Abstract 1733: Automated identification of circulating tumor cells by image analysis

• Published in: Cancer research (Chicago, Ill.) Ill.), 2017-07, Vol.77 (13_Supplement), p.1733-1733
• Main Authors: Zeune, Leonie L., Dalum, Guus van, Bidard, François-Clément, Pierga, Jean-Yves, Fehm, Tanja, Neubauer, Hans, Rack, Brigitte, Alunni-Fabbroni, Marianna, Crespo, Mateus, Bono, Johann de, Terstappen, Leon W.M.M., Brune, Christoph
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2017-1733

In the field of Circulating Tumor Cell (CTC) research many new technologies are emerging to isolate CTCs. Some of them provide accompanying automated image analysis tools that present possible CTCs to the user. Others need fully manual image analysis. For all CTC isolation technologies the definition of a CTC based on the immuno-morphologic criteria is either customized to the specific platform or subjective to the user causing high interreader differences – a problem which may condemn many CTC-based clinical studies to failure. Thus, an important issue that the field is confronted with is the lack of a unified and standardized definition to classify a cellular object as a CTC. This problem is addressed within the European FP7 consortium CTCTrap and the Innovative Medicines Initiative (IMI) consortium CANCER-ID by the development of an open-source image analysis toolbox for CTC identification and enumeration. This toolbox is baptized ACCEPT (Automated CTC Classification, Enumeration and Phenotyping) and can process images generated by various CTC isolation technologies. The main software components are the Marker Characterization, the Full Detection and the Automatic Classification. The Marker Characterization tool aims at quantifying the antigens expressed by previously selected CTCs. The Full Detection tool is based on advanced mathematical methods to reliably detect all objects in the images, visualize the objects in scatter plots and enable the user to classify the cell types by the use of gates or selection of specific objects in the scatter plots or on the actual images. The Automatic Classification tool first detects all objects in the images followed by an automated classification approach that – as a result – presents found CTCs to the user. We demonstrate the effectiveness of these tools on two different datasets. The Marker Characterization tool was tested for Her2 expression on archived CTC images isolated and classified by the CellSearch system from patients with metastatic breast cancer. Investigators from three different institutes were asked to score these cells for Her2 positivity first on the images generated by the CellTracks Analyzer and afterwards using ACCEPT. We show that the improved CTC visualization provided in ACCEPT, combined with several measurements which we extract for each cell, can reduce the inter-user variability. The Full Detection and Automatic Classification tools of ACCEPT were tested on archived samples of patients w