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90003 - PR APP, Breast Cancer

INTRODUCTION

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

This protocol can be used to determine PR 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. Tumor regions must be identified and outlined manually within a region of interest (ROI). Calibration of the protocol allows it to be used on images with different staining intensities.

KEYWORDS

Cancer, Breast cancer, Progesteron receptor, PR, Digital pathology, Image analysis, IHC

METHODS

The first image processing step involves a segmentation of all nuclei in the ROI. 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[2]. Pixels belonging to both positive and negative nuclei are detected by performing a segmentation based on the red RGB color band in the image. 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 [3], where an h-minima transform is used before applying the watershed. Next, pixels that contribute to positively stained nuclei are identified based on a DAB color deconvolution of the image, and from this all pixels within each nucleus are classified as belonging to either a positive or negative class. As a post-processing step, nuclei areas that are too small are removed. The image obtained after post-processing [See FIGURE 2] 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[4], this can be avoided [See FIGURE 3]. This principle is implemented in this APP. Depending on the size of nuclei, the application of this principle could make an important difference.


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 90003 PR APP, Breast Cancer to analyze the nuclei in tumor regions


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.


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

CE IVD
FIGURE 1
FIGURE 1
Manual outline of a tumor region.
FIGURE 2
FIGURE 2
The same field of view as in FIGURE 1 after detection of nuclei as either PR-positive (red label) or PR-negative (blue label). From this classification, the output variables can be calculated.
FIGURE 3
FIGURE 3
A field of view including an un-biased counting frame, ensuring that nuclei are counted only once.