Breast ER AI for Breast Cancer Sample Analysis

ER IHC digital slide (left) with Breast ER AI markup (right) identifying negative (blue), weak (yellow), moderate (orange), and strong (red) cells. Tumor boundary highlighted with red lines.

Breast ER AI

Developed by Indica Labs, the Breast ER AI solution leverages deep learning to standardize the scoring of estrogen receptor (ER) in resections, excisions, or biopsies from primary invasive breast cancer samples. The algorithm reports quantitative results and markup images, including tumor detection, intensity score, Allred score, and percentage of weak, moderate, and strong positive ER cells.

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Breast ER AI is For Research Use Only and not intended for clinical diagnostic use. Breast ER AI accessed via the Aperio HALO AP image management system.

ER IHC-stained breast cancer

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Step 1: Breast ER IHC Image

Breast ER AI standardizes precision scoring of ER IHC-stained breast cancer resections, excisions and biopsy samples.

The algorithm can be launched by a user directly in the viewer or configured to automatically run for every breast ER case in Aperio HALO AP.

Breast ER AI tumor heatmap markup

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Step 2: Breast ER IHC Tumor Detection

An image analysis heatmap mask is generated, denoting regions within the sample containing invasive breast cancer.

Prior to this step, image analysis pre-processing excludes artifact and benign epithelial tissue.

The following metrics are calculated as outputs for each slide:

Tumor Area
Other Area
% Tumor Area
Classified Area

Breast ER AI markup

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Step 3: ER Cell Classification

Breast ER AI generates an image mask segmenting cells cell within the tumor region, denoting the following cell classes:

Negative ER Cell: Blue
Weak ER Cell: Yellow
Moderate ER Cell: Orange
Strong ER Cell: Red

Breast ER AI histogram

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Results: Histogram

Breast ER AI generates a histogram displaying the total number of cancer cells classified within the following categories:

ER Negative Cell
Weak ER Positive Cell
Moderate ER Positive Cell
Strong ER Positive Cell

Breast ER AI quantitative results

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Results: Quantitative

Breast ER AI generates the following metrics as output for the image:

Total Cells
% ER Positive Cells
% ER Cells (Negative, Weak, Moderate, Strong)
Number of ER Positive Cells
Number of ER Cells (Negative, Weak, Moderate, Strong)
Allred: 0-8
Intensity score: 0, 1, 2, 3

Breast ER AI in Aperio HALO AP Demonstration

Explore the future of pathology with the Breast ER AI app from Indica Labs. Breast ER AI seamlessly integrates within the Aperio HALO AP enterprise digital pathology platform to identify and score ER tumor cells in primary invasive breast cancer samples.

After selecting a case, view the IHC slide in the viewer and the Breast ER AI results. Results include overlays and quantitative results. The first set of overlays prepare the tissue for analysis by identifying areas of analyzable tissue. The next overlay identifies areas of tumor in the tissue. The final overlay identifies and classifies cells for ER positivity within the tumor area. Finally, quantitative results at a per slide level are displayed in the assay panel to the right.