Algorithm to identify circulating tumor cell clusters using in vivo flow cytometer

Abstract

Recent studies in oncology have addressed the importance of detecting circulating tumor cell clusters because circulating tumor cell clusters might survive and metastasize more easily than single circulating tumor cells. Signals with larger peak widths detected by in vivo flow cytometer (IVFC) have been used to identify cell clusters in previous studies. However, the accuracy of this criterion might be greatly degraded by variance in blood flow and the rolling behaviors of circulating tumor cells. Here, we propose a criterion and algorithm to distinguish cell clusters from single cells. In this work, we first used area-based and volume-based models for single fluorescent cells. Simulating each model, we analyzed the corresponding morphology of IVFC signals from cell clusters. According to the Rayleigh criterion, the valley between two adjacent peak signals from two distinguishable cells should be lower than 73.5% of the peak values. A novel signal processing algorithm for IVFC was developed based on this criterion. The results showed that cell clusters can be reliably identified using our proposed algorithm. Intravital imaging was also performed to further support our algorithm. With enhanced accuracy, IVFC is a powerful tool to study circulating cell clusters.

Keywords

• In vivo flow cytometer
• circulating tumor cell
• cell clusters
• signal processing algorithm
• computer simulation
• Rayleigh criterion