Ki-67, Breast Cancer Ki-67, Breast Cancer

Process APP




Ki-67, Breast Cancer

The Ki-67 protein is associated with cellular proliferation, and the protein is present in the nucleus of all cells that are in the active phase of the cell cycle, but absent in resting cells, see [1]. The cell proliferation rate can be assessed by Ki-67-immunohistochemical (IHC) staining, and this can be correlated to the tumor grade and the clinical course, see [2].

This protocol can be used to assess tumors by determining the Ki-67 positivity. The protocol detects and classifies nuclei as positive or negative and returns an average proliferation index for the entire tumor region. Tumor regions must be identified and outlined manually within a region of interest (ROI). By allowing the user to adjust the sensitivity for the detection of nuclei, and vary the threshold for differentiation between positive and negative nuclei, the protocol can be used on images with different staining intensities. 


Figure 1

Figure 1

Manual outline of tumor region.

Figure 2

Figure 2

Ki-67 analysis with classification of positive (red) and negative (blue) nuclei. Using un-biased counting frames in the detection of nuclei ensures that each object is counted only once.

Figure 3

Figure 3

Finished Ki-67 analysis within the outlined tumor region.


Quantitative Output variables

The output variables obtained from this protocol are:

  • Neg Nuclei (#): The number of Ki-67 negative nuclei within all ROIs
  • Pos Nuclei (#): The number of Ki-67 positive nuclei within all ROIs
  • Total Nuclei (#): The total number of nuclei within all ROIs
  • Proliferation index (%): The Ki-67 proliferation index


Step 1: Manually outline tumor areas as regions of interest (ROIs)

Step 2: Load and run the APP 10004 Ki-67, Breast Cancer to analyze the nuclei in tumor region(s).


The APP works within an automatically or manually outlined tumor region of interest (ROI), see FIGURE 1. Within each ROI, nuclei are detected and classified into Ki-67 positive or negative. The first image processing step involves a segmentation of all nuclei in the ROI. The HDAB-DAB color deconvolution band is used to detect positively stained nuclei and a multiplication of the red and blue color band is used to detect negative nuclei. A method for nucleus separation which is based on shape, size and nuclei probability is used, employing a fully automated watershed-based nucleus segmentation technique. As a post-processing step, nuclei areas that are too small are removed. The image obtained after post-processing is used to determine the output variables.

Note on counting: Analysis of full virtual slides takes place in a tile-by-tile fashion. If not handled appropriately, nuclei that are intersecting with neighboring tile boundaries would be counted twice (or more). By using unbiased counting frames, see [3], this can be avoided, see FIGURE 2. This principle is implemented in the present APP. Depending on the size of nuclei, the application of this principle could make an important difference.


Cancer, Breast cancer, Ki-67, Ki67, Proliferation index, Digital pathology, Image analysis, IHC


This APP was developed in cooperation with Professor Mogens Vyberg from NordiQC and Aalborg University Hospital.

1.Scholzen, T. et. al. The Ki-67 protein: from the known and the unknown, J. Cell Physiol. 2000, 182 (3), 311-22, DOI.

2.NordiQC Assesments (Last updated: 02.07.09).

3Howard, C.V., Reed, M.G. (2005). Unbiased Stereology: Three-Dimensional Measurement in Microscopy. QTP Publications.

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