Validation of a Microfluidic Device Prototype for Cancer Detection and Identification: Circulating Tumor Cells Classification Based on Cell Trajectory Analysis Leveraging Cell-Based Modeling and Machine Learning

Abstract

AbstractMicrofluidic devices (MDs) present a novel method for detectingcirculating tumor cells(CTCs), enhancing the process through targeted techniques and visual inspection. However, current approaches often yield heterogeneous CTC populations, necessitating additional processing for comprehensive analysis and phenotype identification. These procedures are often expensive, time-consuming, and need to be performed by skilled technicians. In this study, we investigate the potential of a cost-effective and efficient hyperuniform micropost MD approach for CTC classification. Our approach combines mathematical modeling of fluid-structure interactions in a simulated microfluidic channel with machine learning techniques. Specifically, we developed a cell-based modeling framework to assess CTC dynamics in erythrocyte-laden plasma flow, generating a large dataset of CTC trajectories that account for two distinct CTC phenotypes. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were then employed to analyze the dataset and classify these phenotypes. The results demonstrate the potential effectiveness of the hyperuniform micropost MD design and analysis approach in distinguishing between different CTC phenotypes based on cell trajectory, offering a promising avenue for early cancer detection.Author summaryEarly detection is currently the most effective method to combat cancer, as it maximizes treatment options and improves potential survival rates. However, the cost of early detection presents a significant barrier, limiting access for underrepresented groups and discouraging industrial partners from investing in the research and development of screening devices. This study provides an in-silico conceptual validation for the development of an innovative hyperuniform microchip designed to identify circulating tumor cells (CTCs) without the need for biomarker labeling. We created a cell-based modeling framework to examine CTC dynamics in erythrocyte-laden plasma flow, producing an extensive dataset of CTC trajectories that reflect two distinct CTC phenotypes. Two machine learning architectures were utilized to analyze this dataset and classify the phenotypes. The results demonstrate the potential effectiveness of the hyperuniform micropost MD design and analysis approach in distinguishing between different CTC phenotypes based on cell trajectory, offering a promising and cost-effective method for early cancer detection.