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

Abstract

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.

Development and validation of a novel scoring system developed from a nomogram to identify malignant pleural effusion

“…Although numerous clinical features are different between MPE patients and patients with BPE, the clinical significance of a single index in the discrimination between the two conditions is quite limited due to either its low sensitivity or specificity. In recent years, with the development of analytical approaches, the construction of mathematical models based on multiple markers has been increasingly applied in the field of medicine [26] , [27] , [28] , [29] , [30] . This approach combines a series of significant parameters to generate a predictive model to achieve a better diagnostic performance.…”

Driverless artificial intelligence framework for the identification of malignant pleural effusion

“…In our previous work [ 24 ], we succeeded to design a three dimensional scaffold microchip which could efficiently isolate individual effusion tumor cell (ETC) and ETC cluster from effusions. Then we used logistical regression analysis to create a three-marker (effusion CEA, ETC count and ETC cluster count) predictive model, and this predictive model obtained excellent diagnostic performances both in the training and validation sets.…”

Development and validation a simple model for identify malignant ascites

“…In 2000, Alexandrakis et al 8 reported a scoring model including TP, LDH, TNF-α, C4, and haptoglobin, with an accuracy of 89% in the overall date and 70% in the cross-validation date. Tian et al 48 also calculated scores for diagnosing malignant ascites, including innovative features-effusion CEA, effusion tumor cells (ETC), and ETC cluster count, with an AUC of the training dataset and validation dataset of 0.939 and 0.948, respectively. We also developed a predictive score model for the same purpose.…”