After After
Before Before

10118 - H&E, Lung Metastasis

Every year, more than 1.4 million women worldwide are diagnosed with breast cancer and over 450,000 women will lose their lives to this disease, mostly due to metastases to bones, lungs, liver or brain. By using genetically engineered mouse models of metastatic breast cancer (for example: MMTV-PyMT) or orthotopic transplantation of primary breast cancer cells, the effect of various treatment regimens (systemic therapy, targeted therapy) on pulmonary metastasis formation can be determined (1-3).

This protocol can be used to quantify metastases in H&E stained lung sections. The APP detects the total area of the lung tissue, delineates and separates the metastases and quantifies the area for each metastatic nodule. In the end, the total tissue area, the number of metastases, the area of each metastasis and the metastatic area percentage are given.

Breast cancer, lung, metastasis, HE, H&E stained sections, quantitative digital pathology, image analysis, tumor progression

The first image processing step involves a segmentation of the lung tissue [see Examples: Figure 1]. Afterwards the metastases are identified together with the regions that are either too misshaped, too red or too sparse for being identified as metastases. [see Examples: Figure 5]. The parameters defining the shape and sparseness of the metastases can easily be adjusted by the user.

The output variables obtained from this protocol include:

  • Metastasis Count: The total number of metastasis inside all lung tissue
  • Metastasis Area: The total area of metastasis inside all lung tissue
  • Total Tissue Area: The total area of lung tissue
  • Metastasis Fraction: Metastasis Area / Total Tissue Area
  • Metastasis Area Percentage: Metastasis Fraction * 100
  • Too Misshaped Count: The number of too misshaped regions
  • Too Misshaped Area: The area of too misshaped regions
  • Too Red Count: The number of too red regions
  • Too Red Area: The area of too red regions
  • Too Sparse Count: The number of too sparse regions
  • Too Sparse Area: The area of too red regions
  • Area per Metastasis: A file containing the areas of each metastatic nodule
  • Obj ID: A file containing the positions of each metastatic nodule

AUXILIARY APPS (included) 
APP: “01 Tissue Detect”
This APP can be used for automatic and precise identification of tissue present on image.

APP: “03 Analyze”
This APP analyzes the identified metastases and non-metastases, and outputs the analysis results described in "QUANTITATIVE OUTPUT VARIABLES".

Step 1: Load the APP for tissue detection “01 Tissue Detect” which outlines the lung tissue in the image.
Step 2: Load the APP for metastases detection “02 Metastasis Detect” which identifies the metastases inside the lung tissue.
Step 3: Load the quantification protocol “03 Analyze”. Click the save button to transfer the results to the database. 

There is no staining protocol available.

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.

1. Fantozzi A, Christofori G. Mouse models of breast cancer metastasis. Breast Cancer Research. 2006;8
2. Saxena M, Christofori G. Rebuilding cancer metastasis in the mouse. Molecular Oncology. 2013; 7.
3. Lin EY, Jones JG, Li P, et al. Progression to Malignancy in the Polyoma Middle T Oncoprotein Mouse Breast Cancer Model Provides a Reliable Model for Human Diseases. The American Journal of Pathology. 2003;163(5):2113-2126.


Result of analysis with APP: “01 Tissue Detect”. The lung tissue is automatically outlined.
Close up of lung tissue.
Result of the analysis showing the identified metastases (blue labels) inside the lung tissue.
Close up of lung tissue.
Example showing the metastases (blue labels), a region which is too misshaped (orange label) and regions which are too sparse (white labels).