PathExplore
PathExploreTM offers unprecedented spatial and cellular resolution of the tumor microenvironment (TME) from H&E images.
PathExplore is an AI-powered panel of histopathology features that spatially characterize the tumor microenvironment (TME) with single-cell resolution1
Automated Tissue and Cell Detection
PathExplore’s underlying AI models classify both tissue regions and cell types across the entire image of a digitized H&E slide.
HIFs – Structured, Standardized, and Scalable TME Characterization
The number of cells and their spatial orientation within or near certain tissue regions are delivered as a panel of structured metrics called human interpretable features (HIFs).
Introducing PathExplore
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Robustly Trained, Trusted by Partners
>6.5 million
pathologist annotations
>66K slides
used for training
120K+ slides
deployed in research to date
15+
scientific presentations and publications
PathExplore delivers hundreds of standardized, reproducible HIF metrics to unlock insights into TME biology
Results across large-scale datasets can be achieved in days or weeks rather than months
PathExplore will continue to grow the number of disease areas and feature sets including nuclear morphology, stromal subtyping, and TLS
Harmonized features (i.e., Tumor Infiltrating Lymphocytes) across tumor types enables analysis of shared disease pathways and resistance mechanisms
Available across 8 cancer indications for scalable analysis across multiple development programs
Standardized TME spatial features for comparable analyses across eight cancer indications.
Scalable and efficient quantification of millions of cells across the entire H&E image.
Scalable and efficient quantification of millions of cells across the entire H&E image.
Proven Research and Applications
Improve
Improve quantification of established biomarkers and disease signatures.
Identify
Identify novel histological biomarkers and drivers of therapeutic efficacy.
Apply
Scale research and insights across disease areas and drug programs.
Case Study 1
PathExplore HIFs demonstrate biologically-relevant correlations with multi-omic hallmarks of cancer (HoC)
PathExplore HIFs demonstrate biologically-relevant correlations with multi-omic HoC.
Correlations between HIFs and HoC highlight the link between underlying disease biology and histopathology features. These insights may power new or deeper investigations behind mechanisms of action, biomarker expression, and patient outcome prediction.
Case Study 2
Novel H&E-Based Biomarkers
PathAI's analysis of the TME revealed a relationship between cancer cell nuclear morphology and outcomes in high-grade serous ovarian cancer (HGSOC).
Greater variability in nuclear size was correlated with reduced overall survival, providing support for the predictive power of biological events identified by HIFs.
Rapid, Scalable Deployment of PathExplore
PathExplore Data Access Program
PathAI is now offering academic researchers access to PathExplore's human interpretable features (HIFs) that have been deployed on publicly available samples from The Cancer Genome Atlas (TcGA). This program allows researchers to access PathExplore HIFs from H&E whole-slide images via our platform and link them with genomic and outcomes data across eight cancer indications: NSCLC, Breast, CRC, Melanoma, Gastric, RCC, PDAC, and Prostate.
This data is free to academic researchers, and we also offer a license for non-academic partners. To learn more and apply to the PathExplore data access program, please contact [email protected]. If you are an industry-based researcher interested in licensing these data, please reach out to [email protected]
This data is free to academic researchers, and we also offer a license for non-academic partners. To learn more and apply to the PathExplore data access program, please contact [email protected]. If you are an industry-based researcher interested in licensing these data, please reach out to [email protected]
Resources
PathExplore is for research use only. Not for use in diagnostic procedures.