Exploring prognostic precision: a nomogram approach for malignant pleural effusion in lung cancer - Yongjie Jiang; Xin Hu; Yiluo Heibi; Hang Wu; Taibing Deng; Li Jiang (BMC Cancer, 2025)
Abstract Background Patients with lung cancer and malignant pleural effusion (MPE) often have poor prognoses. Accurate prognostic tools are needed to guide interventions and improve outcomes. Methods We retrospectively analyzed clinical and imaging data from MPE patients at two medical centers. A nomogram was developed and externally validated. Clinical and imaging features were refined using least absolute shrinkage and selection operator (LASSO), and independent predictors were identified via multivariate logistic regression. Predictors were integrated into the nomogram, whose predictive performance, calibration, and clinical utility were evaluated using statistical analyses, including receiver operating characteristic (ROC) curves, calibration curves, Hosmer-Lemeshow tests, and decision curve analysis (DCA). Survival curves illustrated prognostic differences among risk groups. Results The final nomogram included five variables: Lactate Dehydrogenase (LDH) levels in pleural fluid, clarity of pleural effusion, treatment regimen, presence of pericardial effusion, and total volume of pleural effusion. In both cohorts, the nomogram demonstrated strong predictive accuracy (Area Under the Curve (AUC): 0.929 and 0.941, respectively) and excellent calibration (Hosmer-Lemeshow test p-values: 0.944 and 0.425, respectively). DCA confirmed the nomogram’s clinical utility. Risk stratification revealed significant survival disparities among patients. Conclusion Our nomogram accurately predicts the prognosis of lung cancer patients with MPE at initial diagnosis, incorporating key variables such as LDH levels in pleural fluid, clarity of pleural effusion, treatment regimen, pericardial effusion, and total volume of pleural effusion. Its robust predictive performance, calibration, and clinical utility support its use in guiding clinical decision-making for this patient population.