Development and validation a simple model for identify malignant ascites.

The differential diagnosis of benign ascites and malignant ascites is incredibly challenging for clinicians. This research aimed to develop a user-friendly predictive model to discriminate malignant ascites from non-malignant ascites through easy-to-obtain clinical parameters. All patients with new-onset ascites fluid were recruited from January 2014 to December 2018. The medical records of 317 patients with ascites for various reasons in Renmin Hospital of Wuhan University were collected and reviewed retrospectively. Thirty-six parameters were included and selected using univariate logistic regression, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses to establish a mathematical model for differential diagnosis, and its diagnostic performance was validated in the other groups. Age, cholesterol, hypersensitivity C-reactive protein (hs-CRP) in serum, ascitic fluid adenosine deaminase (AF ADA), ascitic fluid lactate dehydrogenase (AF LDH) involvement in a 5-marker model. With a cut-off level of 0.83, the sensitivity, specificity, accuracy, and area under the ROC of the model for identifying malignant ascites in the development dataset were 84.7%, 88.8%, 87.6%, and 0.874 (95% confidence interval [CI], 0.822-0.926), respectively, and 80.9%, 82.6%, 81.5%, and 0.863 (95% CI,0.817-0.913) in the validation dataset, respectively. The diagnostic model has a similar high diagnostic performance in both the development and validation datasets. The mathematical diagnostic model based on the five markers is a user-friendly method to differentiate malignant ascites from benign ascites with high efficiency.

High Efficient Isolation of Tumor Cells by a Three Dimensional Scaffold Chip for Diagnosis of Malignant Effusions.

High efficient detection of effusion tumor cells (ETCs) has great clinical significance to identify malignant from benign effusions, but few strategies are designed to enrich and identify tumor cells from effusions. Herein, we developed a three-dimensional scaffold microchip (3D scaffold chip) which could efficiently isolate individual ETC and ETC cluster (ETC/cluster) from pleural effusions and ascites by molecular recognition and physical obstruction. The 3D scaffold chip could enrich ETCs with 94.7% capture efficiency from 2 mL effusions in 20 min and was successfully applied to analysis of pleural effusions or ascites from 152 patients. The results showed that patients with malignant effusions possessed a much higher number of ETC/cluster than that of patients with benign effusions and receiver operating characteristic (ROC) analysis revealed that ETC/cluster count can be used as a complementary biomarker for diagnosis of malignant effusions. Finally, univariate and multivariate logistic regression analyses were adopted to find effusion variables with statistical difference in diagnosis of malignant effusions, and three variables (ETC/cluster count and effusion carcinoembryonic antigen) were selected to establish a three-marker predictive model for differentiating malignant and benign effusions in the training set. ROC analysis revealed that the AUC (area under the curve), sensitivity, and specificity of the predictive model were 0.939, 90.4%, and 91.8%, respectively. The three-marker predictive model was successfully applied to the validation set and proved that this model was promising for clinical diagnosis of effusions from patients.