Transthoracic ultrasound features to differentiate tuberculous from malignant pleural effusion: development of a machine learning-based clinical prediction model.

BACKGROUND

Tuberculous pleural effusion (TBE) and malignant pleural effusion (MPE) are the most common causes of exudative pleural effusion in tuberculosis-endemic regions. This study evaluates transthoracic ultrasound (TUS) features of TBE vs. MPE and incorporates clinical and pleural fluid (Pf) parameters into a machine learning-based random forest (RF) model for differentiation.

METHODS

This prospective observational study was conducted over 6 months across six tertiary hospitals in Malaysia, involving patients undergoing diagnostic medical thoracoscopy (MT). Diagnostic confidence for TBE and MPE was determined based on final thoracoscopic diagnosis. Logistic regression and RF models were applied to predefined clinical, imaging, and laboratory variables.

RESULTS

Among 187 recruited patients, 132 (70 TBE and 62 MPE) subjects with at least moderate diagnostic confidence were analyzed. On TUS, TBE was more likely to be non-hyperechoic, complex, and loculated, with the presence of fibrin and less pleural thickening, while MPE showed hyperechoic effusion and hemidiaphragm nodules. Independent predictors for TBE included younger age [adjusted odds ratio (aOR) =0.94], presence of fibrin (aOR =2.86), lower Pf lactate dehydrogenase (LDH) (aOR =0.996), and higher Pf adenosine deaminase (ADA) (aOR =1.10). Without Pf ADA, these parameters, along with the absence of hemidiaphragm nodules (aOR =0.33), remained significant for TBE prediction. The RF model outperformed logistic regression (92.6% vs. 87.9% accuracy), even without Pf ADA.

CONCLUSIONS

TUS parameters can aid in differentiating TBE from MPE. The RF model demonstrated superior diagnostic performance, offering a potential point-of-care tool, even in the absence of Pf ADA.