After After
Before Before
10004 - Ki-67, Breast Cancer

INTRODUCTION

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 REFERENCES: 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 REFERENCES: 2].

This protocol can be used to assess tumors by determining the Ki-67 positivity. The protocol provides the number of positive nuclei as well as the total number of nuclei. In addition, the ratio of positive nuclei and the area ratio of positive nuclei are given. 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. 

KEYWORDS

Ki-67, Ki67, proliferation rate, immunohistochemistry, quantitative, digital pathology, image analysis.

METHODS

The first image processing step involves a segmentation of all nuclei in the ROI [See EXAMPLES: figure 1 and 2]. This is done by assigning a label probability to all pixels in the image, resulting in a label probability image. The label probability image is found by an intensity dictionary - a dictionary with small image patches. The intensity dictionary can be coupled to a label dictionary from which the label probability image is obtained. Based on this image, segmentation of nuclei can be done by choosing the most probable label in each pixel [See REFERENCES: 3]. Pixels that contribute to positively stained nuclei are identified based on a DAB color deconvolution, whereas pixels of the negative class are identified by their Hematoxylin stain. A method for nuclei separation which is based on shape, size and nuclei probability is used, employing a fully automated watershed-based nuclei segmentation technique. The method is an extension of the method in Jung and Kim [See REFERENCES: 4], where an h-minima transform is used before applying the watershed. The image obtained after post-processing [See EXAMPLES: figure 3] is the basis for quantification of 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 REFERENCES: 5], this can be avoided [See EXAMPLES: figure 4]. This principle is implemented in the present APP. Depending on the size of nuclei, the application of this principle could make an important difference.

QUANTITATIVE OUTPUT VARIABLES

The output variables obtained from this protocol include: 

- Neg Nuclei (N): The number of negative nuclei profiles

- Pos Nuclei (N): The number of positive nuclei profiles

- Total Nuclei (N): The total number of nuclei profiles

- Proliferation Index (N) % = Pos Nuclei (N) / Total Nuclei (N): The ratio of number of positive nuclei profiles

AUXILIARY APPS (included)

Auxiliary APPs are used for additional process steps, e.g. finding Region of Interest (ROI).

There are no Auxiliary APPs available.

STAINING PROTOCOL

There is no staining protocol available

ADDITIONAL INFORMATION

The APP utilizes the EngineTM and Viewer software modules, where EngineTM offers an execution platform to expand processing capability and speed of image analysis. Viewer gives a fast review together with several types of image adjustment properties ex. outlining of regions, annotations and direct measures of distance, curve length, radius, etc. 
By adding the AuthorTM module the APP can be customized to fit other purposes. AuthorTMoffers a comprehensive and dedicated set of tools for creating new fit-for-purpose analysis APPs, and no programming experience is required.

REFERENCES

LITERATURE

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

2. www.nordiqc.org/epitope.php?id=1

3. Learning histopathological patterns, A. Kårsnäs, A. L. Dahl, R. Larsen, Journal of Pathology Informatics, 2(2):12, 2011

4. Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization, C. Jung et al., IEEE Transactions on Biomedical Engineering 2010, 57(10): 2600-2604

5. Unbiased Stereology, C.V. Howard & M.G. Reed, QTP Publications

RUO
FIGURE 1
FIGURE 1
Field of view showing invasive tumor in an image of breast tissue stained by IHC for Ki-67. The tumor ROI has been outlined manually (green hatched line). Ki-67-positive nuclei (Brown: DAB) and Ki-67-negative nuclei (Blue: Hematoxylin) are present within the ROI.
FIGURE 2
FIGURE 2
A field of view comparable to that of Figure 1 including an un-biased counting frame in order to ensure that nuclei within the ROI are counted only once.
FIGURE 3
FIGURE 3
The same field of view as in Figure 1 after classification of all detected nuclei within the ROI as either Ki-67-positive (red label) or Ki-67-negative (blue label). From this classification, the output variables can be calculated.