Easy-to-Use Scoring System Based on CT Pleural Fluid and Blood Features for Discriminating Malignant Pleural Effusions from Tuberculous Pleural Effusions: A Retrospective and Prospective Validation Study

Abstract

Background: Distinguishing malignant pleural effusion (MPE) from tuberculous pleural effusion (TPE) is a challenge when symptom is atypical. Computed tomography (CT) scans and biochemical indicators are the most commonly used methods to evaluate pulmonary and pleural diseases, and the role of CT features (especially pleural and pleural fluid features) in the diagnosis of MPE or TPE is poorly described. Therefore, this study aims to establish and verify diagnostic models based on the characteristics of pleural and pleural effusion for the differential diagnosis of MPE and TPE. Methods: In this study, a first batch of 986 patients from Wuhan union hospital and Wuhan Jinyintan Hospital in the training set was used to develop differential diagnosis models. The CT features (including maximum pleural thickness at the non-mass/nodule, density of the pleural effusion, pleural mass/nodule, mediastinal pleural involvement, pleural wrap, pleural calcifications) and biochemical markers (including serum carcinoembryonic antigen (CEA), and adenosine deaminase (ADA) in the pleural fluid) were collected and subjected to logistic regression analyses. Then, two models were constructed: a model based on CT features (Model 1) and a model based on pleural fluid markers with or without blood biochemical markers (Models 2a/2b). The differential diagnostic model was displayed in the form of nomograms, which were validated by an independent prospective cohort (N=422). Findings: In the training set, eight variables were selected as significant risk factors for distinguishing MPE from TPE after multivariable regression analyses, i.e. age, sex, maximum pleural thickness at the non-nodule/mass, pleural fluid density, pleural nodule/mass, serum CEA, effusion CEA and effusion ADA. In Model 1, the area under the Receiver Operating Characteristic Curve (AUROC) was 0.970, yielding a sensitivity and specificity of 91.07% and 89.86% respectively in the training set, and AUROC = 0.968, sensitivity and specificity was 93.85% and 87.73% respectively in the validation set. In Model 2a, the AUROC, sensitivity and specificity for distinguishing MPE from TPE were 0.976, 92.73% and 93.66% in the training set, respectively, and 0.959, 93.50% and 87.84% in the validation set, respectively. In Model 2b, the AUROC, sensitivity and specificity for distinguishing MPE from TPE were 0.970, 94.87% and 90.87% in the training set, respectively, and 0.962, 96.77% and 80.25% in the validation set, respectively. There were no significant differences between the three models. Interpretation: CT features of pleura and pleural fluid could effectively help physicians differentiate between MPE and TPE, which were readily acquired and could be used as a non-invasive and convenient diagnostic tool in clinical practice. Trial Registration: This study was registered on the Clinical Trials website (No. NCT03997669). Funding Statement: This research was supported by the National Natural Science Foundation of China (No. 81770096; No. 81800094), the National Science and Technology Key Project (No. 2019ZX09301001). Declaration of Interests: The authors declare no competing non-financial/financial interests. Ethics Approval Statement: This study was approved by the Institutional Review Board of the Tongji Medical College, Huazhong University of Science and Technology, and supported by the Wuhan Clinical Research Center for Pleural Diseases