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Decision Support & AI


Ki67 expression varies throughout the different cell-cycle phases. Cells express the ki67 during G1, S, G2, and M phases, but not during the resting phase G0 [1].

Ki67 marker has been explored as a prognostic or predictive marker in breast cancer and other malignant diseases [2].

In general, scoring systems are based on the percentage of tumour cells stained by the antibody. In one method, the pathologist examines the stained section with a standard light microscope 40x objective, and the Ki67 score is defined as the percentage of total number of tumour cells with nuclear staining.

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Ki67, Immunohistochemistry, Breast Cancer, Digital Pathology


The techniques of nuclear classification algorithm is used to detect the nuclei in morphological form and size and classify them as positive or negative according to pixel colorization and intensity. Also, tile extraction, color deconvolution, morphological filters, threshold value method,and cell segmentation algorithms are used to develop Ki67 analysis.

The analysis algorithm contain H-DAB colour deconvolution method. To detect negative nuclei H channel, and to detect and classify brown nuclei DAB channel are used. The positive nuclei are classified into three groups according to given threshold values by the user before running the analysis.

Quantitative output variables
  • Negative Nuclei Number
  • Dark Brown Positive Nuclei Number
  • Medium Brown Nuclei Number
  • Light Brown Nuclei Number
  • Stromal Nuclei Number
  • Positivity index (%)

Step 1: View a Ki-67 stained whole slide images at ViraPath.
Step 2: Outline tumor either manually or automatically using Virapath Tissue Segmentation algorithm.
Step 3: Select the Ki67 analysis and calibrate the parameters.
Step 4: Run the analysis.


[1] Beresford MJ, Wilson GD, Makris A. Measuring proliferation in breast cancer: practicalities and applications. Breast Cancer Res 2006; 8: 216.

[2] Yerushalmi, R., Woods, R., Ravdin, P. M., Hayes, M. M., & Gelmon, K. A. (2010). Ki67 in breast cancer: prognostic and predictive potential. The Lancet Oncology, 11(2), 174–183.

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