Supplementary Material for: An integrated clinical and CT-based radiomics feature model to separate benign from malignant pleural effusion

Introduction: Distinguishing between malignant pleural effusion (MPE) and benign pleural effusion (BPE) poses a challenge in clinical practice. We aimed to construct and validate a combined model integrating radiomic features and clinical factors using computerized tomography (CT) images to differentiate MPE and BPE. Methods: A retrospective inclusion of 315 patients with pleural effusion (PE) was conducted in this study (training cohort: n = 220; test cohort: n = 95). Radiomic features were extracted from CT images, and the dimensionality reduction and selection process were carried out to obtain the optimal radiomic features. Logistic regression (LR), support vector machine (SVM), and random forest (RF) were employed to construct radiomic models. Logistic regression analyses were utilized to identify independent clinical risk factors to develop a clinical model. The combined model was created by integrating the optimal radiomic features with the independent clinical predictive factors. The discriminative ability of each model was assessed by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: Out of the total 1834 radiomic features extracted, 15 optimal radiomic features explicitly related to MPE were picked to develop the radiomics model. Among the radiomics models, the SVM model demonstrated the highest predictive performance[area under the curve (AUC),training cohort:0.876,test cohort:0.774]. Six clinically independent predictive factors, including age, effusion laterality, procalcitonin (PCT), carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125), and neuron-specific enolase (NSE), were selected for constructing the clinical model. The combined model (AUC:0.932,0.870) exhibited superior discriminative performance in the training and test cohorts compared to the clinical model (AUC:0.850,0.820) and the radiomics model (AUC:0.876,0.774). The calibration curves and DCA further confirmed the practicality of the combined model. Conclusion: This study presented the development and validation of a combined model for distinguishing MPE and BPE. The combined model was a powerful tool assisting in the clinical diagnosis of PE patients.