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90002 - ER APP, Breast Cancer

INTRODUCTION

In immunohistochemistry (IHC), estrogen receptor (ER) is used to determine prognosis and as a predictive marker for anti-estrogen in breast cancer. 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[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. Calibration of the protocol allows it to be used on images with different staining intensities. All tumor regions are identified within a region of interest (ROI), which can be outlined manually.

KEYWORDS

Cancer, Breast cancer, Estrogen receptor, ER, Digital pathology, Image analysis, IHC

METHODS

The first image processing step involves a segmentation of all nuclei in the ROI. This is done by using a 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 FIGURE 3] is used to determine the output variables.

Note on counting: Analyzing full virtual slides usually 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[2], this can be avoided [See FIGURE 2]. This principle is implemented in this 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 following output variables are 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

No auxiliary APPs are included.

WORKFLOW

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

Step 2: Load and run the 90002 ER APP, Breast Cancer to analyze the nuclei in tumor regions


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. The Viewer allows a fast review together with several types of image adjustment properties e.g. outlining of regions, annotations and direct measures of distance, curve length, radius, etc.


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

CE IVD
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.