Observational studyTherapeutic advances in respiratory disease
Development and validation of a machine learning model for differential diagnosis of malignant pleural effusion using routine laboratory data.
Observational study in Therapeutic advances in respiratory disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Biochemistry-based machine learning algorithms in differentiating pleural effusion: current status and perspective.ERJ open research · 2026Review
- Development and validation of a diagnostic model for malignant pleural effusion based on random forest.Journal of thoracic disease · 2026Article
- Unraveling the multidimensional pleural ecosystem of lung cancer-associated malignant pleural effusion.Cancer cell international · 2026Review
- Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.Thoracic research and practice · 2026Article
- Development and external validation of machine-learning models for diagnosing malignant pleural effusion.Frontiers in oncology · 2026Article
- Development of a scoring system based on a nomogram to identify malignant pleural effusion.Journal of thoracic disease · 2025Article
- Prediction of Lung Cancer Metastasis Using Machine Learning Models Based on Clinical Laboratory Data.Cancer reports (Hoboken, N.J.) · 2025Article
- Innovative machine learning approach for liver fibrosis and disease severity evaluation in MAFLD patients using MRI fat content analysis.Clinical and experimental medicine · 2025Article
- Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH.Respiratory research · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Harnessing AI for Improved Detection and Classification of Pleural Effusion: Insights and Innovations.Canadian respiratory journal · 2025Review
- Advances in early detection of non-small cell lung cancer: A comprehensive review.Cancer medicine · 2024Review
- Acute ischemic stroke prediction and predictive factors analysis using hematological indicators in elderly hypertensives post-transient ischemic attack.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveThis study aimed to develop and validate a diagnostic model for early differentiation between MPE and BPE based on routine laboratory data.
designThis was a retrospective observational cohort study.
methodsA 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.
resultsAmong 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.
conclusionThe 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.
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