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PathExplore

PathExploreTM offers unprecedented spatial and cellular resolution of the tumor microenvironment (TME) from H&E images.
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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).



 
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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.

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.
Full Abstract Here

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.
Full Abstract Here
nuclear morphology chart

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]
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Resources

Contact Us

Connect with our business development team to learn more about PathExplore.
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PathExplore is for research use only. Not for use in diagnostic procedures.