Automated identification of circulating tumor cells by image cytometry

Abstract

Presence of circulating tumor cells (CTC), as detected by the CellSearch System, in patients with metastatic carcinomas is associated with poor survival prospects. CellTracks TDI, a dedicated image cytometer, was developed to improve the enumeration of these rare CTC. The CellSearch System was used to enumerate CTC in 7.5 mL blood of 68 patients with cancer and 9 healthy controls. Cartridges containing the fluorescently labeled CTC from this system were reanalyzed using the image cytometer, which acquires images with a TDI camera using a 40×/0.6 NA objective and lasers as light source. Automated classification of events was performed by the Random Forest method using Matlab. An automated classifier was developed to classify events into CTC, apoptotic CTC, CTC debris, leukocytes, and debris not related to CTC. A high agreement in classification was obtained between the automated classifier and five expert reviewers. Comparison of images from the same events in CellTracks TDI and CellTracks Analyzer II shows improved resolution in fluorescence images and improved classification by adding bright-field images. Improved detection efficiency for CD45-APC avoids the classification of leukocytes nonspecifically binding to cytokeratin as CTC. The correlation between number of CTC detected in CellTracks TDI and CellTracks Analyzer II is good with a slope of 1.88 and a correlation coefficient of 0.87. Automated classification of events by CellTracks TDI eliminates the operator error in classification of events as CTC and permits quantitative assessment of parameters. The clinical relevance of various CTC definitions can now be investigated.