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Number of positive and negative tumor cells and positive immune cells; Tumor positive score (TPS), Combined positive score (CPS).
Step 1: Open or upload the PD-L1 image / whole-slide image.
Please note that images work best at 20-40x resolution.
Step 2: Select the region of interest for analysis and see that it only takes a couple of seconds to process.
Step 3: Adapt the analysis results to fit your assessment.
You can change the classification of a single cell just clicking on it once and Mindpeak PD-L1 Quantifier will automatically update the result. Double click on a cell or on an empty area to add or remove cells. Select a specific area to analyse and the quantification will only be applied to the selected area.
Step 4: Get your score results on the left side menu.
Proprietary AI model for cell detection in membrane IHC-stainings based on convolutional neural networks.
Standard personal working machine. Minimum requirements: 32 bit Processor Intel Core i5 or better; 4 GB RAM.
All major whole slide image formats: (tif, mrxs, etc.), png, jpg/jpeg.
Lung, Cell quantification, Cell detection, IHC Membrane, Non-Small Cell Lung Cancer (NSCLC), Membrane Staining, PD-L1, Deep learning, Artificial Intelligence, Image analysis.
Mindpeak PD-L1 Quantifier for NSCLC assists pathologists in the challenging assessment of PD-L1 stained lung tissue. Mindpeak PD-L1 Quantifier helps experts by identifying and quantifying tumorous and inflammatory cells to help determine diagnostic scores.
Our PD-L1 Quantifier for NSCLC provides accurate results even in challenging contexts as it was developed with typical lab-specific variations in mind, such as pre-analytical slide preparation and different staining antibodies.