• Rare-event detection;
  • automated microscopy;
  • minimal residual disease;
  • immunocytochemistry;
  • image processing


The detection of rare-event cells circulating in peripheral blood using automated image analysis was evaluated using a model system consisting of cells from a breast cancer cell line (SKBR3) seeded in a mononuclear cell suspension. Slides of cells with optimal morphology were prepared according to an optimized preparation procedure based on centrifugal cytology in combination with formalin fixation.

SKBR3 cells were immunocytochemically stained for cytokeratin using the cam 5.2 monoclonal antibody and labelled with alkaline phosphatase using CAS-red as substrate. Because, for optimal segmentation of cell images, plain differences in absorption wavelength are required, the red immunostaining was combined with a green nuclear counterstaining based on ethyl green. Slides were automatically screened for cytokeratin-positive SKBR3 cells resulting in a lowest detectable frequency of one positive cell per 1.87 × 106 negative cells. A comparison between manual screening and automated screening for cytokeratin-positive cells showed a high level of correlation (0.9998).

For the definition of the total number of objects per slide, two counting procedures were evaluated. Results were close to the visual score with a coefficient of variation of 0.47% for the counting procedure used in this study.

It is concluded that optimization of preparation and staining procedures for the detection of rare-event cells using automated image analysis results in optimal image contrast and, consequently, in an increase in sensitivity for detecting rare events. © 1994 Wiley-Liss, Inc.