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Development and validation of a diagnostic model for malignant pleural effusion based on random forest
Xu Guo1,2, Xiao-Lei Wei1,2, Shu-Min Yin3
1Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Journal of Thoracic Disease
|June 17, 2026
Summary
This study developed a machine learning model to accurately identify malignant pleural effusion (MPE) using pleural effusion and blood indicators. The eight-feature random forest model achieved high accuracy, offering a valuable tool for MPE diagnosis.
Area of Science:
- Oncology
- Pulmonology
- Medical Informatics
Background:
- Malignant pleural effusion (MPE) diagnosis is critical for treatment.
- Current diagnostic methods have limitations.
- Combining indicators can improve diagnostic accuracy.
Purpose of the Study:
- To develop a machine learning (ML) model for MPE identification.
- To integrate pleural effusion (PE) and peripheral blood indicators.
- To enhance diagnostic accuracy beyond single markers.
Main Methods:
- Retrospective study of 1,239 participants with PE.
- Feature selection using Boruta and LASSO.
- Construction and evaluation of five ML models, including Random Forest (RF).
Main Results:
- The eight-feature RF model demonstrated optimal performance with an AUC of 0.940.
- Key predictors included PE-adenosine deaminase, carcinoembryonic antigen, and platelet-to-lymphocyte ratio.
- High sensitivity (0.875) and specificity (0.835) were achieved.
Conclusions:
- An interpretable and accurate eight-feature RF model for MPE identification was developed and validated.
- The model achieved an AUC of 0.940.
- This ML model offers a valuable approach for MPE differential diagnosis.
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