Determines HER2 gene amplification in breast cancer tissue sections stained by the DuoCISH™ assay from DAKO, using automated image analysis based on the Visiopharm HER2 CISH algorithm. This protocol calculates the ratio of HER2 (red signals) to centromere 17 (blue signals) within selected tumor regions, providing HER2 amplification status according to ASCO/CAP guidelines.

Determines the HER2/CEN-17 ratio in breast cancer tissue sections, calculating the total number of HER2 (red) and CEN-17 (green) signals in the same 20 nuclei. Ratios above 2 indicate amplification, while ratios below 2 indicate non-amplification, with caution advised for ratios between 1.8 and 2.2. The operator only needs to define an overall region of interest.

Detects HER2:HER2 and HER2:HER3 protein complexes using proximity ligation assays (PLAs) at a single-molecule level, allowing in situ study of signaling pathways. PLA has reproducibly identified significant associations between HER2 homodimerization, HER2 gene amplification, and poor prognosis, with high HER2:HER2 dimer levels linked to reduced relapse-free and overall survival.

Determines HER2 amplification in breast cancer tissue sections stained by the INFORM™ HER2 Dual ISH assay from Ventana/Roche, using the Visiopharm HER2 SISH algorithm for automated image analysis. This protocol calculates the ratio of HER2 (black signals) to centromere 17 (red signals) within selected tumor regions, providing HER2 amplification status according to ASCO/CAP guidelines.

The H&E Tumor Detection APP uses deep learning to automatically identify and outline tumor regions in H&E-stained brightfield images of different tissue types. It delivers accurate tumor ROIs and a quantitative total tumor area measurement (mm²).

Ensures quality control of HER2 expression in negative (0), 1+, 2+, and 3+ cell line materials by quantifying HER2 expression and comparing it to known reference values. This process improves the accuracy and reproducibility of IHC assays, eliminating variability associated with patient-derived reference standards.

mplements a standardized hot spot scoring method for Ki-67 and other biomarkers, reducing subjectivity and variability. Independent of biomarker and tissue type, this tool can be customized for different use cases, offering superior digital image analysis compared to manual methods.

Automatically segments glomeruli in IHC stained kidney tissue, identifying glomeruli in samples stained with biomarkers such as CD68, COL1A1, COLIV, synaptopodin, nephrin, WT1, and aSMA. Utilizes deep learning for robust and accurate segmentation.

This Quickstart APP will outline invasive tissue across a range of tissues in IHC-stained brightfield images. The Tumor Detection APP can be used after the Tissue Detection APP and prior to any of the Nuclei detection APPs. It has been tested with images of lung, breast and head and neck cancer and exports the total area of detected tumor in mm².

Detects and classifies nuclei based on iNOS staining expression in the tumor microenvironment (TME) of melanoma. The “10134 – iNOS, Melanoma, TME” protocol categorizes nuclei as negative, 1+, 2+, or 3+ according to the level of iNOS expression around each nucleus, aiding in the assessment of inflammation, immunosuppressive processes, and tumor progression.

Distinguishes between invasive and non-invasive tumor components in breast cancer specimens. This tool uses physically double stained (PDS) slides with p63 (myoepithelial cell nuclear marker) in brown and a cytokeratin (CK) tumor marker in red. By identifying invasive tumor regions, it allows for accurate assessment of biomarker expression, such as Ki-67 or ER, within the invasive components.

This app enables the QC and structuring of H&E images and provides an output of cell classification, tissue area segmentation, and biomarker quantification (e.g., TILs or tumor infiltrating lymphocytes). These biomarker signatures can be applied to use cases such as mechanism of action (MOA) analysis, building an H&E data lake, biomarker discovery, clinical trial screening or trial enrollment.