Classification of cells in CTC enriched samples by advanced image analysis

Sanne Mutter-de Wit, Leonie Laura Zeune (Corresponding Author), T. Jeroen N. Hiltermann (Corresponding Author), H.J.M. Groen (Corresponding Author), Guus van Dalum (Corresponding Author), L.W.M.M. Terstappen (Corresponding Author)

Research output: Contribution to journalArticleAcademicpeer-review

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In the CellSearch® system, blood is immunomagnetically enriched for epithelial cell adhesion molecule (EpCAM) expression and cells are stained with the nucleic acid dye 4′6-diamidino-2-phenylindole (DAPI), Cytokeratin-PE (CK), and CD45-APC. Only DAPI+/CK+ objects are presented to the operator to identify circulating tumor cells (CTC) and the identity of all other cells and potential undetected CTC remains unrevealed. Here, we used the open source imaging program Automatic CTC Classification, Enumeration and PhenoTyping (ACCEPT) to analyze all DAPI+ nuclei in EpCAM-enriched blood samples obtained from 192 metastatic non-small cell lung cancer (NSCLC) patients and 162 controls. Significantly larger numbers of nuclei were detected in 300 patient samples with an average and standard deviation of 73,570 ± 74,948, as compared to 359 control samples with an average and standard deviation of 4191 ± 4463 (p < 0.001). In patients, only 18% ± 21% and in controls 23% ± 15% of the nuclei were identified as leukocytes or CTC. Adding CD16-PerCP for granulocyte staining, the use of an LED as the light source for CD45-APC excitation and plasma membrane staining obtained with wheat germ agglutinin significantly improved the classification of EpCAM-enriched cells, resulting in the identification of 94% ± 5% of the cells. However, especially in patients, the origin of the unidentified cells remains unknown. Further studies are needed to determine if undetected EpCAM+/DAPI+/CK-/CD45- CTC is present among these cells.
Original languageEnglish
Article number377
Number of pages16
Issue number10
Publication statusPublished - 10 Oct 2018


  • Cellsearch®
  • Circulating tumor cells
  • Classification
  • Deep learning
  • EpCAM
  • Image analysis
  • Leukocytes
  • Non-small cell lung cancer


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