This novel turbulence device provides a new tool allowing for feasible and specific detection of CTCs in lung cancer patients. It is likely clinically useful in diagnosis and monitoring of lung cancer and may have a role in clinical decision making.

Background Malignant pleural effusion (MPE) is a common complication in cancer patients, indicating the presence of pleural metastasis. However, the ability to identify MPE clinically is still limited. The aim of this study was to develop a novel nomogram-based model for predicting MPE. Methods Between July 2020 and May 2022, a total of 428 patients with pleural effusion (PE) were consecutively enrolled. Demographic data, laboratory test results, and pathological parameters were collected. The LASSO regression method was used to select potential variables, and a multivariate logistic regression method was employed to construct a nomogram. Internal validation was performed using a bootstrapping method, and the nomogram's performance was evaluated based on calibration, discrimination, and clinical utility. Results Out of the 428 patients with PE, 217 (50.7%) were diagnosed with MPE. A diagnostic model was established using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression, which identified four variables: serum carcinoembryonic antigen (sCEA), serum neuron-specific enolase (sNSE), pleural carcinoembryonic antigen (pCEA), and pleural lactate dehydrogenase (pLDH). The internal validation of the model showed an area under the curve (AUC) of 0.894 (95% CI: 0.864–0.934). The model was well-calibrated, and decision curve analysis (DCA) indicated that using the proposed nomogram to predict MPE would obtain a net benefit if the threshold probability of MPE was between 5% and 95%. Conclusion This study aimed to construct a nomogram that includes four demographic and clinical characteristics of patients with PE. The nomogram can be highly beneficial in distinguishing between MPE and benign pleural effusion (BPE).