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10002 - ER, Breast Cancer

INTRODUCTION
In immunohistochemistry (IHC), estrogen receptor (ER) is used to determine prognosis. The American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) recommends that the ER status of patients is determined on all invasive breast cancers and breast cancer recurrences [See REFERENCES: 1]. It is, additionally, recommended that the ER status of the tumor is considered positive if there are at least 1 % positive tumor nuclei in a tissue sample.

This protocol can be used to determine ER positivity and negativity in a tumor, and 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. By allowing the user to adjust the sensitivity for the detection of nuclei, and vary the threshold for differentiation between ER-positive and ER-negative nuclei, the protocol is functional on images with different staining intensities. All tumor regions are identified within a region of interest (ROI), which can be outlined manually.

KEYWORDS
Estrogen receptor, ER status, immunohistochemistry, breast cancer, 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 using an HDAB-DAB color deconvolution band to detect positively stained nuclei and a multiplication of the red and blue color band to detect negative nuclei. 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. Then the positive nuclei are subdivided into three categories based on staining intensities. As a post-processing step, nuclei areas that are too small are removed. The image obtained after post-processing [See EXAMPLES: figure 3] 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 REFERENCES: 4], this can be avoided [See EXAMPLES: figure 4]. This principle is implemented in the present APP. 

QUANTITATIVE OUTPUT VARIABLES
The output variable obtained from this protocol is the H-Score. 
The H-Score is calculated from the percentages of nuclei classified as 3+, 2+, 1+ (the three positive categories, where 3+ has the highest staining intensity) multiplying them with their grade:

- H-score = (Percentage of 3+) x 3 + (Percentage of 2+) x 2 + (Percentage of 1+)

Thus the H-Score is a value between 0 and 300 (0 if there are only negative cells, and 300 if all cells are positive with an intense stain), giving an indication of the ratio of positive cells while factoring in staining intensity.

Furthermore the Positive ratio of ER cells is also calculated:
- Neg Nuclei (#): The number of negative nuclei

- 1+ Nuclei (#): The number of 1+ classified nuclei

- 2+ Nuclei (#): The number of 2+ classified nuclei

- 3+ Nuclei (#): The number of 3+ classified nuclei

- Positive Percentage: The percentage 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
Staining protocols have been developed by the NordiQC and are available from their website. It is also possible to use the links below:

ER-runB17


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. AuthorTM offers a comprehensive and dedicated set of tools for creating new fit-for-purpose analysis APPs, and no programming experience is required. 


REFERENCES

LITERATURE
1. American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Immunohistochemical Testing of Estrogen and Progesterone Receptors in Breast Cancer, M. E. H. Hammond et al., J. Clin Oncol 28:2784-95, 2010.

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

3. 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

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

RUO
FIGURE 1
FIGURE 1
One field of view of the original image at 20X (scaled down to fit this space). ROIs have been outlined manually (green hatched line).
FIGURE 2
FIGURE 2
Using un-biased counting frames in the detection of nuclei ensures that each object is counted only once.
FIGURE 3
FIGURE 3
Detection of all nuclei within the ROIs. Positive nuclei are marked with a red (strong), orange (medium) and yellow (weak) label and negative nuclei are marked with a blue label.