Finding patient‑specific features and providing precision medicine with AI
Cancer exhibits substantial biological and spatial heterogeneity. Our AI-based spatial analysis technology enables quantitative characterization of the tumor microenvironment and provides deeper insights into patient-specific disease states, thereby supporting the identification of more appropriate therapeutic strategies.
Value for BioPharma and Clinical Labs
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Discovery of novel spatial signatures associated with drug efficacy
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Characterization of heterogeneous tumor architecture
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Quantification of spatial clustering between cancer cells and immune cells
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Spatial statistical modeling using multiplex IHC serial sections
Using both pathologist annotation and masked data with Immunofluorescence
as training data enables the detection of indistinguishable cells.
Predicting genomic mutation from morphological features [Publication]
A spatial combination of the obtained features is used to predict patient prognosis and drug efficacy. [Publication]
Collaborators
