PR APP, Breast Cancer PR APP, Breast Cancer

Process APP




PR APP, Breast Cancer

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.

In US: For Research Use Only, not for use in diagnostic procedures.


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.




1. Allison KH, Hammond MEH, Dowsett M, McKernin SE, Carey LA, Fitzgibbons PL, Hayes DF, Lakhani SR, Chavez-MacGregor M, Perlmutter J, Perou CM, Regan MM, Rimm DL, Symmans WF, Torlakovic EE, Varella L, Viale G, Weisberg TF, McShane LM, Wolff AC. Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO/CAP Guideline Update. J Clin Oncol. 2020 Apr 20;38(12):1346-1366.

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

Quantitative Output variables

The primary outputs of the device are:

Positive Percentage,
which is a value between 0 and 100 and is calculated as

Positive Percentage = (Number of ER positive tumor nuclei)/(Total number of tumor nuclei) × 100

Allred Score,
which is a value between 0 and 8 and is calculated as

Allred Score = Proportion Score + Intensity Score

where Proportion Score is a value between 0 and 5 which reflects the positive percentage, and Intensity Score is a value between 0 and 3 which reflects the intensity of the ER positive nuclei.


The secondary outputs of the device are:

  • Neg Nuclei (#), which is the number of tumor nuclei negative for ER
  • Pos Nuclei (#), which is the number of tumor nuclei positive for ER
  • Total Nuclei (#), which is the total number of tumor nuclei
  • Allred Proportion Score
  • Allred Intensity Score


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


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.

Staining Protocol

There is no staining protocol available.

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