.903, 0.918, and 0.886 in the training, validation, and testing cohorts, respectively). Additionally, the XGBoost model outperformed carcinoembryonic antigen (CEA) levels in pleural fluid (PF), serum, and the PF/serum ratio (AUC: 0.726, 0.699, and 0.692 in the training cohort; 0.763, 0.695, and 0.731 in the validation cohort; and 0.722, 0.729, and 0.693 in the testing cohort, respectively). Furthermore, compared with CEA, the XGBoost model demonstrated greater diagnostic power and sensitivity in diagnosing lung cancer-induced MPE.
The development of a machine learning model utilizing routine laboratory biomarkers significantly enhances the diagnostic capability for distinguishing between MPE and BPE. The XGBoost model emerges as a valuable tool for the diagnosis of MPE.