After After
Before Before

10165 - PCK VDS, Tumor Detection

DEVELOPED FOR TUMOR DETECTION IN PCK STAINED BREAST TISSUE

This APP has been developed to improve quantification of ER positive cells within breast carcinoma, by automatically identifying tumor areas. The APP uses the Virtual Double Staining (VDS) technique, which enables automated and robust detection of tumor regions.

Two serial sections stained for ER and Pancytokeratin (PCK), respectively, must be used in this APP. Tumor regions are identified automatically on the PCK stained slides and the outlined tumor region are overlaid on the ER stained slide, thus automatically identifying tumor regions.

KEYWORDS
VDS, Virtual Double Staining, breast carcinoma, immunohistochemistry, breast cancer, quantitative, digital pathology, image analysis, PCK, Cytokeratin, TMA

METHODS
To employ the VDS approach for ER, two serial sections must be stained for ER and PCK, respectively. This APP works as a three-step process as described below.

First, alignment of the two serial sections (see FIGURE 3). The alignment is done both on a large scale, and on a finer detailed level, to get the best possible match of the two tissue sections.

Second, the tumor areas are automatically detected from the PCK slide and outlined as regions of interest (ROIs) (see FIGURE 4 and 5). The ROIs are then superimposed on the aligned ER tissue slide to outline the tumor region for subsequent analysis limited to the inside of the tumor regions.

QUANTITATIVE OUTPUT VARIABLES
The output consists of one or more ROIs, outlining the tumor areas. The output variable obtained from this protocol is:

• Tumor Area: The total area of the tumor regions measured in µm2

AUXILIARY APPs
There are no auxiliary APPs available.

WORKFLOW
Step 1: Load and run the APP “10165 – PCK VDS, Tumor Detection” in a region on the image of interest

STAINING PROTOCOL
There is no staining protocol available.

ADDITIONAL INFORMATION
This APP has been developed in cooperation with Professor Mogens Vyberg from NordiQC and Aalborg University Hospital Denmark.

The APP utilizes the Engine™ and Viewer software modules, where Engine™ 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 Author™ module the APP can be customized to fit other purposes. Author™ offers a comprehensive and dedicated set of tools for creating new fit-for-purpose analysis APPs, and no programming experience is required.

The APP also utilizes the module Tissuealign™, which gives the ability to align and subsequently analyze digitized serial sections. It also offers integrated VirtualDoubleStaining™ and VirtualMultiplexing™.

REFERENCES

LITERATURE
1. Hammond, E. H. et. al. American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Immunohistochemical Testing of Estrogen and Progesterone Receptors in Breast Cancer, Archives of pathology & laboratory medicine 2010, 134 (7), e48-72, DOI.

2. Kårsnas, A., Dahl, A. L., Larsen, R. Learning histopathological patterns, J. P. Inform. 2011, 2 (2), 12, DOI.

3. Jung, C., Changick, K. Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization, IEEE Transactions on Biomedical Engineering 2010, 57 (10), 2600-2604, DOI.

RUO
FIGURE 1
FIGURE 1
The original TMA-core stained with PCK.
FIGURE 2
FIGURE 2
The original TMA-core stained with ER.
FIGURE 3
FIGURE 3
Tissue alignment. The PCK and ER TMA-cores are aligned, one on top of the other.
FIGURE 4
FIGURE 4
Part of tissue stained with PCK.
FIGURE 5
FIGURE 5
The tumor region is segmented in the PCK tissue and outlined with a ROI. The ROI obtained from the PCK tissue is superimposed on the aligned ER issue (not seen here). The ROI only outlines tumor regions. Tissue that is not tumor and the background are excluded.