Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions.

OBJECTIVES: To establish a clinical radiomics nomogram that differentiates malignant and non-malignant pleural effusions. METHODS: A total of 146 patients with malignant pleural effusion (MPE) and 93 patients with non-MPE (NMPE) were included. The ROI image features of chest lesions were extracted using CT. Univariate analysis was performed, and least absolute shrinkage selection operator and multivariate logistic analysis were used to screen radiomics features and calculate the radiomics score. A nomogram was constructed by combining clinical factors with radiomics scores. ROC curve and decision curve analysis (DCA) were used to evaluate the prediction effect. RESULTS: After screening, 19 radiomics features and 2 clinical factors were selected as optimal predictors to establish a combined model and construct a nomogram. The AUC of the combined model was 0.968 (95% confidence interval [CI] = 0.944-0.986) in the training cohort and 0.873 (95% CI = 0.796-0.940) in the validation cohort. The AUC value of the combined model was significantly higher than those of the clinical and radiomics models (0.968 vs. 0.874 vs. 0.878, respectively). This was similar in the validation cohort (0.873, 0.764, and 0.808, respectively). DCA confirmed the clinical utility of the radiomics nomogram. CONCLUSION: CT-based radiomics showed better diagnostic accuracy and model fit than clinical and radiological features in distinguishing MPE from NMPE. The combination of both achieved better diagnostic performance. These findings support the clinical application of the nomogram in diagnosing MPE using chest CT.