Upcoming Events and Conferences

Nucleai will be showcasing our AI-powered spatial biology platform in the following conferences:

 

Fill out the form to book a meeting with our team:

The Leading Spatial Biology Platform for Cancer

Nucleai’s ATOM spatial biology platform leverages computer vision and deep neural networks to structure and characterize tissue and cell architecture in cancer pathology images – helping identify spatial characteristics that predict response to therapy and inform treatment decisions so you can better understand cancer biology, optimize patient stratification and improve patient care.

AI-Powered Image Structuring

Structure and characterize tissue and cell architecture from H&E, IHC, and Multiplex images.

Spatial Feature Extraction

Calculate spatial features to better explain mechanism of action and biological phenomena.

Predictive Models & Novel Insights

Leverage spatial analysis to predict outcomes, genomic status, and protein signatures.

Powerful Multiplex Capabilities

Nucleai ATOM offers powerful multiplex analysis. The platform is agnostic to staining and scanning platforms and is currently available in a comprehensive service mode, providing fast turnaround times for high plex, large datasets.

  • Image management and visualization: the viewer is designed to view high plex images with custom overlays, annotations, and algorithmic results
  • Cell analysis: cell typing, marker expressions, marker positivity, surrounding tissue and cell neighborhood analysis
  • Spatial analysis: spatial features combining cell typing, phenotypic marker expression, neighborhood and area assignment
  • Manual QC reports generated by pathologist’s assessment

Latest publications

The latest publications were presented at the Society for Immunotherapy of Cancer (SITC) in Boston in November 2022. 

Predicting response to immune checkpoint inhibitors (ICI) in non-small-cell lung cancer (NSCLC) by combining spatial analysis of cells and RNA sequencing data from biopsies using deep learning (DL)

A deep learning analysis pipeline for multiplex imaging identifies spatial features associated with clinical outcome in colorectal cancer

Predicting CD8+ cell density and tumor-immune phenotypes in non-small-cell lung cancer (NSCLC) from standard H&E slides using deep learning (DL)

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